Vulnerability root cause analysis (RCA) is a crucial step following the discovery of vulnerabilities. When faced with a multitude of crashes resulting from fuzzing, effective RCA results can assist developers in swiftly identifying and rectifying the root causes of vulnerabilities. Recently, some methods that rely on statistical behavioral differences to analyze the root causes of vulnerabilities have been introduced. However, they suffer from issues such as high time costs, strong randomness, and imprecise results, rendering them impractical for real-world applications. In this paper, we propose an efficient and accurate statistical analysis-based vulnerability RCA approach named RCLocator. We introduce an enhanced crash information tuple extraction tool based on sanitizer to ensure crash consistency during the mutation process of original files. This approach reduces the time cost of the data augmentation stage and enhances the accuracy of RCA. Furthermore, it provides developers with effective explanations for root cause predicates. We evaluate our approach on RCABench and real-world vulnerabilities. The results indicate that RCLocator significantly outperforms state-of-the-art methods, the probability of obtaining correct root cause analysis results increased by 46.7%, and 9.0 times faster in terms of time.

The increasing costs of data breaches have made enhancing security and privacy measures more critical than ever. Static taint analysis has become a key technique for tracking sensitive data propagation. However, existing tools such as FlowDroid and Tai-e face significant challenges related to performance, precision, and the handling of complex taint propagation behaviors, such as those involving arrays and taint elimination. In response, we present EffiTaint, a novel static taint analysis tool. EffiTaint constructs the Pointer Flow Graph (PFG) on demand and employs a taint-driven selective analysis strategy, avoiding the overhead of constructing a complete call graph. EffiTaint enhances the modeling of taint propagation within arrays and taint elimination in program control flows, thereby effectively reducing the generation of erroneous sensitive data flows. Finally, EffiTaint optimizes the source and sink matching strategy using a method extension algorithm, significantly improving the ability to detect taint flows. Benchmarking results demonstrate that EffiTaint achieves high precision (97.5%), high recall (96.7%), and low overhead (average runtime of 26.93 seconds, memory usage of 0.67 GB), making it an effective solution for enhancing privacy and security in Java programs.

With the growing number of software developments and the expansion of functionalities, more and more developers tend to use third-party packages to speed up development and improve efficiency. Due to the openness of npm and the widespread use of Node.js, npm has become the largest open source software ecosystem and therefore a prime target for malicious attackers. Attackers often use various attack vectors to release new malicious packages or destroy existing benign packages, thus posing threats to software that rely on these packages. Therefore, detecting malicious npm packages is of great significance for protecting user security and building a more reliable and secure npm open source software ecosystem.We propose Maldet, an automated malicious npm package detection method. Maldet uses the malicious behavior pattern library we have built as features, training known malicious and benign samples with four different classifiers. In addition, we use a supplementary detector to perform a secondary detect on packages that detected benign, thereby reducing the number of false negatives. The results show that Maldet can detect each package in an average of just a few seconds with 97.12% accuracy, which is superior to other tools, providing high accuracy and fast classification capability.

Abstract Recently, Qiao et al. proposed a novel construction of certificateless aggregate signature (CLAS) scheme to ensure the integrity and authenticity of medical data in healthcare wireless medical sensor networks (HWMSNs). They first created an underlying certificateless signature (CLS) scheme, and then proposed a CLAS scheme from the underlying CLS scheme by adding an aggregation algorithm and a verification algorithm. In this paper, we point out that their CLS scheme is insecure because the Type I adversary can forge valid signatures. That is, the unforgeability is not actually captured by their CLS scheme. Finally, we map our cryptanalysis to the practical application. That is, in the practical application of HWMSNs, the attacker can launch real attack to their CLS scheme using our cryptanalysis to forge signatures. Therefore, Qiao et al.’s CLS scheme can be totally broken.

Summary Federated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption. The requirements of privacy and security in FL are inherently conflicting. Privacy necessitates the concealment of individual client updates, while security requires the disclosure of client updates to detect anomalies. While most existing research focused on the privacy and security aspects of FL, very few studies have addressed the compatibility of these two demands. In this work, we aim to bridge this gap by proposing a comprehensive defense scheme that ensures privacy, security, and compatibility in FL. We categorize the existing literature into two key directions: privacy defense and security defense. Privacy defense includes methods based on additive masks, differential privacy, homomorphic encryption, and trusted execution environment, whereas security defense encompasses distance‐, performance‐, clustering‐, and similarity‐based anomaly detection techniques and statistical information‐based anomaly update bypassing techniques when the server is trusted and privacy‐compatible anomaly update detection techniques when the server is not trusted. In addition, this article presents decentralized FL solutions based on blockchain. For each direction, we discuss specific technical solutions, their advantages, and disadvantages. By evaluating various defense methods, we identify the most suitable approach to address the primary challenge of “achieving a secure and robust FL system against malicious adversaries while protecting users' privacy.” We then propose a theoretical reference framework for end‐to‐end protection of privacy and security in FL for the key problem, which summarizes the attack surface of FL systems from the client to the server under the security model where the client and server are malicious. Leveraging the strengths and characteristics of existing schemes, our proposed framework integrates multiple techniques to strike a balance between privacy, usability, and efficiency. This framework serves as a valuable reference and provides insights for future work in the field. Finally, we also provide recommendations for future research directions in this field.

Binary Code Similarity Detection (BCSD), which calculates the similarity between binary code snippets, plays a vital role in various security fields. Since binary functions have complete semantics, the main research objects in BCSD are binary functions. Current approaches face challenges in effectively capturing the semantic information of assembly instructions and the structural information of control flow graphs (CFG) in binary functions. This paper proposes a graph attention network (GAT) for BCSD, called BSGAT, to detect similarity between binary functions. Our contribution is twofold: first, we propose a strategy to generate rich representations of basic blocks in CFG; second, we introduce GAT, which assigns different weights to basic blocks in CFG, enabling the generation of more discriminative embeddings for target binary functions. We conduct experiments on a binary function similarity detection task and a real vulnerability detection task. The results show that our proposed model BSGAT outperforms existing models in both tasks. In the binary function similarity detection task, BSGAT achieved the highest average AUC value of 0.872. In the real vulnerability detection task, BSGAT achieves the highest average recall@10 0.378, surpassing the best-performing model Gemini (0.337) in the comparison models, with a significant improvement of 12.2%. Our code is available at https://github.com/quchao777/BSGAT.git.

Coverage-based greybox fuzzing (CGF) has been widely studied and commonly used for software vulnerability detection. Existing CGF fuzzers fairly allocate execution time for each mutation operation to generate test cases. However, the fair-time-allocation strategy is revealed to be inefficient by our significant experimental observation that different operations have heterogeneous effectiveness on coverage. Those ineffective operations with vast test cases thus occupy the majority of limited runtime, reducing the opportunities for effective operations to explore more paths and find potential vulnerabilities.In this paper, we propose a novel operation-oriented time allocation strategy OTA, which dynamically allocates operation execution time in real time to cope with the effectiveness variation per operation. OTA has three distinguishing advantages: (1) the execution time per operation is novelly initialized on demand and program-dependent; (2) the execution time for each operation is dynamically weighted by its real-time effectiveness on exploring new coverage; (3) the determination of the execution time per operation is well controlled to achieve a quick convergence. Extensive experiments based on real-world programs and the LAVA-M dataset have been conducted to evaluate the path discovery and vulnerability detection abilities of OTA, which substantially outperforms 5 state-of-the-art fuzzers. In addition, OTA exposes 18 previously unknown vulnerabilities in 6 well-tested programs with 13 confirmed with new CVE IDs.

In cloud environments, group data sharing has become a hot topic in recent years. How to share data securely and efficiently in cloud environments is an urgent problem to be solved. Recently, an anonymous and traceable group data sharing scheme was proposed by Shen et al. to address this issue. They constructed their scheme using a group signature scheme as the building block. In this comment, we discuss the security of their group signature scheme and point out that it does not achieve the anonymity which they claimed and give a corresponding attack.

Coverage-based greybox fuzzing (CGF) is a common method utilizing coverage information to guide fuzzing. American Fuzzy Lop (AFL) is one of the most famous CGF fuzzers and has been used to uncover thousands of vulnerabilities in many software. However, AFL has two major drawbacks, which impedes it from boosting path discovery: (1) aggressively growing mutation overhead; (2) ineffective mutation region selection. In this paper, we propose three new approaches to overcome the drawbacks: (1) Interruptible mutation, which uses a hang monitor to avoid unnecessary mutation overhead; (2) Locality-based mutation, which utilizes mutation information in previous rounds to guide fuzzing useful regions in future rounds; (3) Hotspot-aware fuzzing, which exploits a pre-evaluation process to identify metadata and only mutates these regions. We combine these approaches into a tool named AFLTurbo based on AFL 2.52b. Furthermore, the effectiveness of AFLTurbo is evaluated in terms of both path discovery and bug detection on eight programs as well as LAVA-M with state-of-the-art fuzzers. The experimental results manifest that AFLTurbo can find 141%, 101% and 41% more paths, and reveal 14×, 30× and 5× more bugs than AFL, AFLFast and FairFuzz respectively. Additionally, AFLTurbo discovers 20 vulnerabilities, of which 18 are assigned with CVEs.

Abstract Recently, Tseng et al. proposed a new notion for identity-based signature (IBS) scheme to resist ephemeral secret leakage (ESL) attacks, called leakage-free identity-based signature (leakage-free IBS), and devised the first secure leakage-free IBS scheme. However, they only considered the situation of the leakage of ephemeral secrets used for generating the signatures. Notice that the private key extraction procedure is probabilistic as well in their scheme, that is, there are ephemeral secrets used by the key generation center to generate the signers’ private keys. It is practical to consider that if the adversary comprises these ephemeral secrets, then he can reveal the master key of the system. Therefore, it is desired to introduce a new security notion for the leakage-free IBS schemes to consider the ESL attacks on both private key extraction and signing procedures. In this paper, we present such security notion. Moreover, we propose two IBS schemes that are proved to be secure under the new security notion.

Abstract Rastegari et al. recently proposed a certificateless signcryption (CL-SC) scheme. They claimed that their scheme is the first secure CL-SC scheme, which captures the known session-specific temporary information security (KSSTIS), in the standard model. In this paper, we point out that their scheme is insecure, which implies that how to construct a secure CL-SC scheme with KSSTIS in the standard model is still an open problem.

External wireless interfaces and the lack of security design of controller area network (CAN) standards make it vulnerable to CAN-targeting attacks. Unfortunately, various defense solutions have been proposed merely to detect CAN intrusion attacks, while only a few works are devoted to intrusion source identification. Demonstrated by our experimental studies, the most advanced IDS with intrusion source identification, which is based on the physical feature fingerprints of the in-vehicle Electronic Control Units (ECUs), will fail when the temperature changes. In this paper, we innovatively propose temperature-varied fingerprinting, called TVF, for CAN intrusion detection and intrusion source identification. Motivated by the remarkable observation that the physical feature of an ECU, i.e., its clock offset, changes linearly with the temperature of ECUs, the concept of temperature-varied fingerprints is proposed. Then, for a severe intrusion case, we provide an advanced TVF for further supplemented and expanded. The proposed advanced temperature-varied fingerprinting is implemented, and extensive performance evaluation experiments are conducted in both CAN bus prototype and real vehicles. The experimental results illustrate the effectiveness and performance of advanced TVF.

The in-vehicle controller area network(CAN) provides reliable communications among ECUs, whereas the lack of security design of CAN protocols makes it vulnerable to CAN targeting attacks. Unfortunately, existing CAN intrusion detection systems merely recognize fabricated CAN messages while only little work are devoted to intrusion source identification. Demonstrated by our experimental study, the state-of-the-art source ECU identification approaches, which are based on physical ECU fingerprints, will fail when ECU temperature varies. In this paper, we innovatively propose temperature-varied fingerprinting, called TVF, for CAN intrusion detection and source ECU identification. Inspired by the significant observation that the clock offset of a specific ECU, i.e., its fingerprint, varies with the environment temperature of the ECU, the concept of temperature-varied ECU fingerprints are proposed and exploited to improve source identification accuracy in real-world vehicle CAN intrusion cases. The proposed temperature-varied fingerprinting is implemented and extensive performance evaluation experiments are conducted in both CAN bus prototype and real vehicles. The experimental results demonstrate the efficacy of the proposed TVF.

Abstract Recently, Zhang et al. proposed a new anonymous hierarchical identity-based encryption (anonymous HIBE) over prime order groups to achieve both constant size private key and constant size ciphertext. Moreover, a double exponent technique was used to provide anonymity. They proved that their scheme is secure and anonymous against chosen plaintext attacks in the standard model. In this paper, we point out that their scheme is insecure.

In general, the IEEE 802.11 network identifiers used by wireless access points (APs) can be easily spoofed. Accordingly, a malicious adversary is able to clone the identity information of a legitimate AP (LAP) to launch evil twin attacks (ETAs). The evil twin is a class of rogue access point (RAP) that masquerades as a LAP and allures Wi-Fi victims’ traffic. It enables an attacker with little effort and expenditure to eavesdrop or manipulate wireless communications. Due to the characteristics of strong concealment, high confusion, great harmfulness, and easy implementation, the ETA has become one of the most severe security threats in Wireless Local Area Networks (WLANs). Here, we propose a novel client-side approach, Speical Length Frames Arrival Time (SLFAT), to detect the ETA, which utilizes the same gateway as the LAP. By monitoring the traffic emitted by target APs at a detection node, SLFAT extracts the arrival time of the special frames with the same length to determine the evil twin’s forwarding behavior. SLFAT is passive, lightweight, efficient, hard to be escaped. It allows users to independently detect ETA on ordinary wireless devices. Through implementation and evaluation in our study, SLFAT achieves a very high detection rate in distinguishing evil twins from LAPs.

Certificateless signcryption (CLSC) has attracted much attention from the research community since it provides both confidentiality and unforgeability, and, at the same time, it does not suffer from the certificate management problem in traditional public key cryptography and the key escrow problem in identity-based cryptography. However, most CLSC schemes are based on the bilinear pairing which is still a time-costing operation although many efforts have been made to improve its efficiency. Recently, Yu et al. proposed a pairing-free CLSC scheme and proved its security. In this paper, we point out that their scheme can be totally broken since confidentiality and unforgeability actually are not captured.

Recently, a new framework, called secure server-designation public key encryption with keyword search (SPEKS), was proposed by Chen to withstand online keyword guessing attack. Moreover, Chen proposed a concrete scheme (IBE,TE)-2-SPEKS and proved that their scheme meets trapdoor indistinguishability. In this comment, we point out that the trapdoor, in fact, can be distinguished by the adversary.

Recently, Islam and Li proposed the first certificateless signcryption scheme without ephemeral secret leakage (ESL) attack, called leakage-free certificateless signcryption (leakage-free CLSC) scheme. However, we point out in this paper that the confidentiality property is not captured in their proposal. Moreover, our attack adheres to the security model proposed in the original paper. On the other hand, the security models proposed by Islam and Li are insufficient. In fact, the ESL attack is not involved in the security models since the ephemeral secret is not returned to the adversary when CLSC-Signcryption query and Challenge are issued. Finally, we give the amended security models.

This paper presents a distributed and robust time slot scheduling algorithm, which is suitable for underwater acoustic sensor network (UASN). The information of nodes' 2-hop neighbors is needed to be collected and then be used to calculate nodes' initial time slot by a distributed algorithm. A maximal independent set is formed by the nodes which were assigned with the same initial slot. Some theorems were proved to reveal that in an interference graph the size of this maximal independent set is at least of the size of the maximum independent set of the nodes. The simulation compares UD-TDMA with other three MAC protocols. The results show that the proposed protocol is effective in the UASN with random deployment, especially in high-density underwater acoustic sensor network.