Enhancing IoT Intrusion Detection Systems through Adversarial Training

The augmentation of Internet of Things IoT devices transformed both automation and connectivity but revealed major security vulnerabilities in networks. We address these challenges by designing a robust intrusion detection system IDS to detect complex attacks by learning patterns from the...

Jailbreaking Large Language Diffusion Models: Revealing Hidden Safety Flaws in Diffusion-Based Text Generation

Large Language Diffusion Models LLDMs exhibit comparable performance to LLMs while offering distinct advantages in inference speed and mathematical reasoning tasks.The precise and rapid generation capabilities of LLDMs amplify concerns of harmful generations, while existing jailbreak methodologie...

OneShield -- the Next Generation of LLM Guardrails

The rise of Large Language Models has created a general excitement about the great potential for a myriad of applications. While LLMs offer many possibilities, questions about safety, privacy, and ethics have emerged, and all the key actors are working to address these issues with protective...

Generating Adversarial Point Clouds Using Diffusion Model

Adversarial attack methods for 3D point cloud classification reveal the vulnerabilities of point cloud recognition models. This vulnerability could lead to safety risks in critical applications that use deep learning models, such as autonomous vehicles. To uncover the deficiencies of these models...

Transcript Franking for Encrypted Messaging

Message franking is an indispensable abuse mitigation tool for end-to-end encrypted E2EE messaging platforms. With it, users who receive harmful content can securely report that content to platform moderators. However, while real-world deployments of reporting require the disclosure of multiple...

PrompTrend: Continuous Community-Driven Vulnerability Discovery and Assessment for Large Language Models

Static benchmarks fail to capture LLM vulnerabilities emerging through community experimentation in online forums. We present PrompTrend, a system that collects vulnerability data across platforms and evaluates them using multidimensional scoring, with an architecture designed for scalable...

Running in CIRCLE? A Simple Benchmark for LLM Code Interpreter Security

As large language models LLMs increasingly integrate native code interpreters, they enable powerful real-time execution capabilities, substantially expanding their utility. However, such integrations introduce potential system-level cybersecurity threats, fundamentally different from prompt-based...

Virtual Local Area Network over HTTP for Launching an Insider Attack

Computers and computer networks have become integral to virtually every aspect of modern life, with the Internet playing an indispensable role. Organizations, businesses, and individuals now store vast amounts of proprietary, confidential, and personal data digitally. As such, ensuring the securi...

Clean Code in Practice: Challenges and Opportunities

Reliability prediction is crucial for ensuring the safety and security of software systems, especially in the context of industry practices. While various metrics and measurements are employed to assess software reliability, the complexity of modern systems necessitates a deeper understanding of...

URLCrazy Domain Name Typo Tool 0.8.1

URLCrazy is a tool that can generate and test domain typos and variations to detect and perform typo squatting, URL hijacking, phishing, and corporate espionage. It generates 15 types of domain variants, knows over 8000 common misspellings, supports multiple keyboard layouts, can check if a typo ...

Securing the Internet of Medical Things (IoMT): Real-World Attack Taxonomy and Practical Security Measures

The Internet of Medical Things IoMT has the potential to radically improve healthcare by enabling real-time monitoring, remote diagnostics, and AI-driven decision making. However, the connectivity, embedded intelligence, and inclusion of a wide variety of novel sensors expose medical devices to...

MOCHA: Are Code Language Models Robust against Multi-Turn Malicious Coding Prompts?

Recent advancements in Large Language Models LLMs have significantly enhanced their code generation capabilities. However, their robustness against adversarial misuse, particularly through multi-turn malicious coding prompts, remains underexplored. In this work, we introduce code decomposition...

How to Copy-Protect Malleable-Puncturable Cryptographic Functionalities under Arbitrary Challenge Distributions

A quantum copy-protection scheme Aaronson, CCC 2009 encodes a functionality into a quantum state such that given this state, no efficient adversary can create two possibly entangled quantum states that are both capable of running the functionality. There has been a recent line of works on...

On the Security of a Code-Based PIR Scheme

Private Information Retrieval PIR schemes allow clients to retrieve files from a database without disclosing the requested file's identity to the server. In the pursuit of post-quantum security, most recent PIR schemes rely on hard lattice problems. In contrast, the so called CB-cPIR scheme stand...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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,...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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 ...

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...

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...