Photo Response Non-Uniformity (PRNU) noise serves as a sensor-level fingerprint in camera-based authentication and source attribution systems. Rather than degrading or suppressing PRNU patterns as in prior work, we introduce a novel transfer attack that injects PRNU noise from one device into images from another source or generated by AI. This enables manipulated images to falsely pass forensic source verification checks, posing a new class of threat to PRNU-based authentication. Our method achieves an average 85.5
IoT malware is often created by modifying publicly available source code, resulting in numerous variants. Analyzing the functionality of these variants has become increasingly important. Function Call Sequence Graph (FCSG) have been proposed to represent internal function calls in binaries, offering a promising approach for functional analysis. However, the structure of FCSGs is highly sensitive to CPU architecture and compiler optimization, hindering cross-architecture analysis. In this paper, we propose a method for generating architecture-independent FCSGs by removing obstructive functions—such as initialization routines and architecture-specific functions—that are not called in the source code but appear in conventional FCSGs. Our method removes, on average, 97.4
Research on Android malware has progressed rapidly, yet the task of distinguishing malicious from benign applications continues to test the limits of automated analysis. Earlier work, dominated by static signature matching, frequently struggles when novel or obfuscated samples appear. Contemporary graph–based pipelines alleviate some of these shortcomings by modelling control and data dependencies, but their reliance on pairwise relations often blurs higher–order interactions that experienced adversaries nurture when crafting evasive variants. These observations motivate a return to first principles: we require representations that faithfully encode behaviours without incurring prohibitive overhead. In this study we revisit the problem through the lens of hypergraph representation learning. Treating an application as a hypergraph allows one to encode joint behaviours—such as the co-invocation of critical API calls inside a single execution context—that cannot be decomposed into simple edges without information loss. Building on this representation, we introduce HGANN-Mal, a Hypergraph Attention Neural Network that adaptively emphasises semantically salient hyperedges while softening the influence of spurious ones. The model derives its signals from static analysis to extract structural and semantic features. Importantly, on the Drebin dataset, HGANN-Mal achieves a Macro-F1 score of 97.8
Bootkits threaten the very foundation of system security. By exploiting vulnerabilities in firmware and bootloaders, these attacks gain persistent, stealthy control at the earliest stages of boot. Despite widespread adoption of UEFI Secure Boot and TPM-based measurements, limited visibility and complexity during early boot allow bootkits to evade detection. Ongoing discovery of critical firmware flaws enables privilege escalation and circumvention of core protections. Existing static verification methods cannot detect runtime modifications during early boot, highlighting the need for runtime-aware integrity monitoring. In this paper, we present BootMarker, a runtime integrity monitoring framework built on a dual-layered architecture that combines Driver Execution Environment (DXE) and System Management Mode (SMM) instrumentation. BootMarker dynamically enforces control-flow integrity in the bootloader and performs cryptographic validation of firmware components in real time. It detects and mitigates bootkit attacks as they occur during early execution. Our evaluation shows that BootMarker reliably identifies diverse bootkit behaviors while imposing minimal performance overhead, making it practical for real-world deployment and significantly enhancing boot-time security.
With the rapid development of blockchain technology, vulnerabilities in smart contracts have become a major threat to asset security. Traditional rule-based detection methods, although interpretable, often suffer from high false positive rates and limited scalability. Despite recent progress, deep learning methods are often limited to unimodal approaches and lack the capability for fine-grained analysis. Our analysis of 14 common vulnerabilities revealed that function-level granularity strikes the optimal balance between detection accuracy and efficiency. Building on this observation, we propose the Jakiro method, which improves detection accuracy by integrating the semantic information from control flow graphs (CFGs) and source code using cross-modal contrastive learning. Experiments conducted on a dataset of more than 38,000 real-world contracts demonstrate that Jakiro surpasses the majority of the 10 baseline methods across three tasks: reentry, integer overflow, and transaction order dependency, achieving average improvements of 6.49
In this work, we introduce CryptNyx, a Password Hardening (PH) framework that enhances the security of stored password records through collaboration between the authentication server and an external server, also known as rater. PH mitigates offline password brute force attacks on stolen databases by involving the rater in the password verification process, enabling it to impose limits on password decryption attempts. However, this means that the remote server can track user login requests, raising concerns about potential compromises to user privacy. Consequently, achieving effective rate-limiting while preserving user anonymity has remained an unresolved challenge. CryptNyx ensures anonymity without sacrificing rate-limiting. Essentially, the user pseudonym, which allows the rater to track login requests, can be refreshed any number of times in a controlled but unlinkable manner, offering complete anonymity while still mitigating offline guessing attempts. Furthermore, CryptNyx allows for password-hardened encryption capabilities, which enable users to securely encrypt sensitive data using their strengthened password records. Additional features include an Opt-out protocol that facilitates client withdrawal, and an Anonymous Opt-in protocol designed for efficient batch registration. Experimental results demonstrate the effectiveness and practicality of our approach, highlighting the balance between user privacy, security, and system functionality.
Continuous authentication helps mitigate the risk of session hijacking, insider attack, and privilege abuse. Applying zero trust principles, this paper proposes a family of four formally specified access control models to account for the use of continuous authentication to monitor user access patterns in user-facing software applications, each model providing increasingly expressive user modeling capabilities. We name these models Zero Trust Continuous Authentication (ZTCA). Deploying a ZTCA model requires the authoring of policies. To ease the challenge of developing ZTCA policies, we studied the problem of automatically generating ZTCA policies from declarative usability and security requirements. We devised a novel SAT encoding for the automated policy formulation problem, so that policy formulation can be performed by state-of-the-art SAT solvers. Empirical experiments demonstrate that our novel encoding approach runs significantly faster than a competing encoding approach previously published in the literature.
Fuzzy Extractors (FEs) and Fuzzy Signatures (FSs) are promising primitives for template-protected biometric authentication, and lattice-based constructions of them are known. In this paper, to reveal lattices more suitable for FEs/FSs in terms of application to biometric authentication, we evaluate the accuracy of FEs/FSs for various lattices, along with the computation time of finding the closest lattice vector CV_L(· ) required in the authentication process when FEs/FSs are applied to biometric authentication. Specifically, we treat the integer lattice ℤ^n , a triangular lattice L_n^(tri) , and the direct product E_8^n/8 of the Gosset lattice, which have been treated in conventional studies on FEs/FSs, and additionally the dual lattice L_n^(d-tri) of a triangular lattice and the checkerboard lattice D_n . To evaluate the accuracy of FEs/FSs with these lattices, we give algorithms for computing the lattice norm for L_n^(d-tri) , D_n , and E_8^n/8 , where the lattice norm can be utilized for efficient accuracy evaluation and algorithms for ℤ^n and L_n^(tri) are known. Then, we evaluate the accuracy of FEs/FSs based on these lattices utilizing the lattice norm. Although L_n^(tri) is often used for FEs and FSs conventionally, the evaluation results show that E_8^n/8 achieves the highest accuracy of the evaluated lattices, and D_n achieves accuracy close to L_n^(tri) with shorter computation time of CV_L(· ) . Also, to obtain the lattice norm for L_n^(d-tri) , we give a similarity transformation from a non-full-rank lattice to a full-rank one, which transforms the zero-sum root lattice A_n and its dual A_n^* to L_n^(tri) and L_n^(d-tri) , respectively. Using this transformation, we discuss a relation between L_n^(tri) , often used for FEs/FSs, and A_n , a well-studied lattice in lattice theory.
Conflict-based side-channel attacks allow attackers to monitor victims access patterns by asserting malicious cache conflicts. While cache randomization has emerged as a potential defense, existing solutions face critical limitations. CEASER-S and DT4+EV10 fail to fully prevent existing eviction set searching algorithms. MIRAGE suffers from intolerable area and power overheads. Chameleons relocation mechanism faces the problem of excessive power/energy consumption. To alleviate these limitations, we employ a dual-mapping randomized cache with global indirect replacement (GIR-Cache). A randomized direct-mapped look up table is designed to eliminate dual-index checking overhead by maintaining the active mapping state of each LLC address. Our approach effectively mitigates conflict-based side-channel attacks while incurs negligible runtime performance impact with moderate area and power overhead.
The original Fuzzy Vault scheme is inherently restricted to codes based on polynomial evaluations, in particular Reed–Solomon codes. This structural dependency limits its applicability to a narrow class of error-correcting codes and constrains possible generalizations. In this work, we reformulate the scheme within the framework of generic linear codes, detaching the construction from its polynomial structure. We define locking and unlocking procedures compatible with any linear code that meets a set of explicit conditions, which we identify and justify. This reformulation makes it possible to explore the use of alternative codes that satisfy these conditions, and we detail how Reed–Solomon codes fit into this framework. It also clarifies the internal organization of the scheme and its compatibility with different code families. In addition, we propose a method for embedding the protected secret inside the vault structure, thereby removing the need for external storage and ensuring that all recovery elements remain encapsulated within the scheme itself. In this variant, the error vector contains values obtained by applying a cryptographically secure one-way function to a randomly chosen secret, making them indistinguishable from random noise.
This paper presents BlockLens, a supervised, trace-level framework for detecting malicious Ethereum transactions using large language models (LLMs). Unlike prior approaches limited to static features or storage-level abstractions, BlockLens processes complete execution traces, capturing opcode sequences, memory information, gas usage, and call structures to accurately represent the runtime behavior of each transaction. This framework harnesses the exceptional reasoning capabilities of LLMs for long input sequences and is fine-tuned on transaction data. We design a tokenization strategy aligned with Ethereum Virtual Machine (EVM) semantics, mapping execution traces into interpretable tokens. Each transaction captures its complete execution trace through simulated execution and is then sliced into overlapping chunks using a sliding window, allowing for long-range context modeling within memory constraints. During inference, the model outputs both a binary decision and a probability score indicating the likelihood of malicious behavior. We implement the framework based on LLaMA 3.2-1B backbone and fine-tune the model using Low-Rank Adaptation (LoRA). We evaluate it on a curated dataset containing both real-world attacks and normal DeFi transactions. BlockLens outperforms representative baselines, achieving higher F1 scores and recall at top-k thresholds than representative baselines. Additionally, BlockLens offers interpretable chunk-level outputs by localizing suspicious trace segments that enhance explainability, facilitating rapid forensic analysis and actionable decision-making in security-critical environments.
The rise of online social networks has heightened concerns over image privacy leakage. Although deep learning methods have been applied to privacy recognition, they face two key challenges: (1) a privacy gap between low-level visual features and high-level, context-aware human judgments, and (2) limited consideration of inter-entity context. To address these, we propose MSPP-Net, a Multi-Stage Privacy Perception Network inspired by human cognition. It decomposes privacy inference into three stages: entity perception to detect key objects, attribute perception to align visual features with semantic concepts via multimodal contrastive learning, and privacy perception to model inter-object context using graph attention networks. Experiments on our FineViP dataset show that MSPP-Net outperforms strong baselines, improving mAP by 3
In dynamic group signature schemes (GSS), forward security ensures that newly joined members cannot generate valid signatures for past time periods. Additionally, non-frameability prevents even privileged entities, such as the group manager or key issuer, from falsely attributing signatures to honest users. Most GSS either lack non-frameability or face significant efficiency challenges when updating signing keys to ensure forward security. In this paper, we introduce a forward-secure dynamic group signature scheme that guarantees non-frameability. We also present an alternative scheme that, while lacking non-frameability, offers higher efficiency compared to existing schemes with comparable security. For both protocols, we propose efficient revocation mechanisms that allow an authority to revoke users without requiring re-registering existing users. Additionally, we propose a technique that enables the verification process of both protocols to be performed in batches. We prove the security of our schemes, ensuring the standard dynamic GSS security notions; anonymity, traceability and non-frameability (second scheme). Experimental results demonstrate that our schemes are competitive in both computational and communication efficiency when compared to existing literature.
Stateful Coverage-Based Greybox Fuzzing (SCGF) is a key technique for securing stateful network protocols. To efficiently process feedback and guide mutations, these fuzzers predominantly employ scheduling strategies based on simple short-term heuristics. However, the reliance on myopic heuristics, which fail to adopt a global, long-term optimization perspective, results in inefficient state-space exploration and a struggle to uncover vulnerabilities requiring deep and complex state transitions. To address this issue, we present EvoFuzz, an adaptive scheduling framework that applies Evolutionary Game Theory (EGT). EvoFuzz operates through two core modules: EvoState and EvoSeed. The EvoState module treats states as competing players to guide global exploration, while the EvoSeed module treats candidate seeds as competing players, selecting the most promising one within a target state. We implemented EvoFuzz on top of NSFuzz and evaluated it on five real-world protocols. The results indicate that compared to the state-of-the-art baselines, EvoFuzz increases the unique state sequence by up to 205.56
In this paper, we construct the first lattice-based threshold ring signature scheme with signature size scaling logarithmically in the size of the ring while supporting arbitrary thresholds. Our construction is also concretely efficient, achieving signature sizes of less than 150 kB for ring sizes up to N = 4096 (with threshold size T = N{2, say). This is substantially more compact than previous work. Our approach is inspired by the recent work of Aardal et al. (CRYPTO 2024) on the compact aggregation of Falcon signatures, that uses the LaBRADOR lattice-based SNARKs to combine a collection of Falcon signatures into a single succinct argument of knowledge of those signatures. We proceed in a similar way to obtain compact threshold ring signatures from Falcon, but crucially require that the proof system be zero-knowledge in order to ensure the privacy of signers. Since LaBRADOR is not a zkSNARK, we associate it with a separate (non-succinct) lattice-based zero-knowledge proof system to achieve our desired properties.
With the increasing deployment of encrypted protocols such as TLS, traditional deep packet inspection techniques have become ineffective, posing challenges to traffic classification. In this paper, we propose SimSeq, a robust TLS traffic classification method that relies solely on packet length sequences. To simulate real-world network conditions, we design two perturbation scenarios that emulate fast retransmission and timeout retransmission behaviors. Each scenario is configured with multiple packet loss rates, where smaller rates represent mild congestion and larger rates reflect more congested network conditions. We generate perturbation views of packet sequences using reliable transmission logic and leverage a contrastive learning framework to learn robust and discriminative representations. The encoder, composed of a BiLSTM and attention pooling module, is pretrained with a SimCLR-style contrastive loss and then finetuned with scenario-specific classification heads. Experimental results on the CESNET-TLS22 dataset show that SimSeq achieves strong and stable performance under both scenarios, with average F1-scores of 0.88 and 0.93, respectively.
Differential-linear cryptanalysis is a well-known cryptanalytic method combining differential and linear cryptanalysis. Since its introduction, it has become one of the most important tools for analyzing block ciphers. This paper focuses on differential-linear key-recovery attacks and presents a more efficient key-recovery algorithm by incorporating the partial-sum technique. This algorithm enables the key recovery attack to be divided into multiple steps, and the time complexity of a differential-linear key-recovery attack can be significantly reduced by carefully treating each step. Using this algorithm, we propose the first 19-round differential-linear key-recovery attacks on the message processing phase of GIFT-COFB and HyENA, which are currently the best-known attacks against these ciphers. Additionally, we extend the differential-linear attack on GIFT-64 to 19 rounds, surpassing the previous differential-linear attack by one round. We note that the attack results in this paper are far from threatening the security of GIFT-COFB, HyENA, and GIFT-64.
Side-channel attacks are critical as they, despite the mathematical security of the algorithm, break the security assumption that private data stays hidden from the adversary. Developing secure hardware can be expensive, as multiple iterations of prototyping may be required to achieve a satisfactory level of security against side-channel attacks. Currently, the fairly new and open-source CPU-platform RISC-V is gaining traction by entering the Internet of Things (IoT)- and consumer market and also gains interest in security oriented projects such as OpenTitan. In case of security-critical applications, especially when the hardware is exposed to third party, the implementations of cryptographic algorithms must be secure against side-channel attacks. For the RISC-V platform currently only a small number of tools exist to assess the probing security. Further, we could identify a lack of simulation-based tooling to do so, with the ability to analyze larger implementations as e.g., full ciphers. To address this demand, we use PROLEAD_SW as a starting point and extend it to support the RISC-V platform. By analyzing micro-architectural leakage effects on the RISC-V platform we show that the CPU-independent leakage model used by PROLEAD_SW for the ARM architecture is suitable for the RISC-V platform. To verify the correctness of the new tooling, test-vectors are executed with the new tooling. In a final step, the performance of the new tooling is compared to the performance of the original version of PROLEAD_SW by analyzing two masked AES C implementations with both tools.
The Internet of Things (IoT) devices are increasingly exploited as intermediaries for launching sophisticated cyberattacks. IoT honeypots have emerged as a proactive measure to lure attackers and provide early threat detection. However, existing honeypots exhibit significant limitations, including low interaction levels, vulnerability to fingerprinting, and constrained data collection capabilities. This paper introduces HoneySentry, a high-interaction IoT honeypot specifically designed to overcome these challenges and target advanced attackers adept at sophisticated honeypot fingerprinting and strategic selection of victim IoT devices. HoneySentry utilizes a custom-enhanced IoT firmware emulation framework to achieve high-fidelity emulation of diverse IoT devices and architectures. It incorporates advanced anti-fingerprinting techniques to evade detection, modifying commands frequently used by attackers during reconnaissance. Additionally, HoneySentry enhances its appeal to attackers by deploying a variety of meticulously crafted bait files and processes. To facilitate detailed analysis, HoneySentry captures comprehensive attack data, including both network traffic and host-level activities. Comparative evaluations against traditional honeypots and real-world deployments demonstrate that HoneySentry significantly outperforms existing solutions in fostering deep engagement with attackers, collecting extensive attack data, and enabling comprehensive threat analysis. During its two-month deployment (August 2024 to November 2024), HoneySentry captured over 200,000 requests and generated 61.3 GB of log data. Further analysis revealed variants of known malicious worms and viruses, as well as several intriguing attack behaviors, highlighting its capability to uncover diverse threats.
GCM with Secure Short Tag (GCM-SST) is a variant of the GCM authenticated encryption mode designed for improved security with short tags, and its standardization is ongoing in various organizations, including 3GPP and IETF. The original design specification was published with informal security claims only, and Inoue et al. then verified them with formal security proofs. They proved that the term regarding tag length t is v/2^t in GCM-SST (cf. v ℓ/2^t in GCM), wherein v is the number of decryption queries and the maximum message block length ℓ . However, the proofs were given in the single-user (su) setting only, and its multi-user (mu) security remained an open research problem, which is a significant gap because GCM-SST’s specification document explicitly considers mu use cases and even recommends nonce randomization (NR) for improving mu-security. Addressing this issue, this paper proves mu-security of GCM-SST, verifying that the security with short tags stays intact under the mu setting. Moreover, by combining GCM-SST with NR and nonce-based key derivation (NKD), we show that those enhancement methods improve mu-security in the same level as those combined with GCM.
