8778 matches found
On Anti-Collusion Codes for Averaging Attack in Multimedia Fingerprinting
Multimedia fingerprinting is a technique to protect the copyrighted contents against being illegally redistributed under various collusion attack models. Averaging attack is the most fair choice for each colluder to avoid detection, and also makes the pirate copy have better perceptional quality...
PAM Environment Variable Injection
PAM pamenv.so module allows environment variable injection via /.pamenvironment leading to privilege escalation through SystemD session manipulation. This scripts gauges exploitability...
KD-GAT: Combining Knowledge Distillation and Graph Attention Transformer for a Controller Area Network Intrusion Detection System
The Controller Area Network CAN protocol is widely adopted for in-vehicle communication but lacks inherent security mechanisms, making it vulnerable to cyberattacks. This paper introduces KD-GAT, an intrusion detection framework that combines Graph Attention Networks GATs with knowledge...
An Improved ChaCha Algorithm Based on Quantum Random Number
Due to the merits of high efficiency and strong security against timing and side-channel attacks, ChaCha has been widely applied in real-time communication and data streaming scenarios. However, with the rapid development of AI-assisted cryptanalysis and quantum computing technologies, there are...
LoRA-Leak: Membership Inference Attacks against LoRA Fine-Tuned Language Models
Language Models LMs typically adhere to a "pre-training and fine-tuning" paradigm, where a universal pre-trained model can be fine-tuned to cater to various specialized domains. Low-Rank Adaptation LoRA has gained the most widespread use in LM fine-tuning due to its lightweight computational cost...
PRACtical: Subarray-Level Counter Update and Bank-Level Recovery Isolation for Efficient PRAC Rowhammer Mitigation
As DRAM density increases, Rowhammer becomes more severe due to heightened charge leakage, reducing the number of activations needed to induce bit flips. The DDR5 standard addresses this threat with in-DRAM per-row activation counters PRAC and the Alert Back-Off ABO signal to trigger mitigation...
Assessment of Quantitative Cyber-Physical Reliability of SCADA Systems in Autonomous Vehicle to Grid (V2G) Capable Smart Grids
The integration of electric vehicles EVs into power grids via Vehicle-to-Grid V2G system technology is increasing day by day, but these phenomena present both advantages and disadvantages. V2G can increase grid reliability by providing distributed energy storage and ancillary services. However, o...
Scout: Leveraging Large Language Models for Rapid Digital Evidence Discovery
Recent technological advancements and the prevalence of technology in day to day activities have caused a major increase in the likelihood of the involvement of digital evidence in more and more legal investigations. Consumer-grade hardware is growing more powerful, with expanding memory and...
Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable PE files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra...
Secure One-Sided Device-Independent Quantum Key Distribution under Collective Attacks with Enhanced Robustness
We study the security of a quantum key distribution QKD protocol under the one-sided device-independent 1sDI setting, which assumes trust in only one party's measurement device. This approach effectively provides a balance between the experimental viability of device-dependent DD-QKD and the...
Information Security Based on LLM Approaches: a Review
Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models LLMs have shown a broad application prospect in the field of...
Thermal-Aware 3D Design for Side-Channel Information Leakage
Side-channel attacks are important security challenges as they reveal sensitive information about on-chip activities. Among such attacks, the thermal side-channel has been shown to disclose the activities of key functional blocks and even encryption keys. This paper proposes a novel approach to...
Regression-Aware Continual Learning for Android Malware Detection
Malware evolves rapidly, forcing machine learning ML-based detectors to adapt continuously. With antivirus vendors processing hundreds of thousands of new samples daily, datasets can grow to billions of examples, making full retraining impractical. Continual learning CL has emerged as a scalable...
Unmasking Synthetic Realities in Generative AI: a Comprehensive Review of Adversarially Robust Deepfake Detection Systems
The rapid advancement of Generative Artificial Intelligence has fueled deepfake proliferation-synthetic media encompassing fully generated content and subtly edited authentic material-posing challenges to digital security, misinformation mitigation, and identity preservation. This systematic revi...
Auto-SGCR: Automated Generation of Smart Grid Cyber Range Using IEC 61850 Standard Models
Digitalization of power grids have made them increasingly susceptible to cyber-attacks in the past decade. Iterative cybersecurity testing is indispensable to counter emerging attack vectors and to ensure dependability of critical infrastructure. Furthermore, these can be used to evaluate...
Exploring the Jupyter Ecosystem: an Empirical Study of Bugs and Vulnerabilities
Background. Jupyter notebooks are one of the main tools used by data scientists. Notebooks include features configuration scripts, markdown, images, etc. that make them challenging to analyze compared to traditional software. As a result, existing software engineering models, tools, and studies d...
Learning to Locate: GNN-Powered Vulnerability Path Discovery in Open Source Code
Detecting security vulnerabilities in open-source software is a critical task that is highly regarded in the related research communities. Several approaches have been proposed in the literature for detecting vulnerable codes and identifying the classes of vulnerabilities. However, there is still...
Removing Box-Free Watermarks for Image-To-Image Models Via Query-Based Reverse Engineering
The intellectual property of deep generative networks GNets can be protected using a cascaded hiding network HNet which embeds watermarks or marks into GNet outputs, known as box-free watermarking. Although both GNet and HNet are encapsulated in a black box called operation network, or ONet, with...
Enabling Cyber Security Education through Digital Twins and Generative AI
Digital Twins DTs are gaining prominence in cybersecurity for their ability to replicate complex IT Information Technology, OT Operational Technology, and IoT Internet of Things infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how...
An Empirical Study on Virtual Reality Software Security Weaknesses
Virtual Reality VR has emerged as a transformative technology across industries, yet its security weaknesses, including vulnerabilities, are underinvestigated. This study investigates 334 VR projects hosted on GitHub, examining 1,681 software security weaknesses to understand: what types of...
Restricted Boltzmann Machine As a Probabilistic Enigma
We theoretically propose a symmetric encryption scheme based on Restricted Boltzmann Machines that functions as a probabilistic Enigma device, encoding information in the marginal distributions of visible states while utilizing bias permutations as cryptographic keys. Theoretical analysis reveals...
C-AAE: Compressively Anonymizing Autoencoders for Privacy-Preserving Activity Recognition in Healthcare Sensor Streams
Wearable accelerometers and gyroscopes encode fine-grained behavioural signatures that can be exploited to re-identify users, making privacy protection essential for healthcare applications. We introduce C-AAE, a compressive anonymizing autoencoder that marries an Anonymizing AutoEncoder AAE with...
Towards Unifying Quantitative Security Benchmarking for Multi Agent Systems
Evolving AI systems increasingly deploy multi-agent architectures where autonomous agents collaborate, share information, and delegate tasks through developing protocols. This connectivity, while powerful, introduces novel security risks. One such risk is a cascading risk: a breach in one agent c...
Tab-MIA: a Benchmark Dataset for Membership Inference Attacks on Tabular Data in LLMs
Large language models LLMs are increasingly trained on tabular data, which, unlike unstructured text, often contains personally identifiable information PII in a highly structured and explicit format. As a result, privacy risks arise, since sensitive records can be inadvertently retained by the...
MeAJOR Corpus: a Multi-Source Dataset for Phishing Email Detection
Phishing emails continue to pose a significant threat to cybersecurity by exploiting human vulnerabilities through deceptive content and malicious payloads. While Machine Learning ML models are effective at detecting phishing threats, their performance largely relies on the quality and diversity ...
form-data Insufficient Randomness
form-data uses Math.random to select a boundary value for multipart form-encoded data. This can lead to a security issue if an attacker can observe other values produced by Math.random in the target application and can control one field of a request made using form-data...
PyPitfall: Dependency Chaos and Software Supply Chain Vulnerabilities in Python
Python software development heavily relies on third-party packages. Direct and transitive dependencies create a labyrinth of software supply chains. While it is convenient to reuse code, vulnerabilities within these dependency chains can propagate through dependencies, potentially affecting...
CHAMP: a Configurable, Hot-Swappable Edge Architecture for Adaptive Biometric Tasks
What if you could piece together your own custom biometrics and AI analysis system, a bit like LEGO blocks? We aim to bring that technology to field operators in the field who require flexible, high-performance edge AI system that can be adapted on a moment's notice. This paper introduces CHAMP...
Joint Resource Optimization over Licensed and Unlicensed Spectrum in Spectrum Sharing UAV Networks against Jamming Attacks
Unmanned aerial vehicle UAV communication is of crucial importance in realizing heterogeneous practical wireless application scenarios. However, the densely populated users and diverse services with high data rate demands has triggered an increasing scarcity of UAV spectrum utilization. To tackle...
LLM Meets the Sky: Heuristic Multi-Agent Reinforcement Learning for Secure Heterogeneous UAV Networks
This work tackles the physical layer security PLS problem of maximizing the secrecy rate in heterogeneous UAV networks HetUAVNs under propulsion energy constraints. Unlike prior studies that assume uniform UAV capabilities or overlook energy-security trade-offs, we consider a realistic scenario...
Rethinking HSM and TPM Security in the Cloud: Real-World Attacks and Next-Gen Defenses
As organizations rapidly migrate to the cloud, the security of cryptographic key management has become a growing concern. Hardware Security Modules HSMs and Trusted Platform Modules TPMs, traditionally seen as the gold standard for securing encryption keys and digital trust, are increasingly...
WaveVerify: a Novel Audio Watermarking Framework for Media Authentication and Combatting Deepfakes
The rapid advancement of voice generation technologies has enabled the synthesis of speech that is perceptually indistinguishable from genuine human voices. While these innovations facilitate beneficial applications such as personalized text-to-speech systems and voice preservation, they have als...
CASCADE: LLM-Powered JavaScript Deobfuscator at Google
Software obfuscation, particularly prevalent in JavaScript, hinders code comprehension and analysis, posing significant challenges to software testing, static analysis, and malware detection. This paper introduces CASCADE, a novel hybrid approach that integrates the advanced coding capabilities o...
Quantum Software Security Challenges within Shared Quantum Computing Environments
The number of qubits in quantum computers keeps growing, but most quantum programs remain relatively small because of the noisy nature of the underlying quantum hardware. This might lead quantum cloud providers to explore increased hardware utilization, and thus profitability through means such a...
Leveraging Trustworthy AI for Automotive Security in Multi-Domain Operations: Towards a Responsive Human-AI Multi-Domain Task Force for Cyber Social Security
Multi-Domain Operations MDOs emphasize cross-domain defense against complex and synergistic threats, with civilian infrastructures like smart cities and Connected Autonomous Vehicles CAVs emerging as primary targets. As dual-use assets, CAVs are vulnerable to Multi-Surface Threats MSTs,...
Learning-Based Privacy-Preserving Graph Publishing against Sensitive Link Inference Attacks
Publishing graph data is widely desired to enable a variety of structural analyses and downstream tasks. However, it also potentially poses severe privacy leakage, as attackers may leverage the released graph data to launch attacks and precisely infer private information such as the existence of...
Performance Evaluation and Threat Mitigation in Large-Scale 5G Core Deployment
The deployment of large-scale software-based 5G core functions presents significant challenges due to their reliance on optimized and intelligent resource provisioning for their services. Many studies have focused on analyzing the impact of resource allocation for complex deployments using...
Quantifying the ROI of Cyber Threat Intelligence: a Data-Driven Approach
The valuation of Cyber Threat Intelligence CTI remains a persistent challenge due to the problem of negative evidence: successful threat prevention results in non-events that generate minimal observable financial impact, making CTI expenditures difficult to justify within traditional cost-benefit...
On One-Shot Signatures, Quantum Vs Classical Binding, and Obfuscating Permutations
One-shot signatures OSS were defined by Amos, Georgiou, Kiayias, and Zhandry STOC'20. These allow for signing exactly one message, after which the signing key self-destructs, preventing a second message from ever being signed. While such an object is impossible classically, Amos et al observe tha...
NIST Post-Quantum Cryptography Standard Algorithms Based on Quantum Random Number Generators
In recent years, the advancement of quantum computing technology has posed potential security threats to RSA cryptography and elliptic curve cryptography. In response, the National Institute of Standards and Technology NIST published several Federal Information Processing Standards FIPS of...
Trusted Data Fusion, Multi-Agent Autonomy, Autonomous Vehicles
Multi-agent collaboration enhances situational awareness in intelligence, surveillance, and reconnaissance ISR missions. Ad hoc networks of unmanned aerial vehicles UAVs allow for real-time data sharing, but they face security challenges due to their decentralized nature, making them vulnerable t...
Development of a Standardized Testing Environment for QRNGs Based on Semiconductor Laser Phase Noise
Quantum random number generators QRNGs based on semiconductor laser phase noise are an inexpensive and efficient resource for true random numbers. Commercially available technology allows for designing QRNG setups tailored to specific use cases. However, it is important to constantly monitor...
DREAM: Scalable Red Teaming for Text-To-Image Generative Systems Via Distribution Modeling
Despite the integration of safety alignment and external filters, text-to-image T2I generative models are still susceptible to producing harmful content, such as sexual or violent imagery. This raises serious concerns about unintended exposure and potential misuse. Red teaming, which aims to...
Explainable Vulnerability Detection in C/C++ Using Edge-Aware Graph Attention Networks
Detecting security vulnerabilities in source code remains challenging, particularly due to class imbalance in real-world datasets where vulnerable functions are under-represented. Existing learning-based methods often optimise for recall, leading to high false positive rates and reduced usability...
When LLMs Copy to Think: Uncovering Copy-Guided Attacks in Reasoning LLMs
Large Language Models LLMs have become integral to automated code analysis, enabling tasks such as vulnerability detection and code comprehension. However, their integration introduces novel attack surfaces. In this paper, we identify and investigate a new class of prompt-based attacks, termed...
Analysis of Post-Quantum Cryptography in User Equipment in 5G and Beyond
The advent of quantum computing threatens the security of classical public-key cryptographic systems, prompting the transition to post-quantum cryptography PQC. While PQC has been analyzed in theory, its performance in practical wireless communication environments remains underexplored. This pape...
From Cracks to Crooks: YouTube As a Vector for Malware Distribution
With billions of users and an immense volume of daily uploads, YouTube has become an attractive target for cybercriminals aiming to leverage its vast audience. The platform's openness and trustworthiness provide an ideal environment for deceptive campaigns that can operate under the radar of...
Secure Wireless Communication Via Polarforming
Polarforming is a promising technique that enables dynamic adjustment of antenna polarization to mitigate depolarization effects commonly encountered during electromagnetic EM wave propagation. In this letter, we investigate the polarforming design for secure wireless communication systems, where...
Analysis of Threat-Based Manipulation in Large Language Models: a Dual Perspective on Vulnerabilities and Performance Enhancement Opportunities
Large Language Models LLMs demonstrate complex responses to threat-based manipulations, revealing both vulnerabilities and unexpected performance enhancement opportunities. This study presents a comprehensive analysis of 3,390 experimental responses from three major LLMs Claude, GPT-4, Gemini...