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cs.CR: explore 594 source-linked works published from 2022 to 2026, with original documents and citations.

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Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

System-Level Isolation for Mixed-Criticality RISC-V SoCs: A "World" Reality Check

As RISC-V adoption accelerates, domains such as automotive, the Internet of Things (IoT), and industrial control are attracting growing attention. These domains are subject to stringent Size, Weight, Power, and Cost (SWaP-C) constraints, which have driven a shift toward heterogeneous Systems-on-Chip (SoCs) integrating general-purpose CPUs, tightly coupled accelerators, and diverse I/O devices with different integrity levels. While such integration improves cost efficiency and performance, it introduces a fundamental safety and security challenge: enforcing system-level isolation in mixed-criticality environments. Although RISC-V International has proposed several hardware isolation primitives, including RISC-V Worlds, IOPMP, and SmMTT, their interoperability, scalability, and suitability for real-time systems remain insufficiently understood. In this paper, we present a comparative analysis of these primitives from the perspective of practical heterogeneous SoC designs. We implement an IOPMP, a World-based checker, and a modified RISC-V World checker that addresses key limitations of the baseline specification, and evaluate their trade-offs in terms of security guarantees and power-performance-area (PPA). Our results show that the World-based checker introduces a fixed, configuration-independent access latency, achieving lower worst-case delay than the evaluated alternatives while scaling predictably with system size. At the macro level, we estimate that the proposed modifications reduce SoC area by up to approximately 5% compared to a baseline design. All artifacts will be released as open source, and we expect these findings to directly contribute to the evolution and ratification of RISC-V specifications, as well as to the design of future RISC-V SoCs.

cs.CR↗

RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance

Deep neural networks are highly susceptible to backdoor attacks, yet most defense methods to date rely on balanced data, overlooking the pervasive class imbalance in real-world scenarios that can amplify backdoor threats. This paper presents the first in-depth investigation of how the dataset imbalance amplifies backdoor vulnerability, showing that (i) the imbalance induces a majority-class bias that increases susceptibility and (ii) conventional defenses degrade significantly as the imbalance grows. To address this, we propose Randomized Probability Perturbation (RPP), a certified poisoned-sample detection framework that operates in a black-box setting using only model output probabilities. For any inspected sample, RPP determines whether the input has been backdoor-manipulated, while offering provable within-domain detectability guarantees and a probabilistic upper bound on the false positive rate. Extensive experiments on five benchmarks (MNIST, SVHN, CIFAR-10, TinyImageNet and ImageNet10) covering 10 backdoor attacks and 12 baseline defenses show that RPP achieves significantly higher detection accuracy than state-of-the-art defenses, particularly under dataset imbalance. RPP establishes a theoretical and practical foundation for defending against backdoor attacks in real-world environments with imbalanced data.

cs.CR↗

Empirical Assessment of the Code Comprehension Effort Needed to Attack Programs Protected with Obfuscation

Evaluating the effectiveness of software protection is crucial for selecting the most effective methods to safeguard assets within software applications. Obfuscation involves techniques that deliberately modify software to make it more challenging to understand and reverse-engineer, while maintaining its original functionality. Although obfuscation is widely adopted, its effectiveness remains largely unexplored and unthoroughly evaluated. This paper presents a controlled experiment involving Master's students performing code comprehension tasks on applications hardened with obfuscation. The experiment's goals are to assess the effectiveness of obfuscation in delaying code comprehension by attackers and to determine whether complexity metrics can accurately predict the impact of these protections on success rates and durations of code comprehension tasks. The study is the first to evaluate the effect of layering multiple obfuscation techniques on a single piece of protected code. It also provides experimental evidence of the correlation between objective metrics of the attacked code and the likelihood of a successful attack, bridging the gap between objective and subjective approaches to estimating potency. Finally, the paper highlights significant aspects that warrant additional analysis and opens new avenues for further experiments.

cs.CR↗

ForgeDAN: An Evolutionary Framework for Jailbreaking Aligned Large Language Models

The rapid adoption of large language models (LLMs) has brought both transformative applications and new security risks, including jailbreak attacks that bypass alignment safeguards to elicit harmful outputs. Existing automated jailbreak generation approaches e.g. AutoDAN, suffer from limited mutation diversity, shallow fitness evaluation, and fragile keyword-based detection. To address these limitations, we propose ForgeDAN, a novel evolutionary framework for generating semantically coherent and highly effective adversarial prompts against aligned LLMs. First, ForgeDAN introduces multi-strategy textual perturbations across \textit{character, word, and sentence-level} operations to enhance attack diversity; then we employ interpretable semantic fitness evaluation based on a text similarity model to guide the evolutionary process toward semantically relevant and harmful outputs; finally, ForgeDAN integrates dual-dimensional jailbreak judgment, leveraging an LLM-based classifier to jointly assess model compliance and output harmfulness, thereby reducing false positives and improving detection effectiveness. Our evaluation demonstrates ForgeDAN achieves high jailbreaking success rates while maintaining naturalness and stealth, outperforming existing SOTA solutions.

cs.CR↗

Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift

With the growing volume of Internet of Things (IoT) network traffic, machine learning (ML)-based anomaly detection is more relevant than ever. Traditional batch learning models face challenges such as high maintenance and poor adaptability to rapid anomaly changes, known as concept drift. In contrast, streaming learning integrates online and incremental learning, enabling seamless updates and concept drift detection to improve robustness. This study investigates anomaly detection in streaming IoT traffic as binary classification, comparing batch and streaming learning approaches while assessing the limitations of current IoT traffic datasets. We simulated heterogeneous network data streams by carefully mixing existing datasets and streaming the samples one by one. Our results highlight the failure of batch models to handle concept drift, but also reveal persisting limitations of current datasets to expose model limitations due to low traffic heterogeneity. We also investigated the competitiveness of tree-based ML algorithms, well-known in batch anomaly detection, and compared it to non-tree-based ones, confirming the advantages of the former. Adaptive Random Forest achieved F1-score of 0.990 $\pm$ 0.006 at one-third the computational cost of its batch counterpart. Hoeffding Adaptive Tree reached F1-score of 0.910 $\pm$ 0.007, reducing computational cost by four times, making it a viable choice for online applications despite a slight trade-off in stability.

cs.LG↗

Permutation polynomials over finite fields from low-degree rational functions

This paper considers permutation polynomials over the finite field $F_{q^2}$ in even characteristic by utilizing low-degree permutation rational functions over $F_q$. As a result, we obtain two classes of permutation binomials and six classes of permutation pentanomials over $F_{q^2}$. Additionally, we show that the obtained binomials and pentanomials are quasi-multiplicative inequivalent to the known ones in the literature.

cs.CR↗

SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis

Despite growing attention to deepfake speech detection, the aspects of bias and fairness remain underexplored in the speech domain. To address this gap, we introduce the Speaker Characteristics Deepfake (SCDF) dataset: a novel, richly annotated resource enabling systematic evaluation of demographic biases in deepfake speech detection. SCDF contains over 237,000 utterances in a balanced representation of both male and female speakers spanning five languages and a wide age range. We evaluate several state-of-the-art detectors and show that speaker characteristics significantly influence detection performance, revealing disparities across sex, language, age, and synthesizer type. These findings highlight the need for bias-aware development and provide a foundation for building non-discriminatory deepfake detection systems aligned with ethical and regulatory standards.

cs.SD↗

Secure Goal-Oriented Communication: Defending against Eavesdropping Timing Attacks

Goal-oriented Communication (GoC) is a new paradigm that plans data transmission to occur only when it is instrumental for the receiver to achieve a certain goal. This leads to the advantage of reducing the frequency of transmissions significantly while maintaining adherence to the receiver's objectives. However, GoC scheduling also opens a timing-based side channel that an eavesdropper can exploit to obtain information about the state of the system. This type of attack sidesteps even information-theoretic security, as it exploits the timing of updates rather than their content. In this work, we study such an eavesdropping attack against pull-based goal-oriented scheduling for remote monitoring and control of Markov processes. We provide a theoretical framework for defining the effectiveness of the attack and propose possible countermeasures, including two practical heuristics that provide a balance between the performance gains offered by GoC and the amount of leaked information. Our results show that, while a naive goal-oriented scheduler allows the eavesdropper to correctly guess the system state about 60% of the time, our heuristic defenses can halve the leakage with a marginal reduction of the benefits of goal-oriented approaches.

cs.CR↗

Security Science (SecSci), Basic Concepts and Mathematical Foundations

This textbook compiles the lecture notes from security courses taught at Oxford in the 2000s, at Royal Holloway in the 2010s, and currently in Hawaii. The early chapters are suitable for a first course in security. The middle chapters have been used in advanced courses. Towards the end there are also some research problems.

cs.CR↗

Eavesdropping on Goal-Oriented Communication: Timing Attacks and Countermeasures

Goal-oriented communication is a new paradigm that considers the meaning of transmitted information to optimize communication. One possible application is the remote monitoring of a process under communication costs: scheduling updates based on goal-oriented considerations can significantly reduce transmission frequency while maintaining high-quality tracking performance. However, goal-oriented scheduling also opens a timing-based side-channel that an eavesdropper may exploit to obtain information about the state of the remote process, even if the content of updates is perfectly secure. In this work, we study an eavesdropping attack against pull-based goal-oriented scheduling for the tracking of remote Markov processes. We provide a theoretical framework for defining the effectiveness of the attack and of possible countermeasures, as well as a practical heuristic that can provide a balance between the performance gains offered by goal-oriented communication and the information leakage.

eess.SY↗

Trace of the Times: Rootkit Detection through Temporal Anomalies in Kernel Activity

Kernel rootkits provide adversaries with permanent high-privileged access to compromised systems and are often a key element of sophisticated attack chains. At the same time, they enable stealthy operation and are thus difficult to detect. Thereby, they inject code into kernel functions to appear invisible to users, for example, by manipulating file enumerations. Existing detection approaches are insufficient, because they rely on signatures that are unable to detect novel rootkits or require domain knowledge about the rootkits to be detected. To overcome this challenge, our approach leverages the fact that runtimes of kernel functions targeted by rootkits increase when additional code is executed. The framework outlined in this paper injects probes into the kernel to measure time stamps of functions within relevant system calls, computes distributions of function execution times, and uses statistical tests to detect time shifts. The evaluation of our open-source implementation on publicly available data sets indicates high detection accuracy with an F1 score of 98.7\% across five scenarios with varying system states.

cs.CR↗

The revised boomerang connectivity tables and their connection to the Difference Distribution Table

It is well-known that functions over finite fields play a crucial role in designing substitution boxes (S-boxes) in modern block ciphers. In order to analyze the security of an S-box, recently, three new tables have been introduced: the Extended Boomerang Connectivity Table (EBCT), the Lower Boomerang Connectivity Table (LBCT), and the Upper Boomerang Connectivity Table (UBCT). In fact, these tables offer improved methods over the usual Boomerang Connectivity Table (BCT) for analyzing the security of S-boxes against boomerang-style attacks. Here, we put in context these new EBCT, LBCT, and UBCT concepts by connecting them to the DDT for a differentially $δ$-uniform function and also determine the EBCT, LBCT, and UBCT entries of three classes of differentially $4$-uniform power permutations, namely, Gold, Kasami and Bracken-Leander. We also determine the Double Boomerang Connectivity Table (DBCT) entries of the Gold function. As byproducts of our approach, we obtain some previously published results quite easily.

cs.CR↗

Red Team Redemption: A Structured Comparison of Open-Source Tools for Adversary Emulation

Red teams simulate adversaries and conduct sophisticated attacks against defenders without informing them about used tactics in advance. These interactive cyber exercises are highly beneficial to assess and improve the security posture of organizations, detect vulnerabilities, and train employees. Unfortunately, they are also time-consuming and expensive, which often limits their scale or prevents them entirely. To address this situation, adversary emulation tools partially automate attacker behavior and enable fast, continuous, and repeatable security testing even when involved personnel lacks red teaming experience. Currently, a wide range of tools designed for specific use-cases and requirements exist. To obtain an overview of these solutions, we conduct a review and structured comparison of nine open-source adversary emulation tools. To this end, we assemble a questionnaire with 80 questions addressing relevant aspects, including setup, support, documentation, usability, and technical features. In addition, we conduct a user study with domain experts to investigate the importance of these aspects for distinct user roles. Based on the evaluation and user feedback, we rank the tools and find MITRE Caldera, Metasploit, and Atomic Red Team on top.

cs.CR↗

DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks

Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these attacks work by flipping bits in the memory where quantized model parameters are stored. In this paper, we introduce an encoding-based protection method against bit-flip attacks on neural networks, titled DeepNcode. We experimentally evaluate our proposal with several publicly available models and datasets, by using state-of-the-art bit-flip attacks: BFA, T-BFA, and TA-LBF. Our results show an increase in protection margin of up to $7.6\times$ for $4-$bit and $12.4\times$ for $8-$bit quantized networks. Memory overheads start at $50\%$ of the original network size, while the time overheads are negligible. Moreover, DeepNcode does not require retraining and does not change the original accuracy of the model.

cs.CR↗

HOACS: Homomorphic Obfuscation Assisted Concealing of Secrets to Thwart Trojan Attacks in COTS Processor

Commercial-off-the-shelf (COTS) components are often preferred over custom Integrated Circuits (ICs) to achieve reduced system development time and cost, easy adoption of new technologies, and replaceability. Unfortunately, the integration of COTS components introduces serious security concerns. None of the entities in the COTS IC supply chain are trusted from a consumer's perspective, leading to a ''zero trust'' threat model. Any of these entities could introduce hidden malicious circuits or hardware Trojans within the component, allowing an attacker in the field to extract secret information (e.g., cryptographic keys) or cause a functional failure. Existing solutions to counter hardware Trojans are inapplicable in such a zero-trust scenario as they assume either the design house or the foundry to be trusted and consider the design to be available for either analysis or modification. In this work, we have proposed a software-oriented countermeasure to ensure the confidentiality of secret assets against hardware Trojans that can be seamlessly integrated in existing COTS microprocessors. The proposed solution does not require any supply chain entity to be trusted and does not require analysis or modification of the IC design. To protect secret assets in an untrusted microprocessor, the proposed method leverages the concept of residue number coding (RNC) to transform the software functions operating on the asset to be fully homomorphic. We have implemented the proposed solution to protect the secret key within the Advanced Encryption Standard (AES) program and presented a detailed security analysis. We also have developed a plugin for the LLVM compiler toolchain that automatically integrates the solution in AES. Finally, we compare the execution time overhead of the operations in the RNC-based technique with comparable homomorphic solutions and demonstrate significant improvement.

cs.CR↗

The second-order zero differential spectra of some APN and other maps over finite fields

The Feistel Boomerang Connectivity Table and the related notion of $F$-Boomerang uniformity (also known as the second-order zero differential uniformity) has been recently introduced by Boukerrou et al.~\cite{Bouk}. These tools shall provide a major impetus in the analysis of the security of the Feistel network-based ciphers. In the same paper, a characterization of almost perfect nonlinear functions (APN) over fields of even characteristic in terms of second-order zero differential uniformity was also given. Here, we find a sufficient condition for an odd or even function over fields of odd characteristic to be an APN function, in terms of second-order zero differential uniformity. Moreover, we compute the second-order zero differential spectra of several APN or other low differential uniform functions, and show that our considered functions also have low second-order zero differential uniformity, though it may vary widely, unlike the case for even characteristic when it is always zero.

cs.IT↗

An End-To-End Encrypted Cache System with Time-Dependent Access Control

Due to the increasing use of encrypted communication, such as Transport Layer Security (TLS), encrypted cache systems are a promising approach for providing communication efficiency and privacy. Cache-22 is an encrypted cache system (Emura et al. ISITA 2020) that makes it possible to significantly reduce communication between a cache server and a service provider. In the final procedure of Cache-22, the service provider sends the corresponding decryption key to the user via TLS and this procedure allows the service provider to control which users can access the contents. For example, if a user has downloaded ciphertexts of several episodes of a show, the service provider can decide to provide some of the contents (e.g., the first episode) available for free while requiring a fee for the remaining contents. However, no concrete access control method has been implemented in the original Cache-22 system. In this paper, we add a scalable access control protocol to Cache-22. Specifically, we propose a time-dependent access control that requires a communication cost of $O(\log T_{\sf max})$ where $T_{\sf max}$ is the maximum time period. Although the protocol is stateful, we can provide time-dependent access control with scalability at the expense of this key management. We present experimental results and demonstrate that the modified system is effective for controlling access rights. We also observe a relationship between cache capacity and network traffic because the number of duplicated contents is higher than that in the original Cache-22 system, due to time-dependent access control.

cs.CR↗

Cyber-Forensic Review of Human Footprint and Gait for Personal Identification

The human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore, it is very crucial to recovering the footprints from identifying the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time the world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defence, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints quickly. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

cs.CV↗
Compare source metadata on this page
WorkPublishedSource identifierSource
System-Level Isolation for Mixed-Criticality RISC-V SoCs: A "World" Reality Check2026-02-042602.05002arxiv
RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance2026-10-092602.00183arxiv
Empirical Assessment of the Code Comprehension Effort Needed to Attack Programs Protected with Obfuscation2025-11-262511.21301arxiv
ForgeDAN: An Evolutionary Framework for Jailbreaking Aligned Large Language Models2025-11-172511.13548arxiv
Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift2025-10-312510.27304arxiv
Permutation polynomials over finite fields from low-degree rational functions2025-08-212503.20982arxiv
SCDF: A Speaker Characteristics DeepFake Speech Dataset for Bias Analysis2025-08-112508.07944arxiv
Secure Goal-Oriented Communication: Defending against Eavesdropping Timing Attacks2025-07-152507.14212arxiv
Security Science (SecSci), Basic Concepts and Mathematical Foundations2025-04-232504.16617arxiv
Eavesdropping on Goal-Oriented Communication: Timing Attacks and Countermeasures2025-03-062411.07088arxiv
Trace of the Times: Rootkit Detection through Temporal Anomalies in Kernel Activity2025-03-042503.02402arxiv
The revised boomerang connectivity tables and their connection to the Difference Distribution Table2025-01-272407.12617arxiv
Red Team Redemption: A Structured Comparison of Open-Source Tools for Adversary Emulation2024-08-282408.15645arxiv
DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks2024-06-022405.13891arxiv
HOACS: Homomorphic Obfuscation Assisted Concealing of Secrets to Thwart Trojan Attacks in COTS Processor2024-02-152402.09701arxiv
The second-order zero differential spectra of some APN and other maps over finite fields2023-10-202310.13775arxiv
An End-To-End Encrypted Cache System with Time-Dependent Access Control2023-02-222302.11292arxiv
Cyber-Forensic Review of Human Footprint and Gait for Personal Identification2022-04-202204.09344arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.