Large language models (LLMs) are vulnerable to training data extraction attacks due to data memorization. This paper introduces a novel attack scenario wherein an attacker adversarially fine-tunes pre-trained LLMs to amplify the exposure of the original training data. Unlike prior TDE methods that mainly rely on post-hoc querying or prompt selection to elicit memorized content from a fixed model, our strategy directly alters the model’s parameters to intensify its retention of the pre-training dataset. To achieve this, the attacker needs to collect generated texts that are closely aligned with the pre-training data. However, without knowledge of the actual dataset, quantifying the amount of pre-training data within generated texts is challenging. To address this, we propose the use of pseudo-labels for these generated texts, leveraging membership approximations indicated by machine-generated probabilities from the target LLMusing DetectGPT. We subsequently fine-tune the LLM via reinforcement learning from human feedback (RLHF) to favor generations with higher likelihoods of originating from the pre-training data, based on these membership probabilities. Our empirical findings indicate a remarkable outcome: LLMs with over 1B parameters exhibit a four to eight-fold increase in training data exposure. We discuss potential mitigations and suggest future research directions.

Recent face anti-spoofing (FAS) methods have shown remarkable cross-domain performance by employing vision-language models like CLIP. However, existing CLIP-based FAS models do not fully exploit CLIP's patch embedding tokens, failing to detect critical spoofing clues. Moreover, these models rely on a single text prompt per class (e.g., 'live' or 'fake'), which limits generalization. To address these issues, we propose MVP-FAS, a novel framework incorporating two key modules: Multi-View Slot attention (MVS) and Multi-Text Patch Alignment (MTPA). Both modules utilize multiple paraphrased texts to generate generalized features and reduce dependence on domain-specific text. MVS extracts local detailed spatial features and global context from patch embeddings by leveraging diverse texts with multiple perspectives. MTPA aligns patches with multiple text representations to improve semantic robustness. Extensive experiments demonstrate that MVP-FAS achieves superior generalization performance, outperforming previous state-of-the-art methods on cross-domain datasets. Code: https://github.com/Elune001/MVP-FAS.

Large Language Models (LLMs) have rapidly advanced in reasoning capability and accessibility, driving their deployment across diverse applications. Yet this progress has also widened the surface for safety and security vulnerabilities. Adversaries can exploit prompt diversity, dialog memory, or multimodal inputs to induce unsafe or confidential outputs, while continual fine-tuning and third-party integration render static assurance infeasible. This paper introduces our ongoing national R&D project on developing the AutoPT Framework-an Autonomous Purple Teaming architecture that extends the collaborative principles of purple teaming toward self-adaptive, continuously verifiable LLM assurance. AutoPT unifies autonomous adversarial exploration and adaptive defensive reinforcement through two co-evolving agents. The red module, AutoPT-Red, employs coverage-guided fuzzing and internal measurement metrics to autonomously uncover vulnerabilities. The blue module, AutoPT-Blue, performs self-healing adaptation by updating guardrails and detecting integrity or confidentiality violations using embedding-based feedback. Preliminary case studies on jailbreak fuzzing and backdoor-poisoning defense validate the feasibility of this closed-loop, self-adapting architecture. As part of a broader national initiative, this work lays the conceptual and technical foundation for transitioning industrial purple teaming into a fully autonomous, scalable, and measurable assurance paradigm for generative AI systems.

Large Language Models (LLMs) are vulnerable to jailbreak attacks, where adversaries craft malicious prompts to bypass safety mechanisms and elicit harmful, illegal, or unethical responses. To ensure robustness before deployment, red-teaming LLMs is essential. However, prior methods like GCG and AutoDAN rely on loss functions that only assess whether the output matches a fixed target, offering little insight into how well the prompt circumvents refusal behaviors. We introduce Token Control Score (TCS), a novel metric that quantifies how effectively a prompt steers the LLM toward compliance and away from refusal. TCS compares the logits of key tokens representing compliant and rejecting responses, and can be extended with gradient-based feedback into a Normalized Token Control Score (NTCS) to guide optimization. Using this metric, we propose LLM-CGF, a fuzzing-based framework that iteratively discovers effective jailbreak templates for prompt injection. LLM-CGF leverages NTCS as the fitness function to explore LLM behaviors and uncover vulnerabilities with high efficiency. Experiments show that LLM-CGF outperforms state-of-the-art methods such as LLM-Fuzzer, generating more universal, transferable, and query-efficient jailbreak prompts. These results demonstrate the utility of TCS and NTCS as new objectives for prompt injection, providing deeper insights into LLM safety and enabling more thorough red-teaming evaluations. Warning: This paper contains unfiltered content generated by LLMs that may be offensive to readers.

The metaverse connects the virtual and real worlds, enabling users to interact as avatars across multiple devices, including smartphones, HMDs, and other devices. While multi-device access enhances convenience, it also expands attack surfaces, increasing security risks. Continuous authentication is crucial, but traditional methods like fuzzy extractors struggle with dynamic data, making reliable identification difficult. Moreover, conventional authentication focuses on user-side verification, failing to detect avatar manipulation attacks like avatar hijacking. This paper proposes a continuous authentication system that integrates user and avatar behavior data in multi-device environments. A transformer-based embedding model processes data on edge devices and securely transmits it via JSON Web Tokens (JWT). The authentication model binds user and avatar data in real-time to compute confidence scores and detect avatar manipulation. We implemented a VRSpace-based metaverse on NGINX and conducted simulations using open datasets—HMOG, Liebers, and BOXRR—to evaluate authentication accuracy and continuity. The Smartphone+HMD_6DOF+Avt_act+Window_(20) model achieved an average FAR of 0.0034%, an EER of 0.3386%, and an ADR of up to 97.67% for avatar manipulation detection. Based on our work, industry-driven research is expected to explore real-world applications, further validating our approach in evolving multi-device metaverse ecosystems.

In the Metaverse, continuous authentication is essential for verifying the ongoing connection between a user’s physical identity and avatar, ensuring secure access to various services. This process is crucial for confirming identities, maintaining security, and preventing unauthorized activities that could compromise legitimate services. However, traditional biometric-based authentication methods are susceptible to threats such as impersonation, replay attacks, and disguise, primarily due to the difficulty in directly using biometric information to represent the connection between virtual and physical identities. To address these challenges, some studies have proposed using blockchain schemes to mitigate security threats. Despite this, these approaches often encounter issues like insufficient network protection for authentication connections, prolonged data processing times, and latency. To overcome these limitations, we propose a secure continuous authentication framework that leverages standard protocols such as QUIC and JWT to verify user identities efficiently. Our approach employs embedding models on edge devices to generate and transmit biometric data. In contrast, a deep learning-based model on the server validates the user’s credentials, ensuring both high performance and availability. Experimental results show that our QUIC and JWT-based protocol delivers superior security and effectiveness compared to traditional biometric approaches and blockchain-based methods, achieving an AUC of 0.97, an EER of 3.77, and an F1 score of 0.96.

JavaScript interpreters, crucial for modern web browsers, require an effective fuzzing method to identify security-related bugs. However, the strict grammatical requirements for input present significant challenges. Recent efforts to integrate language models for context- aware mutation in fuzzing are promising but lack the necessary coverage guidance to be fully effective. This paper presents a novel technique called CovRL (Coverage-guided Reinforcement Learning) that combines Large Language Models (LLMs) with Reinforcement Learning (RL) from coverage feedback. Our fuzzer, CovRL-Fuzz, integrates coverage feedback directly into the LLM by leveraging the Term Frequency-Inverse Document Frequency (TF-IDF) method to construct a weighted coverage map. This map is key in calculating the fuzzing reward, which is then applied to the LLM-based mutator through reinforcement learning. CovRL-Fuzz, through this approach, enables the generation of test cases that are more likely to discover new coverage areas, thus improving bug detection while minimizing syntax and semantic errors, all without needing extra post-processing. Our evaluation results show that CovRL-Fuzz outperforms the state-of-the-art fuzzers in enhancing code coverage and identifying bugs in JavaScript interpreters: CovRL-Fuzz identified 58 real-world security-related bugs in the latest JavaScript interpreters, including 50 previously unknown bugs and 15 CVEs.

MagNet is a defense method that adopts autoencoders to detect and purify adversarial examples. Although MagNet is robust against grey-box and black-box attacks, it is vulnerable to white-box attacks. Despite this prior knowledge, the fundamental reason for and mitigation of the vulnerability of MagNet have not been discussed. We suggest that the challenge of MagNet is the generalization of the data manifold. To explain this, in this work, we leverage deep learning coverage for the reformer of MagNet. We mutate training images through image transformation algorithms and then train the reformer using mutants with new coverage information. The selected mutants provide an interesting data manifold, that cannot be handled by the random noise of MagNet, to the reformer. In grey-box settings, our defense method classified adversarial examples for various perturbation sizes much more accurately than MagNet even with the same architecture. Based on the preliminary result of this work, we consider future work to identify whether the generalization power of deep learning coverage is effective for stronger adversaries and different architectures.

FPGAs are field-programmable and reconfigurable integrated circuits; consequently, they entail numerous security concerns. For example, malicious functions such as hardware Trojans (HTs), can be inserted into the circuits in both development and deployment stages, as malicious fabrication and modification are possible even after deployment. Therefore, to detect HTs in FPGAs effectively, it is necessary to exploit both netlists available at the development stage and bitstreams available at deployment stage; this is in contrast with existing approaches, which require source code or gate-level netlists. In achieving this, we encounter two major challenges: effectively exploiting FPGA netlists closer to bitstreams for HTs detection and reverse-engineering bitstreams to netlists at an acceptable level. To address these problems, we develop a bidirectional mechanism for detecting HTs in FPGAs at any stage. To the best of our knowledge, this is the first study on bidirectional HT detection in FPGAs. To address the first challenge, we focus on LUT-level netlists; regarding the second challenge, we directly reverse-engineer bitstreams to LUT-level netlists. For HTs detection, we employ features extracted from LUT-level netlists, which can also be derived from reversed bitstreams and used to identify HTs. We design and implement our system for experimental studies. The experiments achieve a TPR of more than 99.3% and an FPR of less than 0.15% for 15 TrustHub benchmarks in forward and backward (reverse) directions for FPGA Virtex-5 devices.

Hybrid fuzzing, which combines fuzzing and concolic execution, is promising in light of the recent performance improvements in concolic engines. We have observed that there is room for further improvement: symbolic emulation is still slow, unnecessary constraints dominate solving time, resources are overly allocated, and hard-to-trigger bugs are missed. To address these problems, we present a new hybrid fuzzer named Intriguer. The key idea of Intriguer is field-level constraint solving, which optimizes symbolic execution with field-level knowledge. Intriguer performs instruction-level taint analysis and records execution traces without data transfer instructions like mov. Intriguer then reduces the execution traces for tainted instructions that accessed a wide range of input bytes, and infers input fields to build field transition trees. With these optimizations, Intriguer can efficiently perform symbolic emulation for more relevant instructions and invoke a solver for complicated constraints only. Our evaluation results indicate that Intriguer outperforms the state-of-the-art fuzzers: Intriguer found all the bugs in the LAVA-M(5h) benchmark dataset for ground truth performance, and also discovered 43 new security bugs in seven real-world programs. We reported the bugs and received 23 new CVEs.

The OS kernel, which has entire system privileges, is an attractive target of attackers. To reduce this threat, we need to find security bugs in the kernel prior to the attackers, and system call fuzzing is a widely used technique for this purpose. However, many system call fuzzers have not been evaluated for coverage performance which is an important indicator in fuzzing. In this poster, we propose a methodology to evaluate the code coverage performance of system call fuzzers with a strategy that combines virtualization and Intel Processor Trace (PT). First, we extract all the functions in the kernel that can be executed by system calls. Then we perform fuzzing with the target system call fuzzer on the guest OS, and record coverage information by leveraging the Intel PT. Finally, we evaluate system call fuzzers by comparing the list of functions related to system calls with the executed functions logged by Intel PT while fuzzing.

Deep neural networks (DNNs) gained in popularity as an effective machine learning algorithm, but their high complexity leads to the lack of model interpretability and difficulty in the verification of deep learning. Fuzzing, which is an automated software testing technique, is recently applied to DNNs as an effort to address these problems by following the trend of coverage-based fuzzing. However, new coverage metrics on DNNs may bring out the question of which layer to measure the coverage in DNNs. In this poster, we empirically evaluate the performance of existing coverage metrics. By the comparative analysis of experimental results, we compile the most effective layer for each of coverage metrics and discuss a future direction of DNN fuzzing.

User's behavioral biometrics are promising as authentication factors in particular if accuracy is sufficiently guaranteed. They can be used to augment security in combination with other authentication factors. A gesture-based pattern lock system is a good example of such multi-factor authentication, using touch dynamics in a smartphone. However, touch dynamics can be significantly affected by a shape of gestures with regard to the performance and accuracy, and our concern is that user-chosen patterns are likely far from producing such a good shape of gestures. In this poster, we raise this problem and show our experimental study conducted in this regard. We investigate if there is a reproducible correlation between shape and accuracy and if we can derive effective attribute values for user guidance, based on the gesture-based pattern lock system. In more general, we discuss a guided approach to behavioral authentication.

Since FPGAs are field-programmable and reconfigurable integrated circuits, there are many security concerns that malicious functions like hardware Trojans can be infiltrated into circuits not only in development stages but also in deployment stages -- malicious fabrication and modification are possible even after deployment. To detect hardware Trojans effectively, we must be able to deal with the netlists available at development stages and the bitstreams available at deployment stages -- it is highly desired to reverse-engineer the bitstreams to the netlists, but unfortunately greatly challenging. In this poster, we introduce our project aiming at hardware Trojans detection at both stages in FPGAs, and present our bitstream reverse engineering tool called BRET, recently developed for Xilinx Virtex-5 bitstreams. We also discuss the prospective results and directions.

A small touch sensor employed in smartphones can only capture a partial limited portion of the full fingerprint, and so it is more vulnerable to fingerprint spoofing attacks that leverage a user's firm impression. However, it is still unknown whether daily smudges remaining on the smartphone surface can be exploited to circumvent the small touch sensor. In this paper, we first study how to exploit the fingerprint smudges left on the smartphone surface in daily use, and present the so-called fingerprint SCRAP attack, which uses smudges remaining on the home button and touch screen to reconstruct an image of the enrolled fingerprint in good quality. We conduct an experimental study to show the actual risk regarding this attack. We collect 403 latent fingerprints from the smudges left on the touch screens (361) and home buttons (42) by seven users in six conditions (tapping, passcode-typing, text-typing, facebook, in-pocket, wiping). Using them, we perform our attack and evaluate the results in comparison with the firmly impressed fingerprints. The study results indicate that our attack is actual risk to the small touch sensors. We then investigate the user's touch behavior and perception gap. We conduct in-person surveys involving 82 participants, and ask about their touch behaviors and also their risk perception regarding the latent fingerprints. The survey results show that the fingers most frequently used on a touch screen and a home button are the same, and the user's risk perception is very low. We finally discuss mitigation methods and future directions.

Modern smartphones popularly adopt a small touch sensor for fingerprint identification of a user, but it captures only a partial limited portion of a fingerprint. Recently we have studied a gap between actual risk and user perception of latent fingerprints remaining on a smartphone, and developed a fake fingerprint attack that exploits the latent fingerprints as actual risk. We successfully reconstructed a fake fingerprint image in good quality for small touch sensors. In this paper, we subsequently conduct post hoc experimental studies on the facts that we have missed or have since learned. First of all, we examine that the presented attack is not conceptual but realistic. We employ the reconstructed image and make its fake fingerprint, using a conductive printing or a silicon-like glue, to pass directly the touch sensor of real smartphones. Our target smartphones are Samsung Galaxy S6, S7 and iPhone 5s, 6, 7. Indeed we have succeeded in passing Galaxy S6, S7, and now work on the remaining smartphones. We also conduct an experimental study for one of our mitigation methods to see how it can reduce actual risk. Finally, we perform a user survey study to understand user perception on the fake fingerprint attacks and the mitigation methods.

We coin a new term called \textit{data transfusion} as a phenomenon that a user experiences when pairing a wearable device with the host device. A large amount of data stored in the host device (e.g., a smartphone) is forcibly copied to the wearable device (e.g., a smart watch) due to pairing while the wearable device is usually less attended. To the best of knowledge, there is no previous work that manipulates how sensitive data is transfused even without user's consent and how users perceive and behave regarding such a phenomenon for smart watches. We tackle this problem by conducting an experimental study of data extraction from commodity devices, such as in Android Wear, watchOS, and Tizen platforms, and a following survey study with 205 smart watch users, in two folds. The experimental studies have shown that a large amount of sensitive data was transfused, but there was not enough user notification. The survey results have shown that users have lower perception on smart watches for security and privacy than smartphones, but they tend to set the same passcode on both devices when needed. Based on the results, we perform risk assessment and discuss possible mitigation that involves volatile transfusion.

In recent years, people can do most of their personal tasks, such as banking on smart devices like personal computers (PCs). Especially, Malware targeted at personal information stored on mobile are hard to detect and risks from usage patterns are even more difficult. Therefore, a means for easy recognition of the problems and the smartphone usage is necessary. In this paper, we present a personal visual analytics (PVA) system for Android security risk lifelog using app permissions to recognize the risk. Our system stores the security-related personal information on the smartphone device and utilizes it to analyze the security risk lifelog. For the risk analysis, we define security risk scores based on the app and permission statistics. Then, several linked visualizations are designed to present the risk lifelog. We have collected the security lifelog data from eight Android smartphone users and analyzed their security matters. Our PVA system enables Android smartphone users to observe, mitigate the security risk, and eventually understand how Android security risk affects their lives. Moreover, we present a user study to evaluate the PVA system with user feedback.

Users typically reuse the same personalized identification number (PIN) for multiple systems and in numerous sessions. Direct PIN entries are highly susceptible to shoulder-surfing attacks as attackers can effectively observe PIN entry with concealed cameras. Indirect PIN entry methods proposed as countermeasures are rarely deployed because they demand a heavier cognitive workload for users. To achieve security and usability, we present a practical indirect PIN entry method called SteganoPIN. The human-machine interface of SteganoPIN is two numeric keypads, one covered and the other open, designed to physically block shoulder-surfing attacks. After locating a long-term PIN in the more typical layout, through the covered permuted keypad, a user generates a one-time PIN that can safely be entered in plain view of attackers. Forty-eight participants were involved in investigating the PIN entry time and error rate of SteganoPIN. Our experimental manipulation used a within-subject factorial design with two independent variables: PIN entry system (standard PIN, SteganoPIN) and PIN type (system-chosen PIN, user-chosen PIN). The PIN entry time in SteganoPIN (5.4-5.7 s) was slower but acceptable, and the error rate (0-2.1%) was not significantly different from that of the standard PIN. SteganoPIN is resilient to camera-based shoulder-surfing attacks over multiple authentication sessions. It remains limited to PIN-based authentication.

Industrial wireless sensor networks (IWSNs) are advancing to a form of active networks called industrial wireless active sensor networks (IWASNs) with reprogrammable sensor nodes, and thus require a method to check the integrity of software in sensor nodes. Regarding energy efficiency, however, previous methods did not take into account the scalability of IWASNs and thus performed inefficiently. In this paper, we propose FlexiCast, which presents a novel energy-efficient method to check the integrity of software objects. Sensor nodes can efficiently detect a modification in software objects sent by a base station or stored in neighboring nodes through authenticated fingerprints and network-wide attestation. Our analysis shows that FlexiCast can reduce energy consumption by 71% for both updating software objects and checking modifications regarding more critical attacks.

Devising a user authentication scheme based on personal identification numbers (PINs) that is both secure and practically usable is a challenging problem. The greatest difficulty lies with the susceptibility of the PIN entry process to direct observational attacks, such as human shoulder-surfing and camera-based recording. This paper starts with an examination of a previous attempt at solving the PIN entry problem, which was based on an elegant adaptive black-and-white coloring of the 10-digit keypad in the standard layout. Even though the method required uncomfortably many user inputs, it had the merit of being easy to understand and use. Our analysis that takes both the experimental and theoretical approaches reveals multiple serious shortcomings of the previous method, including round redundancy, unbalanced key presses, highly frequent system errors, and insufficient resilience to recording attacks. The lessons learned through our analysis are then used to improve the black-and-white PIN entry scheme. The new scheme has the remarkable property of resisting camera-based recording attacks over an unlimited number of authentication sessions without leaking any of the PIN digits.

When a user interacts with a computing system to enter a secret password, shoulder surfing attacks are of great concern. To cope with this problem, previous methods presumed limited cognitive capabilities of a human adversary as a deterrent, but there was a pitfall with the assumption. In this paper, we show that human adversaries, even without a recording device, can be more effective at eavesdropping than expected, in particular by employing cognitive strategies and by training themselves. Our novel approach called covert attentional shoulder surfing indeed can break the well known PIN entry method previously evaluated to be secure against shoulder surfing. Another contribution in this paper is the formal modeling approach by adapting the predictive human performance modeling tool for security analysis and improvement. We also devise a defense technique in the modeling paradigm to deteriorate severely the perceptual performance of the adversaries while preserving that of the user. To the best of our knowledge, this is the first work to model and defend the new form of attack through human performance modeling. Real attack experiments and user studies are also conducted.

Light field is a large set of spatially correlated images of the same static scene captured using a 2D array of closely spaced cameras. Interactive light field streaming is the application where a client continuously requests successive light field images along a view trajectory of his choosing, and in response the server transmits appropriate data for the client to correctly reconstruct desired images. The technical challenge is how to encode captured light field images into a reasonably sized frame structure a priori (without knowing eventual clients' view trajectories), so that at stream time, expected server transmission rate can be minimized, while satisfying client's view-switch requests. In this paper, using I-frames, redundant P-frames and distributed source coding (DSC) frames as building blocks, we design coding structures to optimally trade off storage size of the frame structure with expected server transmission rate. The key novelty is to facilitate the use of “landmarks” in the structure-popular reference frames cached in the decoder buffer-so that the probability of having at least one useful predictor frame available in the buffer for disparity compensation is greatly increased. We first derive recursive equations to find the optimal caching strategy for a given coding structure. We then formulate the structure design problem as a Lagrangian minimization, and propose fast heuristics to find near-optimal solutions. Experimental results show that the expected server streaming rate can be reduced by up to 93.6% compared to an I-frame-only structure, at twice the storage required.

Light field is a large set of spatially correlated images of the same static scene captured using a 2D array of closely spaced cameras. Interactive light field streaming is the application where a client continuously requests successive light field images along a view trajectory of her choosing, and in response the server transmits appropriate data for the client to correctly reconstruct desired images. The technical challenge is how to encode captured light field images into a reasonably sized frame structure a priori (without knowing eventual clients' view trajectories), so that during streaming session, expected server transmission rate can be minimized, while satisfying client's view requests. In this paper, we design efficient frame structures, using I-frames, redundant P-frames and distributed source coding (DSC) frames as building blocks, to optimally trade off storage size of the frame structure with expected server transmission rate. The key novelty is to optimize structures in such a way that decoded images in caches of neighboring cooperative peers, connected together via a secondary network such as ad hoc WLAN for content sharing, can be reused to further decrease the server-to-client transmission rate. We formulate the structure design problem as a Lagrangian minimization, and propose fast heuristics to find near-optimal solutions. Experimental results show that the expected server streaming rate can be reduced by up to 83% compared to an I-frame-only structure, at less than twice the storage required.

We propose a rate adaptation scheme for visual MIMO camera-based communications, wherein parallel data transmissions from light emitting arrays are received by multiple receive elements of a CCD/CMOS camera image sensor. Unlike RF MIMO, multipath fading is negligible in the visual MIMO channel. Instead, the channel is largely dependent on receiver perspective (distance and angle) and visibility issues (partial line-of-sight availability and occlusions). This allows for slower adaptation but requires the adaptation algorithm to choose among a more complex set of modes. In this paper, we define a set of operating modes for visual MIMO transmitters and propose a rate adaptation scheme to switch between these modes. Our Visual MIMO Rate Adaptation (VMRA) is a packet based rate adaptation protocol that bases its rate selection decisions on the packet error rate feedback. Using trace-based simulation results for a vehicle-to-vehicle communication scenario, we illustrate how our VMRA algorithms can adapt over distance as well as visibility variations in an optical link and achieve a higher average throughput.

We propose a new method to speed up discrete logarithm (DL)-based cryptosystems by considering a new variant of the DL problem, where the exponents are formed as e1+ e2for some fixed a and two integers e1, ae2with a low weight representation. We call this class of exponents split exponents, and we show that with certain choice of parameters the DL problem on split exponents is essentially as secure as the standard DL problem, while the exponentiation operation using exponents of this class is significantly faster than best exponentiation algorithms given for standard exponents. For example, the speed of scalar multiplication on the standard Koblitz curve K163 is estimated to be accelerated by up to 51.5 % and 23.5 % at the cost of memory for one precomputed point, compared to the TNAF and window TNAF methods, respectively. As for security, we show that the provable security of the DL problem using split exponents is only by a small constant, e.g., 1/4, worse than the security of the standard DL problem. Split exponents can be adopted to speed up various DL-based cryptosystems. We exemplify this on the recent CCA-secure public key encryption of Bellare, Kohno, and Shoup.

Authenticated broadcast, enabling a base station to send commands and requests to low-powered sensor nodes in an authentic manner, is one of the core challenges for securing wireless sensor networks. μTESLA and its multilevel variants based on delayed exposure of one-way chains are well known valuable broadcast authentication schemes, but concerns still remain for their practical application. To use these schemes on resource-limited sensor nodes, a 64-bit key chain is desirable for efficiency, but care must be taken. We will first show, by both theoretical analysis and rigorous experiments on real sensor nodes, that if μTESLA is implemented in a raw form with 64-bit key chains, some of the future keys can be discovered through time-memory-data-tradeoff techniques. We will then present an extendable broadcast authentication scheme called X-TESLA, as a new member of the TESLA family, to remedy the fact that previous schemes do not consider problems arising from sleep modes, network failures, idle sessions, as well as the time-memory-data tradeoff risk, and to reduce their high cost of countering DoS attacks. In X-TESLA, two levels of chains that have distinct intervals and cross-authenticate each other are used. This allows the short key chains to continue indefinitely and makes new interesting strategies and management methods possible, significantly reducing unnecessary computation and buffer occupation, and leads to efficient solutions to the raised problems.

Wireless industrial sensor networks are necessary for industrial applications, so that wireless sensor nodes sense around themselves and detect anomaly events in the harsh industrial environments. Due to the harshness, anomaly events such as adversarial intrusions may result in harmful and disastrous situations for industrial applications but it is difficult to detect them over wireless medium. Intrusion detection is an essential requirement for security, but as far as we know, there have not been such studies for wireless industrial sensor networks in the literature. The previous intrusion detection methods proposed for wireless sensor networks consider networks rather in general senses and restrict capabilities to specific attacks only. In this paper, we first study intrusion detection for wireless industrial sensor networks, through various experiments and design of a hierarchical framework. We classify and select better methodologies against various intrusions. Subsequently, we find novel results on the previous methodologies. We also propose a new hierarchical framework for intrusion detection as well as data processing. Throughout the experiments on the proposed framework, we stress the significance of one-hop clustering, which was neglected in the previous studies. Finally, we construct required logical protocols in the hierarchical framework; hierarchical intrusion detection and prevention protocols.

A practical pairwise key distribution scheme is necessary for wireless sensor networks since sensor nodes are susceptible to physical capture and constrained in their resources. In this paper, we investigate a simple and practical scheme that achieves higher connectivities and perfect resilience with less resources, even in case of deployment errors.

We propose an authentication methodology that combines multimodal biometrics and cryptographic mechanisms for border control applications. We accommodate faces and fingerprints without a mandatory requirement of (tamper-resistant) smart-card-level devices on e-passports for easier deployment. It is even allowable to imprint (publicly readable) bar codes on the passports. Additionally, we present a solution based on the certification and key management method to control the validity of passports within the current Public-Key Infrastructure (PKI) technology paradigm.

We propose a new authentication and key distribution protocol which is adaptable and reliable for communication networks. The secrets for authentication, which are chosen from a relatively small space by common users, are easy to guess. Our protocol gives a solution to protect the weak secrets from guessing attacks. Compared with other related work, our protocol is more reliable because it is resistant to various kinds of attacks including guessing attacks, and more adaptable because it reduces several overheads which make the existing protocols more expensive. We show how to apply our protocol to the Q.931 calling sequences and to the World Wide Web model.

KT-EDI, an EDI system based on X.435, has been developed jointly by Korea Telecom and ETRI (Electronics and Telecommunications Research Institute) in Korea. We describe the design of the security management system for KT-EDI. We specified the requirements and functions of security management for KT-EDI on the basis of X.800 and other standards. By the above specifications, we designed the security management system for KT-EDI and the prototype is being developed.

Cryptographic protocols for authentication and key exchange are necessary for secure communications. Most protocols have assumed that a strong secret for authentication should be shared between communicating participants in the light of a threat of dictionary attacks. But a user-chosen weak secret, i.e. password, is typically used for authentication. Since most users want to use an easily memorizable password, which tends to be easy to guess, several authentication protocols that protect such a weak secret from password guessing attacks, have been developed. However, those security-oriented protocols are more expensive in terms of the number of random numbers, cipher operations, and protocol steps than the previous protocols which are not resistant to guessing attacks. The authors propose new authentication and key exchange protocols, which are efficient considerably in protecting a poorly-chosen weak secret from guessing attacks.

We design a feature interaction manager (FIM) used in the AIN switching system. By analyzing the interactions between features that have been the major obstacle of introducing the AIN service, it is shown that most of the interactions are caused by sharing limited resources, and on the basis of this knowledge, an interaction testing and handling logic is designed. Compared with other methods, the FIM has advantages such as a simple process logic, small memory space requirements, and runtime processing, and it is independent of new features. The FIM is designed using Z schema. This paper also shows the testing and handling process of interactions using the set relation of Z schema.