The detection of vulnerabilities in smart contracts remains a significant challenge. While numerous tools are available for analyzing smart contracts in source code, only about 1.79% of smart contracts on Ethereum are open-source. For existing tools that target bytecodes, most of them only consider the semantic logic context and disregard function interface information in the bytecodes. In this paper, we proposeCOBRA, a novel framework that integrates semantic context and function interfaces to detect vulnerabilities in bytecodes of the smart contract. To our best knowledge,COBRAis the first framework that combines these two features. Moreover, to infer the function signatures that are not present in signature databases, we proposeSRIF, automatically learn the rules of function signatures from the smart contract bytecodes. The bytecodes associated with the function signatures are collected by constructing a control flow graph (CFG) for theSRIFtraining. We optimize the semantic context using the operation code in the static single assignment (SSA) format. Finally, we integrate the context and function interface representations in the latent space as the contract feature embedding. The contract features in the hidden space are decoded for vulnerability classifications with a decoder and attention module. Experimental results demonstrate thatSRIFcan achieve 94.76% F1-score for function signature inference. Furthermore, when the ground truth ABI exists,COBRAachieves 93.45% F1-score for vulnerability classification. In the absence of ABI, the inferred function feature fills the encoder, and the system accomplishes an 89.46% recall rate.

The web3 applications have recently been growing, especially on the Ethereum platform, starting to become the target of scammers. The web3 scams, imitating the services provided by legitimate platforms, mimic regular activity to deceive users. However, previous studies have primarily concentrated on de-anonymization and phishing nodes, neglecting the distinctive features of web3 scams. Moreover, the current phishing account detection tools utilize graph learning or sampling algorithms to obtain graph features. However, large-scale transaction networks with temporal attributes conform to a power-law distribution, posing challenges in detecting web3 scams. To overcome these challenges, we present ScamSweeper, anovelframework that emphasizes the dynamic evolution of transaction graphs, to identify web3 scams on Ethereum. ScamSweeper samples the network with a structure temporal random walk, which is an optimized sample walking method that considers both temporal attributes and structural information. Then, the directed graph encoder generates the features of each subgraph during different temporal intervals, sorting as a sequence. Moreover, a variational Transformer is utilized to extract the dynamic evolution in the subgraph sequence. Furthermore, we collect a large-scale transaction dataset consisting of web3 scams, phishing, and normal accounts, which are from the first 18 million block heights on Ethereum. Subsequently, we comprehensively analyze the distinctions in various attributes, including nodes, edges, and degree distribution. Our experiments indicate that ScamSweeper outperforms SIEGE, Ethident, and PDTGA in detecting web3 scams, achieving a weighted F1-score improvement of at least 17.29% with the base value of 0.59. In addition, ScamSweeper in phishing node detection achieves at least a 17.5% improvement over DGTSG and BERT4ETH in F1-score from 0.80.

Automating vulnerability repair (AVR) in Python remains constrained by low accuracy, long feedback loops, and rapidly escalating token spend when large repositories must be reasoned about. These challenges are amplified by the prevalence of multi-file, environment-dependent CVEs whose fixes span configuration, tests, and cross-module semantics. We introduce MAAP, a self-evolving, role-specialized LLM architecture for end-to-end automated patch synthesis in Python codebases. MAAP decomposes a vulnerable repository into fine-grained, dependency-aware subtasks and dispatches them via a contextual-bandit router that jointly optimizes correctness, latency, and cost. An experience-centric knowledge base surfaces successful semantic exemplars and failure signals to prune search. An agent factory dynamically spawns, retires, or coordinates agents when novel patterns or degraded rewards emerge. We evaluate MAAP on 120 real-world Python CVEs. MAAP achieves a 70.3% patch success rate, surpassing the SWE-agent baseline, which has a 56.7% success rate. This improvement incurs a 62.8% higher average token cost, but it results in a $\mathbf{1 5. 5 \%}$ lower cost per successful patch. Ablation studies show substantial drops in success when disabling the router, knowledge base, or cooperative spawning, underscoring the necessity of each component. Collectively, these results indicate that MAAP’s design can deliver robust, largely hands-free vulnerability repair for Python at practical cost envelopes and is readily extensible to other language ecosystems.

Once deployed for malicious traffic detection, a statically trained supervised model swiftly sees its false-positive and false-negative rates soar as evolving attack tactics and resources trigger concept drift. Previous studies have predominantly employed methods such as incremental learning, online learning, periodic retraining, and event-triggered updates to address concept drift. However, they still struggle to eliminate the burden of frequent updates fully. To address this issue, we propose a method named TGF-JA4, which integrates temporal and graph-based multi-feature representations for robust malicious traffic detection under concept drift, eliminating the need for model retraining. Specifically, we first leverage a Transformer to extract three-layer structural features (data packets, bursts, and flows) as the initial representation of graph nodes. We then construct a robust heterogeneous graph by combining inter-flow temporal edges with the newly introduced JA4 edges. Subsequently, it extracts deep graph-level features using GNN. Finally, it accomplishes malicious traffic detection under concept drift by fine-tuning LLMs. In concept drift experiments on both experimental datasets and real-world network environment datasets, this method achieved accuracies exceeding 90% and 99%, respectively, outperforming SOTA models and effectively reducing the requirement for model retraining.

As a critical component of industrial infrastructure, the security of power information systems is directly related to the stable operation of power dispatch and supply. However, due to insufficient security considerations during the design phase and the diversity of customized implementations, power information systems widely suffer from defect at the software, firmware, and communication protocol levels, facing a variety of complex attack threats. This paper systematically reviews the development and research progress of existing defect detection techniques, based on the typical layered architecture of power information systems. First, focusing on customized industrial control software, it summarizes various detection methods, including classical program analysis, machine learning, and large language models (LLMs). Second, for embedded firmware security, it provides an in-depth comparison of static and dynamic analysis techniques, with a focus on key technologies such as taint analysis, firmware emulation, and fuzzing. Finally, for industrial communication protocols, it comprehensively analyzes the application scenarios and limitations of detection methods such as formal verification, symbolic execution, and protocol fuzzing. Building upon this, the paper further explores future research directions, including the deep application of LLMs, AI-driven firmware and protocol defect detection, efficient detection for complex industrial control protocols, and enhanced firmware emulation.

The proliferation of black-box Large Language Models (LLMs) makes source attribution essential for accountability and security. Yet, progress is limited by the lack of a large multilingual benchmark and by fragile or computationally intensive methods. We introduce FD-Dataset, a bilingual benchmark of 90,000 samples from 20 major LLMs, and FDLLM, a LoRA-adapted detector that extracts persistent decoding fingerprints from a foundation model. LoRA induces intra-model clustering and inter-model separation in representation space, explaining its effectiveness for fingerprinting. On FD-Dataset, FDLLM surpasses the strongest baseline by 22.1% Macro F1, generalizes to newly released models with 95% accuracy, and remains robust to polishing, translation, and synonym substitution, reducing average attack success rate from 49.2% (LM-D) to 23.9%.

In today’s software development environment, third-party libraries (TPLs) enhance productivity but also introduce security risks. Effective Software Composition Analysis (SCA) is crucial for managing these risks. Yet, existing SCA tools for C/C++ projects struggle with challenges like detecting modified and nested TPLs, precise version representation, and comprehensive TPL databases. In modern software development, third-party libraries (TPLs) are commonly used to boost functionality and save development time. However, this convenience introduces security risks. We introduce OSSDetector, a new SCA tool that addresses these issues. OSSDetector uses sliding window and fuzzy hashing techniques to generate detailed signatures, improving detection of modified TPLs. It features a "Nested TPL Function Filtering" algorithm to accurately identify and filter nested TPL functions, and a "TPL Recognition" algorithm based on import ratios and function paths to determine the TPLs used in the software. It also addresses version representation by using function weights and release times. To overcome the lack of a comprehensive TPL database, we have developed a large database with 29,416 C/C++ TPLs and 767,405 versions. Experimental results demonstrate that OSSDetector surpasses state-of-the-art tools, achieving better precision (85.52%), recall (79.82%), and F1 score (82.57%), and higher precision (84.27%) at the library version level.

In the digital age, cross-border data flows are a key driver of global economic growth. However, this growth is accompanied by mounting security and compliance risks. Although countries are prioritizing data security, the absence of unified, universally binding regulations creates a fragmented and complex governance landscape. This paper systematically reviews trade agreements, domestic laws, and international regulations to define the concept and scope of cross-border data flows. It also introduces a data lifecycle theory adapted for cross-border scenarios. Through a comparative analysis of relevant policies in the European Union, the United States, and China at various stages of the data lifecycle, the paper identifies commonalities and differences. Based on these findings, the paper provides policy recommendations to harmonize global data governance frameworks, strengthen international cooperation, and balance national security concerns with the free flow of data.

Hospital mini-programs have become widely adopted as lightweight portals for medical services, handling large volumes of personally identifiable information (PII) and protected health information (PHI). Among the most critical threats to such systems is the insecure direct object reference (IDOR) vulnerability, which allows unauthorized access to sensitive resources due to improper object–level access control. However, systematic detection of IDOR in the wild, especially within hospital mini-programs, remains underexplored due to restricted server access and stringent ethical regulations. To address this challenge, we propose a black–box detection framework designed for hospital mini-programs operating in sensitive data environments. Our framework introduces a novel token–substitution probing strategy that adheres to ethical standards and pioneers the use of Large Language Models (LLMs) for automated IDOR vulnerability detection in API endpoints, enabling token field identification, request classification and differential response analysis. We evaluated the framework on 80 real-world mini-programs and identified 114 vulnerable endpoints across 38 applications. Among these, 55 involved sensitive data disclosure, and 34 enabled unauthorized execution of sensitive operations. All findings were responsibly disclosed to the CNVD, and 15 cases have been officially confirmed.

Mobile applications (Apps) have become indispensable to our daily routines, infiltrating every facet of modern life. However, the widespread use of these apps also poses serious risks of privacy leakage for individuals. In this paper, we uncover a critical yet overlooked security issue: the ubiquitous exposure of private data within the graphical user interfaces (GUIs) of apps. Specifically, we revealed that many apps did not adopt the anonymity principle to protect users’ privacy data, and some apps even displayed more private data than users provided, underscoring a significant oversight in data privacy practices within the app ecosystem. We designed and implemented a novel semi-automated tool, PryDroid, for detecting private data exposed in Android app GUIs. This tool employs a depth-first search strategy to explore UI interfaces and minimizes redundant page exploration through sensory processing and template matching techniques. By measuring 234 real-world apps in Chinese app markets with the help of PryDroid, we found 95.7% of apps display private data in one or more UI pages. Our evaluation confirms widespread exposure of user data across the app ecosystem, underlining the urgent need for enhanced privacy protections and user awareness regarding digital privacy.

Against the backdrop of rapid development in digital transformation and cloud-native architectures, the massive, multi-source, and heterogeneous logs generated by enterpriselevel IT systems have imposed higher requirements on real-time analysis and intelligent operation and maintenance. While traditional ELK stacks facilitate centralized log storage and retrieval, they exhibit significant bottlenecks in complex causal reasoning and dynamic format adaptation. To address these limitations, this paper proposes an intelligent log knowledge base construction method based on a "data classification-hierarchization-graphization" pipeline. Specifically, we design an ELK-Neo4j streaming fusion architecture, a dynamic ontology-driven semantic enhancement mechanism, and a sensitivity-aware hierarchical graph governance model. The system achieves millisecond-level bidirectional synchronization between Elasticsearch and Neo4j through Kafka Connect. Incorporated with template drift detection and relationship weight learning, it enables online ontology evolution and adaptive expansion. For compliance, sensitive fields are identified via regular expressions and NLP, while RBAC+ABAC mechanisms are introduced to achieve triple-level access control. Experiments on HDFS, BGL, and custom security log datasets show that our method significantly outperforms static ontology and batch processing schemes in template extraction F1-score, field recall, graph query latency, synchronization delay, and root cause localization efficiency. The proposed framework can be widely applied in finance, healthcare, and cloud-native scenarios, effectively improving fault diagnosis efficiency and data governance accuracy.

Firmware identification is a core component of firmware analysis technology, whose primary purpose is to provide sufficient information for subsequent analysis processes. Among such information, Instruction Set Architecture (ISA) information is particularly critical – without it, reverse engineering and firmware simulation cannot be carried out. However, currently there is no effective tool for identifying the ISA of monolithic firmware. This paper presents an new identification method based on a filter mechanism to address the issue of ISA identification for monolithic firmware. By studying multiple instances of instruction set architectures, our key observation is that certain instructions appear in pairs, and we summarize the concept of Instruction Pairs and develop a mechanism to extract and filter binary blocks, which integrates instruction pairs and the logical correctness analysis of the P-Code Intermediate Representation (IR). Furthermore, we build a prototype of FBFISADetector, a monolithic firmware ISA identification system based on the filter mechanism. This system uses the aforementioned methods to extract and filter binary blocks and determines the ISA of tested firmware by the number of remaining binary blocks. Finally, we evaluate our work, and the results show that: In the test of 870 ARM-Cortex M architecture samples from the monolithic firmware dataset, the identification accuracy reaches 98%; in the test of more than 15,000 ELF files covering 5 ISAs (ARM, RISC-V64, MIPS, POWERPC, TRICORE), the overall identification accuracy is as high as 99%. Meanwhile, we verify the code region hit effect of the instruction pair filtering mechanism and the IR analysis filtering mechanism based on the above ELF file dataset. The experimental results demonstrate that the final binary blocks have high credibility regarding their attribution to the current ISA.

Data is increasingly recognized as one of the most critical assets for enterprises. Data lineage graphs, serving as a key foundation of data governance, articulate the evolution of data and the lineage relationships among diverse datasets. By leveraging data lineage graphs, developers can efficiently trace the root causes of anomalies in data metrics. However, as the volume of data within organizations continues to expand, troubleshooting through such graphs has grown increasingly complex. The rise of large language models (LLMs) has been driving significant advances in software engineering, data management, and related disciplines, motivating us to explore their application in root cause analysis for data lineage anomalies. At the same time, due to commercial confidentiality and privacy compliance requirements, the field of data governance suffers from a pronounced scarcity of open datasets—particularly those featuring comprehensive data lineage relationships with annotated anomalies. To address the lack of labeled anomaly data grounded in data lineage, we designed a synthetic data generation process based on the TPC‑H benchmark, creating a specialized dataset that includes four types of anomalies: null field values, unexpected field values, empty tables, and anomalous data distributions— comprising 100 cases in total. For the task of root cause diagnosis in data lineage anomalies, we developed a multi-agent framework powered by a large language model to evaluate its performance on this task. Our study explores the potential of LLMs in diagnosing data lineage anomalies, and results demonstrate that our approach successfully resolves 90% of the constructed cases.

This paper considers a strongly connected network of agents, each capable of partially observing and controlling a discrete-time linear time-invariant (LTI) system that is jointly observable and controllable. Additionally, agents collaborate to achieve a shared estimated state, computed as the average of their local state estimates. Recent studies suggest that increasing the number of average consensus steps between state estimation updates allows agents to choose from a wider range of state feedback controllers, thereby potentially enhancing control performance. However, such approaches require that agents know the input matrices of all other nodes, and the selection of control gains is, in general, centralized. Motivated by the limitations of such approaches, we propose a new technique where: (i) estimation and control gain design is fully distributed and finite-time, and (ii) agent coordination involves a finite-time exact average consensus subroutine, allowing arbitrary selection of the convergence rate of the overall asymptotic estimation process despite the estimator's distributed nature. We verify our methodology's effectiveness using illustrative numerical simulations.

The “sharing” feature provided by smart home platforms enables multiple users to access the device simultaneously with different roles and permissions, but it also presents new security challenges for the design and implementation of the permission management. The key issue is that the platform adopts two different permission assignments on the app side and the cloud side, and these two assignments must maintain consistency in authorizing. Unfortunately, real-world smart home platforms may not be able to ensure this when implementing RBAC (Role-Based Access Control) model. The inconsistency between these assignments may lead to security vulnerabilities, which can be easily exploited by malicious users. Although many existing studies have revealed security issues with smart home platforms, less attention has been paid to the sharing feature and permission assignments, as well as security issues that arise from this. In this work, we conducted a systematic study on the RBAC model and permission management of smart home platforms. To overcome technical challenges imposed by the “black-box” platform, we also proposed a novel testing framework. By testing 10 smart home platforms that all belong to the “device-connected, black-box, and multi-user supported” category, we collected each platform's “configurable permission assignment” and inferred “enforced permission assignment”. At last, we identified 44 inconsistencies that could lead to security vulnerabilities. Malicious users could exploit these vulnerabilities to initiate attacks such as device hijacking, unauthorized access, illegal control, and eavesdropping. We promptly reported these vulnerabilities to vendors and CNVD, and proposed mitigation measures.

In contrast to prevalent Federated Learning (FL) privacy inference techniques such as generative adversarial networks attacks, membership inference attacks, property inference attacks, and model inversion attacks, we devise an innovative privacy threat: the Data Distribution Decompose Attack on FL, termedDecaf. This attack enables an honest-but-curious FL server to meticulously profile the proportion of each class owned by the victim FL user, divulging sensitive information like local market item distribution and business competitiveness. The crux ofDecaflies in the profound observation that the magnitude of local model gradient changes closely mirrors the underlying data distribution, including the proportion of each class.Decafaddresses two crucial challenges: accurately identify the missing/null class(es) given by any victim user as a premise and then quantify the precise relationship between gradient changes and each remaining non-null class. Notably,Decafoperates stealthily, rendering it entirely passive and undetectable to victim users regarding the infringement of their data distribution privacy. Experimental validation on five benchmark datasets (MNIST, FASHION-MNIST, CIFAR-10, FER-2013, and SkinCancer) employing diverse model architectures, including customized convolutional networks, standardized VGG16, and ResNet18, demonstratesDecaf’s efficacy. Results indicate its ability to accurately decompose local user data distribution, regardless of whether it is IID or non-IID distributed. Specifically, the dissimilarity measured using$L_{\infty }$distance between the distribution decomposed byDecafand ground truth is consistently below 5% when no null classes exist. Moreover,Decafachieves 100% accuracy in determining any victim user’s null classes, validated through formal proof.

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes that may occur during malicious events. However, this victim-only approaches seldom possess the capability to cover the attacker’s interaction intention logic. Furthermore, only a minuscule percentage of DeFi protocols experience attacks in real-world scenarios, which poses a significant challenge for these detection tools to demonstrate practical effectiveness. In this paper, we propose DeFiTail, thefirstframework that utilizes deep learning technology for access control and flash loan exploit detection. Through feeding the cross-contract static data flow, DeFiTail automatically learns the attack logic in real-world malicious events that occur on DeFi protocols, capturing the threat patterns between attacker and victim contracts. Since the DeFi protocol events involve interactions with multi-account transactions, the execution path with external and internal transactions requires to be unified. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, DeFiTail validates the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Comparative experiment results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset. By monitoring existing contracts, we identified five distinct categories of vulnerabilities: repetition abuse, unsafe unintended exploitation, signature violated exploitation, insecure interfaces exploitation, and unrestricted token transfer.

Membership inference (MI) attacks pose a significant threat to user privacy in machine learning systems. While numerous attack mechanisms have been proposed in the literature, the lack of standardized evaluation parameters and metrics has led to inconsistent and even conflicting comparison results. To address this issue and facilitate a systematic analysis of these disparate findings, we introduce MIBench, a comprehensive benchmark that includes a suite of carefully designed evaluation scenarios (ESs) and evaluation metrics to provide a consistent framework for assessing the efficacy of various MI techniques. The ESs are crafted to encompass four critical factors: intra-dataset distance distribution, inter-sample distance within the target dataset, differential distance analysis, and inference withholding ratio. In total, MIBench includes ten typical evaluation metrics and incorporates 84 distinct ESs for each dataset. Using MIBench, we conducted a thorough comparative analysis of 15 state-of-the-art MI attacks across 588 ESs, seven widely adopted datasets, and seven representative model architectures. Our analysis revealed 83 instances of Conflicting Comparison Results (CCR), providing substantial evidence for the CCR Phenomenon. We identified two CCR types: Type 1 (single-factor) and Type 2 (dual-factor). The distribution of CCR instances across the four critical factors was: inter-sample distance (40.96%), differential distance (37.35%), inference withholding ratio (19.28%), and intra-dataset distance (2.41%). All MIBench codes and evaluations are available athttps://github.com/MIBench/MIBench.github.io/blob/main/README.md.

The right to be forgotten has incentivized machine unlearning, but a key challenge persists: the lack of reliable methods to verify unlearning conducted by model providers. This gap facilitates dishonest model providers to deceive data contributors. Current approaches often rely on invasive methods like backdoor injection. However, it poses security concerns and is also inapplicable to legacy data—already released data. To tackle this challenge, this work initializes the first non-invasive unlearning verification framework which operates at triple-granularity (class-, volume-, sample-level) to assess the data facticity and volume integrity of machine unlearning. In this paper, we propose a framework, named TruVRF, encompasses three Unlearning-Metrics, each tailored to counter different types of dishonest model providers or servers (Neglecting Server, Lazy Server, Deceiving Server). TruVRF leverages non-invasive model sensitivity to enable multi-granularity verification of unlearning. Specifically, Unlearning-Metric-I checks if the removed class matches the data contributor’s unlearning request, Unlearning-Metric-II measures the amount of unlearned data, and Unlearning-Metric-III validates the correspondence of a specific unlearned sample with the requested deletion. We conducted extensive evaluations of TruVRF efficacy across three datasets, and notably, we also evaluated the effectiveness and computational overhead of TruVRF in real-world applications for the face recognition dataset. Our experimental results demonstrate that TruVRF achieves robust verification performance: Unlearning-Metric-I and -III achieve over 90% verification accuracy on average against dishonest servers, while Unlearning-Metric-II maintains an inference deviation within 4.8% to 8.2%. Additionally, TruVRF demonstrates generalizability across diverse conditions, including varying numbers of unlearned classes and sample volumes. Significantly, TruVRF is applied to two state-of-theart unlearning frameworks: SISA [3] (presented at Oakland’21) and Amnesiac Unlearning [18], representing exact and approximate unlearning methods, respectively, which affirm TruVRF’s practicality. In addition, we conducted extensive evaluations around TruVRF, including ablation experiments, trade-offs in computational overhead, and the robustness of model sensitivity, among others.

Currently, the Short Message Service (SMS) One-Time Passwords (OTP) authentication is widely adopted in mobile applications. However, due to improper implementation by developers, significant security flaws exist in the SMS OTP authentication mechanisms of some apps. To provide a comprehensive and accurate assessment, we propose a new approach. First, we locate the SMS OTP authentication page through UI exploration. Then, using hooking technology, we conduct simulated attacks to verify the security of the SMS OTP authentication in the app, focusing on its susceptibility to brute-force attacks. This approach is applicable to apps with app-side or UI-layer protection measures, uncovering hidden implementation flaws beneath these protections. Technically, we employ dynamic analysis based on the ART virtual machine instrumentation to obtain runtime information of the app and generate vulnerability verification scripts, overcoming the challenges posed by code-packing in program analysis. We implemented a semi-automatic tool namedAuthCheckerand tested it on 950 popular apps, identifying 87 apps with security flaws that potentially allow attackers to achieve unauthorized account access. Our findings highlight the security issues in SMS OTP authentication of apps, promoting improvements in vulnerability patching and preventive strategies by developers.

The decentralized exchange (DEX) leverages smart contracts to trade digital assets for users on the blockchain. Developers usually develop several smart contracts into one project, implementing complex logic functions and multiple transaction operations. However, the interaction among these contracts poses challenges for developers analyzing the state logic. Due to the complex state logic in DEX projects, many critical state derailment defects have emerged in recent years. In this article, we conduct the first systematic study of state derailment defects in DEX. We define five categories of state derailment defects and provide detailed analyses of them. Furthermore, we propose a novel deep learning-based framework StateGuardfor detecting state derailment defects in DEX smart contracts. It leverages a smart contract deconstructor to deconstruct the contract into an abstract syntax tree (AST), from which five categories of dependency features are extracted. Next, it implements a graph optimizer to process the structured data. At last, the optimized data is analyzed by graph convolutional networks to identify potential state derailment defects. We evaluated StateGuardthrough a dataset of 46 DEX projects containing 5671 smart contracts, and it achieved 94.25% F1-score. In addition, in a comparison experiment with state-of-the-art, StateGuardleads the F1-score by 6.29%. To further verify its practicality, we used StateGuardto audit real-world contracts and successfully authenticated multiple novel common vulnerabilities and exposures.

Automated Program Repair (APR) has garnered significant attention in recent years, especially when combined with the latest advancements in deep learning, further enhancing its automation and repair efficiency. Traditional learning-based APR typically employs Neural Machine Translation (NMT) technology, treating the repair task as a translation process from defective code to repaired code, known as the NMT paradigm. With the emergence of large pre-trained models, a novel approach considers the repair process as a fill-in-the-blanks task, masking defective code and using Masked Language Models (MLM) to predict the masked positions, resulting in repaired code, referred to as the MLM paradigm. However, the applicability of this new learning paradigm in APR has not been widely explored, and the differences in repair effectiveness compared to the NMT paradigm remain unclear. This paper delves into the performance differences between the MLM and NMT paradigms in APR through empirical research. Although both paradigms have drawn attention in the APR domain, their methodologies differ, necessitating a comprehensive evaluation and comparison in the same experimental environment, particularly in Natural Language Models (NLM) and Code Language Models (CLM).The results reveal that in NLM, the NMT paradigm excels with higher repair accuracy, leveraging large parallel corpora for supervised training and focusing on machine translation tasks, facilitating better learning of correspondences and semantic representations between languages. However, when dealing with more complex programming languages like CLM, the repair effectiveness of the NMT paradigm lags behind that of the MLM paradigm. NMT struggles to learn the syntax and semantic relationships of real-world programming languages, while the MLM paradigm comprehends code syntax and structure more effectively. It accurately identifies variable scopes, function call relationships, and dependencies between code blocks, allowing the model to infer errors and generate repaired code that aligns with programming language specifications. In summary, each paradigm demonstrates advantages in different repair scenarios. It is recommended to choose the appropriate technical paradigm based on specific repair needs and contexts in practical applications.

Vulnerability classification is a crucial task in software security analysis, essential for identifying and mitigating potential security risks.Learning-based methods often perform poorly due to the long-tail distribution of vulnerability classification datasets.Recent approaches try to address the problem but treat each CWE class in isolation, ignoring their relationships.This results in non-scalable code vector representations, causing significant performance drops when handling complex realworld vulnerabilities.We propose a hierarchical contrastive learning framework for multiclass code vulnerability type classification to bring vector representations of related CWEs closer together.To address the issue of class collapse and enhance model robustness, we mix self-supervised contrastive learning loss into our loss function.Additionally, we employ max-pooling to enable the model to handle longer vulnerability code inputs.Extensive experiments demonstrate that our proposed framework outperforms state-of-the-art methods by 2.97%-17.90% on accuracy and 0.98%-22.27% on weighted-F1, with even better performance on higher-quality datasets.We also utilize an ablation study to prove each component's contribution.These findings underscore the potential and advantages of our approach in the multi-class vulnerability classification task.

This study aims to address the problem of medical text data privacy protection in the context of extensive development of cloud computing. This paper comprehensively researches the relevant literature and technical progress in recent years on privacy protection in medical scenarios using homomorphic encryption and other advanced cryptography techniques, and synthesizes the scalability of cloud computing, the timeliness of edge computing, and the inerrancy of blockchain technology to achieve the privacy protection of medical text data through the cloud storage of medical data and the dense state computation, and protects the privacy of patient's medical data in order to achieve the purpose of protecting individual privacy, property security, and security. It protects the privacy of patients' medical data to realize the purpose of protecting personal privacy and property security. By changing the encryption parameters of the existing DGHV multi-key homomorphic encryption scheme to realize the characteristics of the adapted scheme, and at the same time, the public key parity restriction can be eliminated. In addition, this paper also discusses the application of blockchain technology in homomorphic encryption key management, and proposes a blockchain-based key security storage system to ensure the key's tamperability and security. The privacy security problem in medical data transmission, storage and processing is solved by constructing this medical text data privacy protection system based on cloud-edge cooperative computing and homomorphic encryption. Simulation experiments verify that this scheme is practically feasible, while the correct parameters and the choice of the edge computing approach take into account both security and feasibility.

Mobile app vendors/developers extensively integrate third-party libraries into mobile applications. While they enrich the functions of apps, third-party libraries also bring in security risks. It has been widely studied that malicious third-party libraries could collect users’ sensitive data from the host apps and the app backend servers. Recent research has reported a new attack vector — malicious libraries strategically target other vendors’ library(SDKs) integrated in the same host app to harvest private user data.In this paper, we found two new dimensions of cross library data harvesting(XLDH) attack with serious privacy impacts that start from two new attack surfaces — accessing sensitive fields and accessing sensitive storage. However, the mitigation scheme, significantly, has not been yet studied. To prevent the leaks of sensitive data due to XLDH activities, we first proposed a mitigation scheme - LibGuard, which has been proven to be effective without affecting user’s experience on real-world apps.

In the scenario of multi-user access in smart homes, the platform’s access control mechanism is crucial in ensuring that users can legitimately and appropriately access devices. However, we have found that the permission management mechanisms provided by smart home platforms and the solutions proposed in academic research are insufficient to fully meet the new requirements of access control. This can result in issues such as unauthorized access, inability to coordinate access conflicts, and lack of support for black-box platforms. In this paper, we present SHPAC, a new smart home access control scheme based on Policy-Based Access Control. This scheme not only provides fine-grained permission management, but also supports users in submitting their personalized access preferences, as well as automatic conflict resolution and multi-platform support. We also implemented a prototype system and evaluated the system from the perspectives of black-box platform support, access control performance, and policy management effectiveness. The results show that our design can address the shortcomings of existing solutions, enhancing the flexibility and effectiveness of smart home access control.

Although deepfake technology is neutral, it can be maliciously used by criminals to cause serious security issues. These deepfake videos generated by deep learning technology are no different from real videos, posing a major threat to personal privacy and information credibility. Existing deepfake detection models face a core challenge: most models have limited generalization capabilities, and often have unsatisfactory detection results in the face of increasingly complex forgery technologies. To solve this problem, we introduce a two-stream deepfake detection model. One stream leverages the Video Swin Transformer to identify inter-frame discontinuities, a common anomaly in deepfakes. While another stream utilizes deep convolutional neural networks to detect facial texture inconsistencies, another telltale sign of a fake face. Furthermore, we use an improved meta-learning method called meta-learning for deepfake detection (MLDD) to train our model, which enhances the model’s adaptability and ability to quickly learn from multiple deepfake styles. Experimental results demonstrate that our model has superior performance and strong generalization compared to state-of-the-art existing techniques.

The detection of vulnerabilities in smart contracts remains a significant challenge. While numerous tools are available for analyzing smart contracts in source code, only about 1.79% of smart contracts on Ethereum are open-source. For existing tools that target bytecodes, most of them only consider the semantic logic context and disregard function interface information in the bytecodes. In this paper, we propose COBRA, a novel framework that integrates semantic context and function interfaces to detect vulnerabilities in bytecodes of the smart contract. To our best knowledge, COBRA is the first framework that combines these two features. Moreover, to infer the function signatures that are not present in signature databases, we present SRIF (Signatures Reverse Inference from Functions), automatically learn the rules of function signatures from the smart contract bytecodes. The bytecodes associated with the function signatures are collected by constructing a control flow graph (CFG) for the SRIF training. We optimize the semantic context using the operation code in the static single assignment (SSA) format. Finally, we integrate the context and function interface representations in the latent space as the contract feature embedding. The contract features in the hidden space are decoded for vulnerability classifications with a decoder and attention module. Experimental results demonstrate that SRIF can achieve 94.76% F1-score for function signature inference. Furthermore, when the ground truth ABI exists, COBRA achieves 93.45% F1-score for vulnerability classification. In the absence of ABI, the inferred function feature fills the encoder, and the system accomplishes an 89.46% recall rate.

In the context of globalization and rapid development of information technology, cross-border flow of data has become an important part of international trade. This paper firstly analyzes the necessity and risk of data cross-border and discusses the relevant legal framework in China. Secondly, this paper, for the first time, uses Petri nets to model the business process of cross-border data flow, effectively and comprehensively describing the steps and interrelationships in the three phases of data transmission: before transmission, during transmission, and overseas maintenance. Finally, the proposed model is simulated and experimented by PIPE software, which verifies the boundedness, security and deadlock-free characteristics of the model, and proves the feasibility and necessity of Petri nets in the application of cross-border business processes of data.

In the context of the information age, promoting digital government and smart cities has made government data sharing a key trend. Given its special nature, securing government data requires an effective security system for safe and efficient management. This paper explores government data security and technical systems, examines China's current data management situation, and compares management strategies in China, the EU, and the US. This paper adopts a data lifecycle-driven security management approach and leverages two widely recognized frameworks to propose a system that balances data openness and security. Finally, we propose an integrated learning method based on BERT and Random Forest, use real data sets to verify the feasibility of data grouping, and promote the integration of government data management and efficient technology.

Energy storage is an important part of the new power system, responsible for ensuring stable power output and balancing loads. At the same time, it is also a national critical infrastructure. As the country gradually strengthens its control over data security risks of critical infrastructure, data security issues in the energy storage industry have become a focus of attention. In this context, this paper collects and organizes and analyzes relevant literature on data security in the energy storage industry. First, the energy storage system and energy storage technology are summarized and analyzed, and the data and data characteristics of the energy storage system are explained. Secondly, the current data security risks of the energy storage system are sorted out, including BMS network security risks, user privacy leakage risks, information interaction risks of distributed energy storage systems, smart meter data leakage risks, and trust risks between devices. Based on the above data security risks, the existing energy storage data security governance methods are sorted and classified. Finally, it is summarized that the current research in the field of data security in the energy storage industry is not sufficient. This paper comprehensively analyzes the literature on data security in the energy storage industry in recent years, which will provide support for the research on data security governance in the energy storage industry.

Data distillation is a technique to reduce the training data for machine learning models, by which a large training dataset can be reduced to a smaller, more streamlined dataset while retaining the key information of the original dataset and the model's learning capability. Previous research on the security of data distillation has only been done on image data, and the security of text data distillation has not yet been studied. In this paper, we conduct a systematic research on backdoor attack methods for text data in the process of data distillation. Specifically, this paper embeds a backdoor and implements a backdoor attack before and during data distillation of text data. The experimental results show that, relative to embedding the backdoor before the data distillation operation, embedding the backdoor during the distillation process can make the backdoor information more effectively retained in the distilled data, and can achieve a 100% success rate of the attack under the premise of guaranteeing good concealment. These results show that although the data distillation technique can improve the efficiency of model training, its potential security risk should not be ignored.

The widespread adoption of third-party libraries (TPLs) in software development has significantly accelerated the creation of modern software. However, this convenience comes with potential legal risks. Developers may inadvertently violate the licenses of TPLs, leading to legal issues. While existing studies have explored software licenses and potential incompatibilities, these studies often focus on a limited set of licenses or rely on low-quality license data, which may affect their conclusions. To address this gap, there is an urgent need for a high-quality license dataset that encompasses a broad range of mainstream licenses and provides accurate terms and conflict information, to help developers navigate the complex landscape of software licenses, avoid potential legal pitfalls, and guide more informed and effective solutions for managing license compliance and compatibility in software development. To this end, we conduct the first work to understand the mainstream software licenses based on term granularity and obtain a high-quality dataset of 453 SPDX licenses with well-labeled terms and conflicts. Specifically, we first conduct a differential analysis of the mainstream platforms that provide license data to understand the terms and attitudes of each license. N ext, we further propose a standardized set of license terms to capture and label existing mainstream licenses with high quality. Moreover, we improve the existing license conflict mode to include copyleft conflicts and conclude the three major types of license conflicts among the 453 SPDX licenses. Based on the dataset, we carry out two empirical studies to reveal the concerns and threats from the perspectives of both licensors and licensees. One study provides an in-depth analysis of the similarities, differences, and conflicts among SPDX licenses, and the other revisits the usage and conflicts of licenses in the NPM ecosystem and draws conclusions that differ from previous work. Our studies reveal some insightful findings and disclose relevant analytical data, which set the stage for further research into the complexities of license compliance and compatibility.

Reversible adversarial example (RAE) is an effective cutting‐edge technology for protecting the intellectual property (IP) of datasets. However, existing RAE schemes primarily focus on the adversarial and restoration capabilities of adversarial examples (AE), with little attention paid to traceability, which is crucial for IP protection. This oversight leads to the inability to prevent authorized users from redistributing data, thereby posing significant IP security risks. To address this issue, we propose a novel approach named TAE‐RWP, wherein adversarial perturbations in AEs are treated as tools for IP verification. To enable the traceability of AEs, we introduce varying degrees of warping to the adversarial perturbations within the AEs of authorized users, utilizing the warping degree as a traceable feature. To further strengthen traceability, we adopt a technique named “random warping” to maintain the resilience of adversarial perturbations against distortions, and employ a strategy named “noise mode” to improve the verification model’s capacity to recognize distortion features. Experimental results indicate that AEs generated by TAE‐RWP exhibit remarkable adversarial strength and restoration abilities, while the verification model demonstrates excellence in recognizing distortion features.

Access tokens have been thoroughly researched in website and mobile application security. However, we believe that the traditional application of access tokens must fulfill new security requirements in smart home environments due to the distinct features of multiuser sharing usage. Smart home platforms allow different types of users to share access to a single IoT device through mobile apps, with varying levels of permissions that are closely tied to access tokens. One security concern is that existing security standards or literature, as well as the development and implementation by vendors, may overlook these features, thereby introducing potential security risks to the application of access tokens. In this work, we propose a novel testing framework and conduct a systematic study to test the extent to which real-world smart home platform implementations neglect these new requirements. The testing results show that seven out of the 11 real-world smart home platforms are plagued by access token management flaws, which collectively violate four security properties. We have found that these security flaws can be exploited to enable unrestricted file upload, DoS attack, remote command execution, and illegal surveillance in real-world scenarios. Finally, we conducted responsible disclosure of these flaws and attacks and obtained seven China national vulnerability database vulnerability IDs and one CVE vulnerability ID. Additionally, we also provide suggestions for mitigating the vulnerabilities.

Deeply embedded systems powered by microcontrollers are becoming popular with the emergence of Internet-of-Things (IoT) technology. However, these devices primarily run C/C \({+}{+}\) code and are susceptible to memory bugs, which can potentially lead to both control data attacks and non-control data attacks. Existing defense mechanisms (such as control-flow integrity (CFI), dataflow integrity (DFI) and write integrity testing (WIT), etc.) consume a massive amount of resources, making them less practical in real products. To make it lightweight, we design a bitmap-based allowlist mechanism to unify the storage of the runtime data for protecting both control data and non-control data. The memory requirements are constant and small, regardless of the number of deployed defense mechanisms. We store the allowlist in the TrustZone to ensure its integrity and confidentiality. Meanwhile, we perform an offline analysis to detect potential collisions and make corresponding adjustments when it happens. We have implemented our idea on an ARM Cortex-M-based development board. Our evaluation results show a substantial reduction in memory consumption when deploying the proposed CFI and DFI mechanisms, without compromising runtime performance. Specifically, our prototype enforces CFI and DFI at a cost of just 2.09% performance overhead and 32.56% memory overhead on average.

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor attacks (SSBAs). Compared to traditional SABAs, SSBAs are more advanced in that they have superior stealthier in bypassing mainstream countermeasures that are effective against SABAs. Nonetheless, existing SSBAs suffer from two major limitations. First, they can hardly achieve a good trade-off between ASR (attack success rate) and FPR (false positive rate). Besides, they can be effectively detected by the state-of-the-art (SOTA) countermeasures (e.g., SCAn [40]).

Keeping open-source software (OSS) up to date is one potential solution to prevent known vulnerabilities. However, it requires frequent and costly testing and may introduce compatibility issues. Consequently, developers often choose to backport security patches to the vulnerable versions instead. Manual backporting is time-consuming, especially for large OSS such as the Linux kernel. Therefore, automating this process is urgently needed to save considerable time. Existing automated approaches for backporting patches involve either automatic patch generation or automatic patch migration. However, these methods are often ineffective and error-prone since they failed to locate the precise patch locations or generate the correct patch, operating only on the syntactic level.

The advent of large language models (LLMs) has opened up new opportunities for automated program repair (APR). In particular, some recent studies have explored how to leverage large language models of code (LLMCs) for program repair tasks and show promising results. However, most of them adopt the zero/few-shot learning paradigm for APR, which directly use LLMCs to generate the possibly correct code given its surrounding context. Though effective, the repair capabilities of LLMCs based on the fine-tuning paradigm have yet to be extensively explored. Also, it remains unknown whether LLMCs have the potential to repair more complicated bugs (e.g., multi-hunk bugs). To fill the gap, in this work, we conduct a comprehensive study on the program repair capability of LLMCs in the fine-tuning paradigm. We select 5 popular LLMCs with representative pre-training architectures, including CodeBERT, GraphCode-BERT, PLBART, CodeT5, and UniX coder. We consider 3 typical program repair scenarios (i.e., bugs, vulnerabilities, and errors) involving 3 programming languages (i.e., Java,$\mathrm{C}/\mathrm{C}++$, and JavaScript). Notably, we take both single-hunk and multi-hunk bugs/vulnerabilities into account. We then fine-tune them on widely-used datasets and compare them with existing state-of-the-art APR tools. We also investigate the impact of different design choices, which include code abstractions, code representations, and model evaluation metrics. Our experimental results show that LLMCs in the fine-tuning paradigm can significantly outperform previous state-of-the-art APR tools. Through in-depth analysis, we provide insights into choosing appropriate strategies to guide LLMCs for better performance. Lastly, we reveal several limitations of LLMCs for APR and make suggestions for future research on LLMC-based APR.

Swarm attestation, as an important branch of Remote Attestation (RA), enables a trusted party (verifier) to verify the security states of multiple devices (provers) in a large network (swarm) simultaneously via a challenge-response mechanism. However, swarm attestation suffers from significant redundancy overhead since all devices in the swarm need to be attested in each attestation round. Besides, it faces challenges such as verifier-impersonation Denial of Service (DoS) attacks, highly dynamic networks, transient & self-relocating malware, and Time-Of-Check-Time-Of-Use (TOCTOU) attacks. In this paper, considering not only the detection accuracy but also the privacy of swarm owners in real Internet of Things (IoT) scenarios, we propose an Automatic Federated Swarm Attestation scheme (FeSA). Under this scheme, we design a federated-learning-based automatic swarm attestation protocol that enables theverifiersto identify the suspicious devices by a neural network model and then attest them. To the best of our knowledge, this is the first scheme to apply a federated learning method to RA, ruling out the redundancy attestation rounds while preserving data privacy. The FeSA redesigns the interaction model of RA by a challenge-query mechanism to reduce the overhead of an individual device to a constant. In order to evaluate our scheme, we first set up a smart office environment with 12 types of smart IoT devices for real-world data collection up to 21 days. Based on the real dataset, we demonstrate that FeSA can indeed identify the compromised IoT devices while reducing redundancy. We further simulate large-scale swarms of up to 1,000,000 devices to validate the efficiency of FeSA in large-scale swarms. Last, the security analysis proves the ability of FeSA to resist various attacks.

Emulating firmware of microcontrollers is challenging due to the lack of peripheral models. Existing work finds out how to respond to peripheral read operations by analyzing the target firmware. This is problematic because the firmware sometimes does not contain enough clues to support the emulation or even contains misleading information (e.g., a buggy firmware). In this work, we propose a new approach that builds peripheral models from the peripheral specification. Using NLP, we translate peripheral behaviors in human language (documented in chip manuals) into a set of structured condition-action rules. By checking, executing, and chaining them at run time, we can dynamically synthesize a peripheral model for each firmware execution. The extracted condition-action rules might not be complete or even be wrong. We, therefore, propose incorporating symbolic execution to quickly pinpoint the root cause. This assists us in the manual correction of the problematic rules. We have implemented our idea for five popular MCU boards spanning three different chip vendors. Using a new edit-distance-based algorithm to calculate trace differences, our evaluation against a large firmware corpus confirmed that our prototype achieves much higher fidelity compared with state-of-the-art solutions. Benefiting from the accurate emulation, our emulator effectively avoids false positives observed in existing fuzzing work. We also designed a new dynamic analysis method to perform driver code compliance checks against the specification. We found some non-compliance which we later confirmed to be bugs caused by race conditions.

In the smart home platform communication, the access token might properly represent the user’s identity and access permissions. Any access token used in multi-user smart home access should be rigorously regulated by the cloud to guarantee that users only use their devices in permitted ways. However, we were astonished to discover that access tokens in certain popular smart home platforms are unfettered, allowing attackers to illegally eavesdrop on or control IoT devices connected to the cloud. While the access token is essential for managing smart home permissions, there are currently no security checks in place. The fundamental reason is that many standard Web testing tools that check the security of access tokens are disabled by SSL/TLS encryption. Furthermore, whereas many previous studies have focused on the security of OAuth2.0 or smart home apps, only a small amount of research has combined the two. We presented a systematic analysis on smart home platform access token security in this paper. Furthermore, we created a testing tool that allowed us to reuse the app’s underlying logic while also overcoming SSL/TLS encryption issues. We used this tool to examine the security of access tokens in a number of major smart home platforms. Finally, we discovered three types of security issues in seven platforms, one of which is the DoR (Denial of Refresh) flaw, which we discovered for the first time. Our tests revealed that attackers may use these security issues to exploit a total of 106 cloud APIs, posing a serious security risk to device owners.

The Internet of Things (IoT) platforms have been widely used in many application scenarios, especially for the smart home. Under the management of the IoT platform, a massive amount of IoT devices have been connected between remote cloud servers and users’ mobile terminals. While bringing unprecedented convenience for device manufacturers and smart home users, the existence of mainstream IoT platforms has also become the primary target for malicious attackers. Thus, many intrusion detection mechanisms of specific IoT platform traffic have been proposed. However, as a prerequisites work of intrusion detection or vulnerability assessment, identifying target IoT platform traffic among real-world network traffic has not been deeply studied. Given this situation, we first time proposed and achieved “fingerprinting” for IoT platform traffic. We designed a set of standardized workflows of traffic capturing, fingerprint feature extraction, and fingerprint model construction. Based on such workflow, we implemented a software tool named IoTPF for distinguishing the traffic between the mobile terminal and remote server of different mainstream IoT platforms among network traffic. We also tested the usability and performance of IoTPF. Finally, we discuss the application scenarios of fingerprinting on IoT platforms.

Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users’ data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners’ sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at leasttgroup managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.

With the thriving of the Android ecosystem, codes are widely reused in Android apps in the form of third-party libraries. Recent research shows that emerging third-party libraries may introduce a lot of privacy risks and other security threats. Nevertheless, current approaches on libraries identification are far away from the demand for accuracy and efficiency. In this paper, we present LibHawkeye, a \jice{new} clustering-based technique to identify third-party libraries in millions of Android apps. Our approach utilizes four different kinds of dependencies inside Android apps to build intra-app dependency graphs but discards package homogeny which is heavily depended upon by most previous works. What's more, we propose three steps of refinement to eliminate false positives in the initial result as much as possible. The experiment on 1,000 apps reports that compared to existing tools, LibHawkeye can precisely identify at least 26.5\% more libraries. We also evaluate it with 3,987,206 Android apps published in Google Play, and the accuracy of sampled libraries from the clustering result is 93.25\%. Results show that LibHawkeye significantly outperforms the state-of-the-art tools without loss of scalability.

To improve data availability and durability, cloud users would like to store multiple copies of their original files at servers. The multi-copy auditing technique is proposed to provide users with the assurance that multiple copies are actually stored in the cloud. However, most multi-replica solutions rely on Public Key Infrastructure (PKI), which entails massive overhead of certificate computation and management. In this article, we propose an efficient multi-copy dynamic integrity auditing scheme by employing certificateless signatures (named MDSS), which gets rid of expensive certificate management overhead and avoids the key escrow problem in identity-based signatures. Specifically, we improve the classic Merkle Hash Tree (MHT) to achieve batch updates for multi-copy storage, which allows the communication overhead incurred for dynamics to be independent of the replica number. To meet the flexible storage requirement, we propose a variable replica number storage strategy, allowing users to determine the replica number for each block. Based on the fact that auditors may frame Cloud Storage Servers (CSSs), we use signature verification to prevent malicious auditors from framing honest CSSs. Finally, security analysis proves that our proposal is secure in the random oracle model. Analysis and simulation results show that our proposal is more efficient than current state-of-the-art schemes.

An IoT device today can be managed through different channels, e.g., by its device manufacturer's app, or third-party channels such as Apple's Home app, or a smart speaker. Supporting each channel is a management framework integrated in the device and provided by different parties. For example, a device that integrates Apple HomeKit framework can be managed by Apple Home app. We call the management framework of this kind, including all its device- and cloud-side components, a device management channel (DMC). 4 third-party DMCs are widely integrated in today's IoT devices along with the device manufacturer's own DMC: HomeKit, Zigbee/Z-Wave compatible DMC, and smart-speaker Seamless DMC. Each of these DMCs is a standalone system that has full mandate on the device; however, if their security policies and control are not aligned, consequences can be serious, allowing a malicious user to utilize one DMC to bypass the security control imposed by the device owner on another DMC. We call such a problem Chaotic Device Management (Codema).

In recent years, Internet-of-Things (IoT) platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This article seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems and discuss the lessons we learned from these bugs. In particular, 20 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

Deduplication, which can save storage cost by enabling us to store only one copy of identical data, becomes unprecedentedly significant with the dramatic increase in data stored in the cloud. For the purpose of ensuring data confidentiality, they are usually encrypted before outsourced. Traditional encryption will inevitably result in multiple different ciphertexts produced from the same plaintext by different users' secret keys, which hinders data deduplication. Convergent encryption makes deduplication possible since it naturally encrypts the same plaintexts into the same ciphertexts. One attendant problem is how to reliably and effectively manage a huge number of convergent keys. Several deduplication schemes have been proposed to deal with the convergent key management problem. However, they either need to introduce key management servers or require interaction between data owners. In this paper, we design a novel client-side deduplication protocol named KeyD without such an independent key management server by utilizing the identity-based broadcast encryption (IBBE) technique. Users only interact with the cloud service provider (CSP) during the process of data upload and download. Security analysis demonstrates that KeyD ensures data confidentiality and convergent key security, and well protects the ownership privacy simultaneously. A thorough and detailed performance comparison shows that our scheme makes a better tradeoff among the storage cost, communication and computation overhead.

There is a rapidly growing interest in the security of cyber-physical systems (CPS) and internet-of-things (IoT) in industry, government and academia. NIST recently created a Cyber-Physical Systems group and it is leading a public-private initiative to identify a general architecture, design principles, solutions, and challenges ahead. In Europe, the Horizon 2020 research program has targeted security issues relating to cyber-physical infrastructures and Internet of Things, while the fundamental science program CHIST-ERA launched calls for research projects on resilient trustworthy cyber-physical systems (2015) and user-centered security and privacy in the Internet of Things (2016). In the US, grant calls such as the 2019 CPS grant call by NSF (total of 50M USD) show the significance of CPS&IoT and their security.

With the increasing popularity of the Internet of Things (IoT), many IoT cloud platforms have emerged to help the IoT manufacturers connect their devices to their users. Serving the device-user communication is general messaging protocol deployed on the platforms. Less clear, however, is whether such protocols, which are not designed to work in the adversarial environment of IoT, introduce new risks. In this paper, we report the first systematic study on the protection of major IoT clouds (e.g., AWS, Microsoft, IBM) put in place for the arguably most popular messaging protocol - MQTT. We found that these platforms' security additions to the protocol are all vulnerable, allowing the adversary to gain control of the device, launch a large-scale denial-of-service attack, steal the victim's secrets data and fake the victim's device status for deception. We successfully performed end-to-end attacks on these popular IoT clouds and further conducted a measurement study, which demonstrates that the security impacts of our attacks are real, severe and broad. We reported our findings to related parties, which all acknowledged the importance. We further propose new design principles and an enhanced access model MOUCON. We implemented our protection on a popular open-source MQTT server. Our evaluation shows its high effectiveness and negligible performance overhead.

Federated learning can combine a large number of scattered user groups and train models collaboratively without uploading data sets, so as to avoid the server collecting user sensitive data. However, the model of federated learning will expose the training set information of users, and the uneven amount of data owned by users in multiple users' scenarios will lead to the inefficiency of training. In this article, we propose a privacy-preserving federated learning scheme in fog computing. Acting as a participant, each fog node is enabled to collect Internet-of-Things (IoT) device data and complete the learning task in our scheme. Such design effectively improves the low training efficiency and model accuracy caused by the uneven distribution of data and the large gap of computing power. We enable IoT device data to satisfy ε -differential privacy to resist data attacks and leverage the combination of blinding and Paillier homomorphic encryption against model attacks, which realize the security aggregation of model parameters. In addition, we formally verified our scheme can not only guarantee both data security and model security but completely resist collusion attacks launched by multiple malicious entities. Our experiments based on the Fashion-MNIST data set prove that our scheme is highly efficient in practice.

The Second Workshop on Internet of Things Security and Privacy is held in London, UK on November 15, 2019, co-located with the ACM Conference on Computer and Communications Security (CCS). The workshop aims to address the security and privacy challenges of the emerging Internet-of-Things landscape. The workshop aims to bring together academic and industrial researchers, and to that end, we have put together an exciting program offering a mix of current and potential challenges. The workshop will also features 8 papers, 2 posters, and an invited keynote.

With the development of the attack and defense technology, more and more network objects become the targets of cyber attack. However, these objects have different defense goals because of the different network functions and assets they have. Therefore, the defender needs to develop the optimal defense strategy based on their defense goals. Game theory based methods can help defender to develop the optimal defense strategy. However, there is no recognized effective method for utility calculation within the game theory model. In this paper, we introduced a hybrid model, which applied the analytic hierarchy process(AHP) on utility calculation and applied the attack-defense stochastic game model to develop the optimal defense strategy. Compared with previous methods, our model is more comprehensive and flexible, which is more competent to develop the optimal defense strategy, and through a case study, we demonstrated its effectiveness.

Traffic-related messages exchange (TME) is considered as a powerful approach to improve traffic safety and efficiency in vehicular networks. However, TME assumes all vehicles always are honest, and thus offering opportunities for attackers to fake traffic-related messages. To combat such threat, recent efforts have been made to trust mechanism. In this article, a vulnerability for trust mechanism is found, that is, the ratings from initiator vehicles (IVs) are generally unchecked. Such ratings corresponding to the truth of traffic-related events can be exploited by attackers to disturb trust mechanism. Specially, attackers would form a clique to help with each other in an inside-and-outside collusive (IOC) manner. One of the IOC attackers can disguise as an IV who sends the rating in accordance with the traffic-related messages of his conspirators, result in promoting their trust value quickly. With high trust value, attackers can escape the detection of trust mechanism. We conduct an in-depth investigation on IOC attack and propose a defense scheme called TFAA from the design ideas of trust fluctuation association analysis. In addition, the trust data management of central and distributed trust mechanism may be unsuitable for vehicular networks. To support the trust data management for the TFAA scheme, we also design a semi-distributed trust data storage scheme called TruChain with the combination of consortium blockchain and vehicular regions partition. The simulation results show that the TFAA scheme can enhance the accuracy of trust value evaluation, and thus successfully reducing the power of IOC attack against TME.

An Internet of Things (IoT) system generally contains thousands of heterogeneous devices which often operate in swarms-large, dynamic, and self-organizing networks. Remote attestation is an important cornerstone for the security of these IoT swarms, as it ensures the software integrity of swarm devices and protects them from attacks. However, current attestation schemes suffer from single point of failure verifier. In this paper, we propose an Efficient and Secure Distributed Remote Attestation (ESDRA) scheme for IoT swarms. We present the first many-to-one attestation scheme for device swarms, which reduces the possibility of single point of failure verifier. Moreover, we utilize distributed attestation to verify the integrity of each node and apply accusation mechanism to report the invaded nodes, which makes ESDRA much easier to feedback the certain compromised nodes and reduces the run-time of attestation. We analyze the security of ESDRA and do some simulation experiments to show its practicality and efficiency. Especially, ESDRA can significantly reduce the attestation time and has a better performance in the energy consumption comparing with list-based attestation schemes.

Internet of Things (IoT) is an increasingly popular technology that enables physical devices, vehicles, home appliances, etc., to communicate and even inter operate with one another. It has been widely used in industrial production and social applications including smart home, healthcare, and industrial automation. While bringing unprecedented convenience, accessibility, and efficiency, IoT has caused acute security and privacy threats in recent years. There are increasing research works to ease these threats, but many problems remain open. To better understand the essential reasons of new IoT threats and the challenges in current research, this survey first proposes the concept of “IoT features.” Then, we discuss the security and privacy effects of eight IoT features including the threats they cause, existing solutions to threats and research challenges yet to be solved. To help researchers follow the up-to-date works in this field, this paper finally illustrates the developing trend of IoT security research and reveals how IoT features affect existing security research by investigating most existing research works related to IoT security from 2013 to 2017.

Password-based authenticated key exchange (PAKE) protocol has been widely used in practice, since it is convenient for users. However, the easy-to-remember property of the password also brings security problem. In this paper, we show there is an off-line dictionary attack in an efficient PAKE protocol when the smart card is lost. In order to resist the attack, we give a countermeasure to improve it. The countermeasure makes a simple change to the original protocol which does not affect the efficiency of the protocol.

Non peer reviewed

The First Workshop on Internet of Things Security and Privacy is held in Dallas, TX, USA on November 3, 2017, co-located with the ACM Conference on Computer and Communications Security (CCS). The workshop aims to address the security and privacy challenges of the emerging Internet-of-Things landscape. The workshop aims to bring together academic and industrial researchers, and to that end, we have put together an exciting program offering a a mix of current and potential challenges. The workshop will also features 12 papers, 4 posters, and an invited keynote.

In this paper, we investigate the problem of integrity auditing for cloud deduplication storage. Specifically, in addition to the outsourced data confidentiality, we also aim to ensure the integrity of the deduplicated cloud storage. With the existing works based on Provable Data Possession (PDP)/Proof of Retrievability (PoR), we are either required to rely on a fully trusted proxy server or inevitably sacrifice the privacy and efficiency. In contrast, we present a novel message-locked integrity auditing scheme without an additional proxy server, which is applicable to both file-level and chunk-level deduplication systems. In particular, our scheme is storage efficient in the sense that apart from eliminating the ciphertext redundancy, we also enable the integrity tag deduplication by a message-derived signing key, which merely incurs minimal client-side computation overhead. Besides, we can still publicly perform the integrity check over any client's cloud storage by incorporating the proxy re-signature technique. We show that the proposed scheme will not disclose the data ownership information and is provably secure under the Computational Diffie-Hellman (CDH) assumption in the random oracle model. Finally, the performance evaluation demonstrates its effectiveness and efficiency.

Uploading data streams to a resource-rich cloud server for inner product evaluation, an essential building block in many popular stream applications (e.g., statistical monitoring), is appealing to many companies and individuals. On the other hand, verifying the result of the remote computation plays a crucial role in addressing the issue of trust. Since the outsourced data collection likely comes from multiple data sources, it is desired for the system to be able to pinpoint the originator of errors by allotting each data source a unique secret key, which requires the inner product verification to be performed under any two parties' different keys. However, the present solutions either depend on a single key assumption or powerful yet practically-inefficient fully homomorphic cryptosystems. In this paper, we focus on the more challenging multi-key scenario where data streams are uploaded by multiple data sources with distinct keys. We first present a novel homomorphic verifiable tag technique to publicly verify the outsourced inner product computation on the dynamic data streams, and then extend it to support the verification of matrix product computation. We prove the security of our scheme in the random oracle model. Moreover, the experimental result also shows the practicability of our design.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.

In the multi-label classification issue, some implicit constraints and dependencies are always existed among labels. Exploring the correlation information among different labels is important for many applications. It not only can enhance the classifier performance but also can help to interpret the classification results for some specific applications. This paper presents an improved multi-label classification method based on local label constraints and classifier chains for solving multi-label tasks with large number of labels. Firstly, in order to exploit local label constraints in multi-label problem with large number of labels, clustering approach is utilized to segment training label set into several subsets. Secondly, for each label subset, local tree-structure constraints among different labels are mined based on mutual information metric. Thirdly, based on the mined local tree-structure label constraints, a variant of classifier chain strategy is implemented to enhance the multi-label learning system. Experiment results on five multi-label benchmark datasets show that the proposed method is a competitive approach for solving multi-label classification tasks with large number of labels.

Abstract Machine‐type communication (MTC) is a very important application of the Internet of things. It has a vast market and application scenarios. However, supporting a large number of low‐power devices transmission is an important issue in long‐term evolution/long‐term evolution advanced (LTE/LTE‐A) networks. Specifically, when a large number of machine‐type communication devices (MTCDs) with low‐power consumption requirements simultaneously request access to the LTE/LTE‐A networks, each MTCD needs an independent complete access authentication process with core network, which may cause a serious signaling congestion in the core network. To solve this problem, in this paper, we propose a novel group authentication protocol with privacy‐preserving for MTC in the LTE/LTE‐A networks. The proposed protocol cannot only simultaneously authenticate a group of MTCDs and minimize the signaling overhead but also provide robust privacy‐preserving for each MTCD (including anonymity, unlinkability, and traceability). In particular, our scheme can avoid denial of service attack by filtering some illegal devices in the first four procedures of the mutual authentication. Moreover, our scheme fulfills all the security requirements of the MTC in LTE/LTE‐A networks. In addition, the formal verification by the ProVerif tool shows that the proposed scheme is secure against various malicious attacks, and the performance evaluation indicates that it achieves outstanding results in terms of signaling and computation overhead. Copyright © 2016 John Wiley & Sons, Ltd.

Cooperative spectrum sensing CSS for vacant spectrum is one of the key techniques in cognitive radio networks. However, most of CSS schemes assume secondary users SUs to always tell the truth, and thus offering opportunities for malicious SUs to launch the spectrum-sensing data falsification attack SSDF attack. To combat such vicious behaviors, recent efforts have been made to trust evaluation. In this paper, we argue that powering CSS with traditional trust schemes is not enough. The intermittent SSDF attack ISSDF attack is found in this paper. Unlike SSDF, ISSDF attackers can maintain high trustworthiness in an alternant process of reporting true or false sensing data, resulting in difficultly detecting malicious SUs in trust schemes. To defend against ISSDF attack for CSS, a novel-trust scheme using dynamic evaluation is proposed in this paper. Simulation results show that this scheme can successfully reduce the power of ISSDF and thus can ensure the performance of CSS. Copyright © 2015John Wiley & Sons, Ltd.

Abstract To provide secure roaming services for mobile users in Global Mobility Networks, many schemes have been proposed in recent years. However, most of them focus only on authentication and fail to satisfy many practical security requirements such as user anonymity and untraceability. To address this problem, we propose a privacy‐preserving authentication scheme based on elliptic curve cryptography. The proposed scheme is provably secure under a formal model that satisfies all practical security requirements. Compared with existing authentication schemes, ours enjoys better performance in terms of computation cost and security. Copyright © 2015 John Wiley & Sons, Ltd.

Third-party tracking has proliferated across the whole Internet in recent years. To protect Web users, much effort has been spent and almost all of them merely choose to stop third-party service to prevent tracking. However, users should be in total charge of their personal information and it does not mean that every user would like to give up personalization service to protect privacy in any case. In this poster, we present a new approach to make a balance between privacy and personalization through recommendation system, which can help users judge the level of privacy threat so that users can choose between privacy and personalization in a reasonable way.

ABSTRACT This paper concerns about logic vulnerabilities that result from faulty logic of a web application. Logic vulnerabilities typically accompany with the exposure of unexpected functionalities and lead to the bypass of the intended constraints. From a semantic perspective, logic vulnerabilities occur when mistakes arise in the control flows guarding the processes of invoking critical functionalities. In this paper, we propose the first lightweight static analysis approach to automatically detect logic vulnerabilities in Java web applications. Logic errors in our approach are characterized as erroneous invocations of functionalities. Program‐slicing technique has been leveraged to capture the processes of invoking critical functionalities. A back‐tracing algorithm is originally designed to extract control flows guarding functionality‐invocation processes. Finally, logic vulnerability detection is transformed into mining abnormal functionality‐invocation processes in a cluster of similar ones by comparing these processes' control flows. We implemented our approach in a prototype tool named logic vulnerability detector and evaluated it on seven real‐world applications scaled from thousands to million lines of code. The evaluation results show that our approach achieves bigger coverage with acceptable cost and better scalability than previous approaches. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT By integrating the traditional grid with the advanced communication and information technologies, smart grid can provide a reliable and efficient energy service for our modern society. Data aggregation plays an important role in evaluating the current energy usage information of consumer domains, based on which the operation center can accommodate distributed power sources to maximize the utilization efficiency. However, it also incurs a potential risk to the consumer privacy. In this paper, we propose an anonymous multi‐dimensional data aggregation for smart grid systems. With the proposed scheme, the operation center can compute both additive and non‐additive aggregation functions over the collected reports from consumers. The computation cost of each consumer is independent of the number of collected data types. In addition, by using the batch verification technique, the operation center's computation cost can be significantly reduced. The security analysis demonstrates that the proposed scheme can achieve identity privacy preserving, data authentication, and confidentiality. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT This study focuses on the false feedback of spectrum information and the malicious behavior that forcibly occupies spectrum resources in cognitive radio networks. A credible spectrum sensing and allocation model based on fuzzy theory is proposed. The spectrum sensing behavior and spectrum utilization behavior are taken as two evaluation factors. On the basis of subjectivity and uncertainty of trust, this study adopts the fuzzy comprehensive evaluation method to construct the trust evaluation of nodes. In cooperative spectrum sensing, comprehensive evaluation results can be used to identify malicious nodes and eliminate false feedback information. In spectrum allocation, the degree of lattice closeness among fuzzy sets is used to define and calculate the difference between the actual comprehensive evaluation set and the ideal comprehensive evaluation. The difference is calculated to quantify the credibility of non‐malicious nodes and to determine the allocation of spectrum resources to nodes using the multi‐objective optimization algorithm. These techniques can control the malicious behaviors of nodes and encourage cooperative behavior among nodes. Consequently, the joint design of spectrum sensing at the physical layer and spectrum allocation at the media access control layer is realized. Simulation results and analysis indicate that under malicious attacks, the proposed model has sound performance in terms of system sensing, throughput, and fair spectrum allocation compared with existing models. Copyright © 2013 John Wiley & Sons, Ltd.

Provable data possession (PDP) is a probabilistic proof technique for cloud service providers (CSPs) to prove the clients' data integrity without downloading the whole data. In 2012, Zhu et al. proposed the construction of an efficient PDP scheme for multicloud storage. They studied the existence of multiple CSPs to cooperatively store and maintain the clients' data. Then, based on homomorphic verifiable response and hash index hierarchy, they presented a cooperative PDP (CPDP) scheme from the bilinear pairings. They claimed that their scheme satisfied the security property of knowledge soundness. It is regretful that this comment shows that any malicious CSP or the malicious organizer (O) can generate the valid response which can pass the verification even if they have deleted all the stored data, i.e., Zhu et al.'s CPDP scheme cannot satisfy the property of knowledge soundness. Then, we discuss the origin and severity of the security flaws. It implies that the attacker can get the pay without storing the clients' data. It is important to clarify the scientific fact to design more secure and practical CPDP scheme in Zhu et al.'s system architecture and security model.

Flash objects are widely embedded in web pages, supporting Rich Internet Applications using ActionScript. However, according to our survey, many Flash objects are seriously exposed to Cross-site Scripting vulnerabilities as they are usually coded without proper sanitization of their inputs. This becomes a potential danger for cyber users. In this paper, we analyze XSS in online Flash and present an engine FXD (Flash XSS Detector) for automatically scrambling Flash files in web pages and checking whether or not they are vulnerable to XSS. We call vulnerable ActionScript functions "key functions" and divide them into four categories by its functionality. The usability of FXD is further evaluated by disposing it in real-world websites. Our results reveal that at least 48 Flash applications in 18% of Alexa top 100 sites on the web are vulnerable to XSS. Each of these vulnerable Flash objects has been verified and confirmed of their XSS flaws. Finally, we discuss a new trend of Flash XSS, nowadays it is mainly caused by combination of key functions in different categories.

Fuzz testing is an automated testing technique where random data is used as an input to software systems in order to reveal security bugs/vulnerabilities. Fuzzed inputs must be binaries embedded with compiled bytecodes when testing against ActionScript virtual machines (AVMs). The current fuzzing method for JavaScript-like virtual machines is very limited when applied to compiler-involved AVMs. The complete source code should be both grammatically and semantically valid to allow execution by first passing through the compiler. In this paper, we present ScriptGene, an algorithmic approach to overcome the additional complexity of generating valid ActionScript programs. First, nearly-valid code snippets are randomly generated, with some controls on instruction flow. Second, we present a novel mutation method where the former code snippets are lexically analyzed and mutated with runtime information of the AVM, which helps us to build context for undefined behaviours against compiler-check and produce a high code coverage. Accordingly, we have implemented and evaluated ScriptGene on three different versions of Adobe AVMs. Results demonstrate that ScriptGene not only covers almost all the blocks of the official test suite (Tamarin), but also is capable of nearly twice the code coverage. The discovery of six bugs missed by the official test suite demonstrates the effectiveness, validity and novelty of ScriptGene.

The Android provides a permission-based security model to restrict the operations that each application can perform; however, it has been shown to be vulnerable to privilege escalation attacks. Applications can cooperate to perform operations that forbidden to perform separately which may lead to privacy leakage. In this poster, we present the design of a new policy-centered security framework against the application-level privilege escalation attacks. Different from previous policy-centered schemes, the communication content is also considered into the inspection besides the permissions. Specially, we allow the privacy information selectively to be passed in the middleware and deploy a mandatory access control at the kernel based on the dynamical taint tracking. Test results show that it can prevent known confused deputy attacks and is also flexible to prevent the unknowns; furthermore it can reduce the false positives of preventing colluding attacks compared to the previous work.

ABSTRACT Recently two‐factor authenticated key exchange (AKE) protocols have been widely used in various networks to ensure communication security. In 2009, Sun et al. proposed an improved password authenticated key agreement scheme based on smart cards. It is efficient in two‐factor authentication key exchange protocols. However, we found that the scheme of Sun et al. was vulnerable to a password‐guessing attack and a key compromise impersonation (KCI) attack. We made an improvement on the scheme of Sun et al. by using an elegant but simple trick to eliminate the security vulnerabilities. The improved scheme possesses untraceability, which is absent in the scheme of Sun et al. but important in anonymous communication. Moreover, the new scheme can also resist KCI attack, which is a strong secure property in AKE protocol. We proved the security of our protocol in the random oracle model provided that the Elliptic Curve Gap Diffie–Hellman assumption holds. The trick used in this paper can provide a new way for designing authenticated key agreement in two‐factor authentication mechanism. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Session Initiation Protocol, which can be applied to various networks for establishing, modifying, and terminating multimedia sessions, has received much attention in these years because of its wide applications. Because different users may belong to different networks and different networks may have different Key Generation Centers in identity‐based networks, how to make different networks be compatible and how to make users belong to different Key Generation Centers can establish a secure Session Initiation Protocol session become two key problems. In this paper, we propose a new certificateless authenticated key agreement protocol for different networks to address the problems. The protocol is provably secure against a fully adaptive adversary in the random oracle model, provided that the underlying problem of Gap Bilinear Diffie–Hellman problem is hard. Moreover, our scheme has good performance compared with the certificateless authentication key agreement protocols using pairings. The trick proposed in this paper may give some help in making different networks be compatible. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT It is highly probable that many requests for the same service (popular/hot videos) arrive at the service provider in a short time or even simultaneously in a pay‐TV system. In conventional schemes, for nonrepudiation of communication, the service provider verifies each service request signed by subscribers one by one, which results in a high computation burden and long delay. In this paper, we propose an efficient privacy‐preserving authentication mechanism for mobile pay‐TV systems. The mechanism adopts batch verification technique, allowing the service provider to verify multiple requests from different subscribers in a batch manner instead of one by one. In addition, a hand‐off authentication mechanism with privacy preserving based on proxy signature cryptography is also proposed to support mobile pay‐TV systems. With the proxy signature technique, the communication is only between a subscriber and a new transmitter while the head end system is no longer involved during hand‐off. Simulation results show that even in the case of high bogus request ratio (p = 25 %), batch verification still excels individual verification in performance. Moreover, the proposed protocol only requires point multiplication operations on subscribers. Therefore, this scheme enjoys computation and communication efficiency compared with the existing schemes. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Symmetric polynomial‐based key distribution scheme has been widely adopted in various communication applications. This type of key distribution consists of a server and a set of users, where the server is responsible to distribute shares for each user via a symmetric polynomial. Based on the property of symmetry of this polynomial, each pair of users can compute a common secret key using their shares for establishing a secure communication channel. However, some users may receive faulty shares from the server because of some uncertain factors in the communication environment, such as software failures and transmission errors. As a result, the users who receive faulty shares cannot share common secret keys with other users. To solve this problem, in this paper, we propose two individual verifiable key distribution schemes on the basis of a symmetric polynomial based key distribution. In both our proposed schemes, the server adopts the same approach to distribute shares for users; the users are able to verify the validity of their shares without revealing them before establishing communication channels. If all shares are verified valid, users can ensure that each pair of them possesses a common secret key, they can establish secure communication channels when needed; otherwise, all users can collaborate to identify those users who possess faulty shares and require the server to distribute a set of valid shares for those users. Furthermore, both our proposed schemes are efficient, because the procedures of verification and identification do not involve any complicated cryptographic operation. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Fuzz testing, also known as fuzzing, has long been recognized as an effective technique to detect software vulnerabilities. Unfortunately, this approach is demonstrated noneffective when applied to test programs with highly structured inputs, such as interpreters and compilers. These programs usually process inputs in several stages as lexing and parsing, where the test input will be rejected if its structure does not obey the grammar. In this paper, we present a novel approach for fuzzing highly structured input programs. By disassembling the existing test cases into multiple grammatical fragments and inferring their grammar structures, we build a new series of test cases that can pass the validation and reach the previously unexplored places in the target program. We have implemented this approach in our general fuzzing framework BlendFuzz. Experiments have shown that BlendFuzz achieves higher code coverage compared with other blackbox fuzzing tools. BlendFuzz has also detected over two dozens of previously unreported vulnerabilities in real‐world applications, with seven of them being considered high risky. Copyright © 2013 John Wiley & Sons, Ltd.

ABSTRACT Internet Key Exchange (IKE) protocol is widely applied on the Internet to protect confidentiality of the Internet communication. However, there are many high‐risk security vulnerabilities in various IKE implementations. Traditional fuzzing approaches with the aim of discovering vulnerabilities have some blind spots, such as time‐consuming, low efficiency, and low degree of automation. This paper introduces a new vulnerability discovering approach based on fuzzing and applies the approach to the IKE protocol. Through summarizing the most comprehensive vulnerable points of IKE protocol and proposing a two‐stage test cases library, an IKE protocol vulnerability discovering tool called IKEProFuzzer is designed and implemented. It is a network protocol fuzzing framework with extensibility and automated Monitor/Debugger designed by ourselves. In the experiments, IKEProFuzzer has discovered 14 vulnerabilities, including nine released vulnerabilities and five unreleased ones, which affect many kinds of routers and applications. The evaluation results prove the feasibility, efficiency, and extensibility of the approach compared with the existing approaches. Copyright © 2012 John Wiley & Sons, Ltd.

With the character of low maintenance, cloud computing provides an economical and efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of our scheme in experiments.

In wireless network, a secure roaming authentication protocol enables a mobile user to get services from a foreign server when he/she is outside of the home server. However, the conventional approach requires the home server's participation during the authentication between the mobile user and the foreign server. So the larger number of the roaming requests are performed the heavier burden will be on the home server. Meanwhile, in wireless communication the privacy protection is also of great concern for the mobile user. In this paper we propose a lightweight roaming authentication protocol for anonymous wireless communication without the home server's participation. The new roaming authentication protocol takes advantage of the ID-based cryptography and provides user anonymity. It has good performance compared with the roaming authentication protocols whose authentication do not need the home server's participation in terms of security and computation costs. Moreover, it can be applied to various kinds of wireless networks such as Cellular Networks and Wireless Mesh Networks.

In this paper, we propose a handover authentication scheme with location privacy preserving based on the proxy ring signature scheme for EAP-based wireless networks. First, we integrate an efficient ring signature and a proxy signature into a proxy ring signature scheme, which allows the mobile node (MN) to be authenticated without revealing its identity and location privacies due to the inherent anonymity of the proxy ring signature. Second, our scheme only requires point multiplication operations on the resource-constraints MN, thus, it is suitable for low-power mobile devices in the wireless networks. Finally, an extensive simulation is given to validate the performance of the proposed scheme. The results demonstrate that our scheme is relatively efficient in terms of computation and communication overhead.

Logic vulnerabilities occur when mistakes arise in the control flow associated to critical functionalities. We propose a lightweight static analysis approach to detect logic vulnerabilities in Java Web applications. The core idea of our approach is to discover deviant behaviors among duplication samples. Program slicing technique is leveraged to extract duplicated invocations targeted similar functionalities. Subsequently, path exploration is conducted to split slices into several path sensitive slices. Then we make comparison between any two similar slices on their path condition, and report the slices with abnormal path condition as logic vulnerabilities. We implemented our approach in a prototype tool named LVD (Logic Vulnerability Detector), and evaluated it on seven real world applications scaled from thousands to million lines of code. The evaluation results show that our approach achieves bigger coverage with acceptable cost and better scalability than previous approaches.

Anonymous signcryption provides anonymity of the sender with the advantages of signcryption. When a sender wants to send a message to multiple receivers in the confidential and authenticated way, multi receiver signcryption is needed. In 2010, Zhang et al. proposed an identity based anonymous signcryption scheme for multiple receivers which is proved secure in the standard model. At the same time, Lal et al. designed another identity based anonymous signcryption scheme for multiple receivers in the random oracle model. Unfortunately, we show that the two schemes do not satisfy the semantic security. Then we improve their corresponding anonymous signcryption schemes that remedy the weaknesses of the above two schemes. Our proposed schemes satisfy the semantic security, unforgeability, signcrypter identity's ambiguity, and public authenticity.

Fuzz testing has been widely used in practice to detect software vulnerabilities. Traditional fuzzing tools typically use blocks to model program input. Despite the demonstrated success of this approach, its effectiveness is inherently limited when applied to test programs that process grammatical inputs, where the input data are mainly human-readable text with complex structures that are specified by a formal grammar. In this paper we present BlendFuzz, a fuzz testing framework that is grammar-aware. It works by breaking a set of existing test cases into units of grammar components, then using these units as variants to restructure existent test data, resulting in a wider range of test cases that have the potential to explore previously uncovered corner cases when used in testing. We've implemented this framework along with two language fuzzers on top of it. Experiments with these fuzzers have shown improved code coverage, and field testing has revealed over two dozens of previously unreported bugs in real-world applications, with seven of them being medium or high risk zero-day vulnerabilities.

In 2011, Sun et al. proposed a security architecture to ensure unconditional anonymity for honest users and traceability of misbehaving users for network authorities in wireless mesh networks (WMNs). It strives to resolve the conflicts between the anonymity and traceability objectives. In this paper, we attacked Sun et al. scheme's traceability. Our analysis showed that trusted authority (TA) cannot trace the misbehavior client (CL) even if it double-time deposits the same ticket.

Extensible Authentication Protocol (EAP) is a framework which aims to provide a flexible authentication for wireless networks. Due to the involvement of an EAP server and several round trips between a mobile node (MN) and the EAP server, a full EAP authentication takes about 1000ms which is unacceptable in a handover process. This paper proposes a privacy preserving handover authentication scheme for EAP-based wireless networks. We use the proxy signature scheme to accomplish authentication between MN and an access point (AP) without involving the third party. The detailed security analysis shows that our scheme can achieve the privacy preserving and forward/backward security. In addition, we evaluate the latency performance of the proposed scheme by the analysis and simulation. The results demonstrate that our scheme is more efficient in terms of computation and communication overheads.

In order to improve the security of ring signature, we combine standard ring signature and key-insulated cryptography. We give the definition and security model for ID-based key-insulated ring signature, and at the same time we proposed an ID-based strong key-insulated ring signature scheme. This scheme can deal with key-exposure problem. The proposed ring signature scheme enjoys several attractive features: (1)it is provably secure in standard model, (2)it is unconditional anonymous, (3)it is strong key-insulated, (4)it allows frequent key-updates without increasing the risk of helper key-exposure, and thus enhances the security of the system.

Recently, IEEE 802.16m was submitted to the ITU and acknowledged as the candidate technology of the IMT-advanced standardization. In this paper, we point out several security issues, such as key agreement blocking and Denial of Service (DoS) attacks, of the key management protocol in IEEE 802.16m. To address the found security issues, we propose an enhanced key management protocol, named as EKMP, which extends the pairwise master key and adds the integrity protection to all key agreement messages. EKMP can not only provide advanced relay station access but also avoid key agreement blocking and DoS attacks. In particular, our scheme is provably secure in the Canetti-Krawczyk model. Furthermore, our theoretical analysis shows that EKMP is much more efficient in terms of communication overhead and memory requirement.

In 2009, Han et al. proposed an efficient threshold self-healing key distribution scheme with sponsorization for infrastructureless wireless networks. They claimed that the key distribution scheme satisfies the forward security, i.e., any internal user who has been revoked can not generate a new session key. In this paper,an attack method against this key distribution scheme's forward security was presented. Furthermore, this attack method can also be applied to this scheme's backward security.Thus,the original threshold self-healing key distribution scheme is insecure.

Most of the current reputation models for wireless sensor networks (WSNs) do not consider seriously the vulnerabilities of reputation models themselves, which makes them easily manipulated by traitor attacks. The traitors can launch attacks against reputation models on both service level and recommendation level. Until now, recommendation traitor attack has received little attention, although it causes great harm to reputation models. In this paper, we build an economic Cobweb model for traitor attacks on both of the two levels to exactly formalize traitors' behaviors. Then we propose an attack-resilient scheme inspired by Macroeconomic Control. Through reputation fluctuation analysis, the traitors can be discriminated from benign sensors and then punished. In the simulations we verify that although both traitor attacks cause great harm to reputation models, our scheme can discriminate traitor sensors from benign sensors and resist traitor attacks effectively with lightweight energy consumption.