Elevating Intrusion Detection and Security Fortification in Intelligent Networks through Cutting-Edge Machine Learning Paradigms

The proliferation of IoT devices and their reliance on Wi-Fi networks have introduced significant security vulnerabilities, particularly the KRACK and Kr00k attacks, which exploit weaknesses in WPA2 encryption to intercept and manipulate sensitive data. Traditional IDS using classifiers face...

ReGAIN: Retrieval-Grounded AI Framework for Network Traffic Analysis

Modern networks generate vast, heterogeneous traffic that must be continuously analyzed for security and performance. Traditional network traffic analysis systems, whether rule-based or machine learning-driven, often suffer from high false positives and lack interpretability, limiting analyst...

Holoscope: Open and Lightweight Distributed Telescope and Honeypot Platform

The complexity and scale of Internet attacks call for distributed, cooperative observatories capable of monitoring malicious traffic across diverse networks. Holoscope is a lightweight, cloud-native platform designed to simplify the deployment and management of distributed telescope passive and...

6DAttack: Backdoor Attacks in the 6DoF Pose Estimation

Deep learning advances have enabled accurate six-degree-of-freedom 6DoF object pose estimation, widely used in robotics, AR/VR, and autonomous systems. However, backdoor attacks pose significant security risks. While most research focuses on 2D vision, 6DoF pose estimation remains largely...

Anti-Malicious ISAC: How to Jointly Monitor and Disrupt Your Foes?

Integrated sensing and communication ISAC systems are key enablers of future networks but raise significant security concerns. In this realm, the emergence of malicious ISAC systems has amplified the need for authorized parties to legitimately monitor suspicious communication links and protect...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...

Evaluating MCC for Low-Frequency Cyberattack Detection in Imbalanced Intrusion Detection Data

In many real-world network environments, several types of cyberattacks occur at very low rates compared to benign traffic, making them difficult for intrusion detection systems IDS to detect reliably. This imbalance causes traditional evaluation metrics, such as accuracy, to often overstate model...

Needles in a Haystack: Using Forensic Network Science to Uncover Insider Trading

Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To address this challenge, we propose using a data-driven...

Quantum-Resistant Cryptographic Models for Next-Gen Cybersecurity

Another threat is the development of large quantum computers, which have a high likelihood of breaking the high popular security protocols because it can use both Shor and Grover algorithms. In order to fix this looming threat, quantum-resistant cryptographic systems, otherwise known as...

Efficient Jailbreak Mitigation Using Semantic Linear Classification in a Multi-Staged Pipeline

Prompt injection and jailbreaking attacks pose persistent security challenges to large language model LLM-based systems. We present an efficient and systematically evaluated defense architecture that mitigates these threats through a lightweight, multi-stage pipeline. Its core component is a...

DREAM: Dynamic Red-Teaming across Environments for AI Models

Large Language Models LLMs are increasingly used in agentic systems, where their interactions with diverse tools and environments create complex, multi-stage safety challenges. However, existing benchmarks mostly rely on static, single-turn assessments that miss vulnerabilities from adaptive,...

An Evidence-Driven Analysis of Threat Information Sharing Challenges for Industrial Control Systems and Future Directions

The increasing cyber threats to critical infrastructure highlight the importance of private companies and government agencies in detecting and sharing information about threat activities. Although the need for improved threat information sharing is widely recognized, various technical and...

DeepGuard: Defending Deep Joint Source-Channel Coding against Eavesdropping at Physical-Layer

Deep joint source-channel coding DeepJSCC has emerged as a promising paradigm for efficient and robust information transmission. However, its intrinsic characteristics also pose new security challenges, notably an increased vulnerability to eavesdropping attacks. Existing studies on defending...

Cyber Threat Detection Enabled by Quantum Computing

Threat detection models in cybersecurity must keep up with shifting traffic, strict feature budgets, and noisy hardware, yet even strong classical systems still miss rare or borderline attacks when the data distribution drifts. Small, near-term quantum processors are now available, but existing...

Software Vulnerability Management in the Era of Artificial Intelligence: An Industry Perspective

Artificial Intelligence AI has revolutionized software development, particularly by automating repetitive tasks and improving developer productivity. While these advancements are well-documented, the use of AI-powered tools for Software Vulnerability Management SVM, such as vulnerability detectio...

AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software

Large language models LLMs for code generation are becoming integral to modern software development, but their real-world prevalence and security impact remain poorly understood. We present the first large-scale empirical study of AI-generated code AIGCode in the wild. We build a high-precision...

Enhancing Decision-Making in Windows PE Malware Classification during Dataset Shifts with Uncertainty Estimation

Artificial intelligence techniques have achieved strong performance in classifying Windows Portable Executable PE malware, but their reliability often degrades under dataset shifts, leading to misclassifications with severe security consequences. To address this, we enhance an existing LightGBM...

SoK: Understanding (New) Security Issues across AI4Code Use Cases

AI-for-Code AI4Code systems are reshaping software engineering, with tools like GitHub Copilot accelerating code generation, translation, and vulnerability detection. Alongside these advances, however, security risks remain pervasive: insecure outputs, biased benchmarks, and susceptibility to...

MORPHEUS: A Multidimensional Framework for Modeling, Measuring, and Mitigating Human Factors in Cybersecurity

Current cybersecurity research increasingly acknowledges the human factor, yet remains fragmented, often treating user vulnerabilities as isolated and static traits. This paper introduces MORPHEUS, a holistic framework that operationalizes human-centric security as a dynamic and interconnected...

Optimizing Epsilon Security Parameters in QKD

We investigate the optimization of epsilon-security parameters in quantum key distribution QKD, aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm CGA to optimize the epsilon-security...

New Exam Security Questions in the AI Era: Comparing AI-Generated Item Similarity between Naive and Detail-Guided Prompting Approaches

Large language models LLMs have emerged as powerful tools for generating domain-specific multiple-choice questions MCQs, offering efficiency gains for certification boards but raising new concerns about examination security. This study investigated whether LLM-generated items created with...

GameDriverX64.sys 7.23.4.7 Resource Consumption

GameDriverX64.sys versions 7.23.4.7 and below suffers from an uncontrolled resource consumption vulnerability...

Securing Agentic AI Systems -- a Multilayer Security Framework

Securing Agentic Artificial Intelligence AI systems requires addressing the complex cyber risks introduced by autonomous, decision-making, and adaptive behaviors. Agentic AI systems are increasingly deployed across industries, organizations, and critical sectors such as cybersecurity, finance, an...

MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-Of-Distribution Malware Detection and Classification

Out of distribution OOD detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to...

Social Engineering Attacks: A Systemisation of Knowledge on People against Humans

Our systematisation of knowledge on Social Engineering Attacks SEAs, identifies the human, organisational, and adversarial dimensions of cyber threats. It addresses the growing risks posed by SEAs, highly relevant in the context physical cyber places, such as travellers at airports and residents ...

GRAudit Grep Auditing Tool 4.0

Graudit is a simple script and signature sets that allows you to find potential security flaws in source code using the GNU utility, grep. It's comparable to other static analysis applications like RATS, SWAAT, and flaw-finder while keeping the technical requirements to a minimum and being very...

CISA: Safe Handling Considerations for Downed Unmanned Aircraft Systems

Safe Handling Considerations for Downed Unmanned Aircraft Systems provides information on how to prepare for and respond to downed unmanned aircraft systems UAS that may pose a safety or security concern...

PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS

Modern intrusion detection systems IDS leverage graph neural networks GNNs to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations...

A Practical Solution to Systematically Monitor Inconsistencies in SBOM-Based Vulnerability Scanners

Software Bill of Materials SBOM provides new opportunities for automated vulnerability identification in software products. While the industry is adopting SBOM-based Vulnerability Scanning SVS to identify vulnerabilities, we increasingly observe inconsistencies and unexpected behavior, that resul...

Cryptanalysis of Pseudorandom Error-Correcting Codes

Pseudorandom error-correcting codes PRC is a novel cryptographic primitive proposed at CRYPTO 2024. Due to the dual capability of pseudorandomness and error correction, PRC has been recognized as a promising foundational component for watermarking AI-generated content. However, the security of PR...

CISA: Suspicious Unmanned Aircraft System Activity Guidance

Suspicious Unmanned Aircraft System Activity Guidance for Critical Infrastructure Owners and Operators is intended for critical infrastructure stakeholders who are concerned with unmanned aircraft system UAS activity near or around their facilities...

RAPTOR - Autonomous Offensive/Defensive Security Research Framework

RAPTOR is an autonomous offensive/defensive security research framework, based on Claude Code. It empowers security research with agentic workflows and automation. RAPTOR stands for Recursive Autonomous Penetration Testing and Observation Robot...

Phishing Detection System: An Ensemble Approach Using Character-Level CNN and Feature Engineering

In actuality, phishing attacks remain one of the most prevalent cybersecurity risks in existence today, with malevolent actors constantly changing their strategies to successfully trick users. This paper presents an AI model for a phishing detection system that uses an ensemble approach to combin...

A First Look at Common RPKI Publication Practices

The RPKI is crucial for securing the routing system of the Internet. With the RPKI, owners of Internet resources can make cryptographically backed claims, for example about the legitimate origin of their IP space. Thousands of networks use this information to detect malicious or accidental route...

NGCaptcha: A CAPTCHA Bridging the past and the Future

CAPTCHAs are widely employed for distinguishing humans from automated bots online. However, current vision based CAPTCHAs face escalating security risks: traditional attacks continue to bypass many deployed CAPTCHA schemes, and recent breakthroughs in AI, particularly large scale vision models,...

AutoDFBench 1.0: A Benchmarking Framework for Digital Forensic Tool Testing and Generated Code Evaluation

The National Institute of Standards and Technology NIST Computer Forensic Tool Testing CFTT programme has become the de facto standard for providing digital forensic tool testing and validation. However to date, no comprehensive framework exists to automate benchmarking across the diverse forensi...

A Systematic Study of Code Obfuscation against LLM-Based Vulnerability Detection

As large language models LLMs are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse vulnerability types have become a pressing concern. In traditional adversarial settings, code obfuscation has long been used as a general strategy to bypass...

SoK: Reviewing Two Decades of Security, Privacy, Accessibility, and Usability Studies on Internet of Things for Older Adults

The Internet of Things IoT has the potential to enhance older adults' independence and quality of life, but it also exposes them to security, privacy, accessibility, and usability SPAU risks. We conducted a systematic review of 44 peer-reviewed studies published between 2004 and 2024 using a...

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model LLM safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of...

Security Risks of Agentic Vehicles: A Systematic Analysis of Cognitive and Cross-Layer Threats

Agentic AI is increasingly being explored and introduced in both manually driven and autonomous vehicles, leading to the notion of Agentic Vehicles AgVs, with capabilities such as memory-based personalization, goal interpretation, strategic reasoning, and tool-mediated assistance. While framework...

Large Language Models As a (Bad) Security Norm in the Context of Regulation and Compliance

The use of Large Language Models LLM by providers of cybersecurity and digital infrastructures of all kinds is an ongoing development. It is suggested and on an experimental basis used to write the code for the systems, and potentially fed with sensitive data or what would otherwise be considered...

Analysing Multidisciplinary Approaches to Fight Large-Scale Digital Influence Operations

Crime as a Service CaaS has evolved from isolated criminal incidents to a broad spectrum of illicit activities, including social media manipulation, foreign information manipulation and interference FIMI, and the sale of disinformation toolkits. This article analyses how threat actors exploit...

Bounty Hunter: Autonomous, Comprehensive Emulation of Multi-Faceted Adversaries

Adversary emulation is an essential procedure for cybersecurity assessments such as evaluating an organization's security posture or facilitating structured training and research in dedicated environments. To allow for systematic and time-efficient assessments, several approaches from academia an...

Time Will Tell: Large-Scale De-Anonymization of Hidden I2P Services Via Live Behavior Alignment (Extended Version)

I2P Invisible Internet Project is a popular anonymous communication network. While existing de-anonymization methods for I2P focus on identifying potential traffic patterns of target hidden services among extensive network traffic, they often fail to scale effectively across the large and diverse...

Quantum Machine Learning for Cybersecurity: A Taxonomy and Future Directions

The increasing number of cyber threats and rapidly evolving tactics, as well as the high volume of data in recent years, have caused classical machine learning, rules, and signature-based defence strategies to fail, rendering them unable to keep up. An alternative, Quantum Machine Learning QML, h...

WuppieFuzz: Coverage-Guided, Stateful REST API Fuzzing

Many business processes currently depend on web services, often using REST APIs for communication. REST APIs expose web service functionality through endpoints, allowing easy client interaction over the Internet. To reduce the security risk resulting from exposed endpoints, thorough testing is...

CAPIO: Safe Kernel-Bypass of Commodity Devices Using Capabilities

Securing low-latency I/O in commodity systems forces a fundamental trade-off: rely on the kernel's high overhead mediated interface, or bypass it entirely, exposing sensitive hardware resources to userspace and creating new vulnerabilities. This dilemma stems from a hardware granularity mismatch:...

Random Coding for Long-Range Continuous-Variable QKD

Quantum Key Distribution QKD schemes are key exchange protocols based on the physical properties of quantum channels. They avoid the computational-hardness assumptions that underlie the security of classical key exchange. Continuous-Variable QKD CVQKD, in contrast to qubit-based discrete-variable...

Packed Malware Detection Using Grayscale Binary-To-Image Representations

Detecting packed executables is a critical step in malware analysis, as packing obscures the original code and complicates static inspection. This study evaluates both classical feature-based methods and deep learning approaches that transform binary executables into visual representations,...

Talking to the Airgap: Exploiting Radio-Less Embedded Devices As Radio Receivers

Intelligent electronics are deeply embedded in critical infrastructures and must remain reliable, particularly against deliberate attacks. To minimize risks and impede remote compromise, sensitive systems can be physically isolated from external networks, forming an airgap. Yet, airgaps can still...