CLIF: Cross-Layer LEO-ISL Fingerprinting for Physical and Network Attack Detection in Dense LEO Constellations

Low-Earth Orbit LEO mega-constellations such as Starlink by SpaceX and Kuiper by Amazon rely on optical Inter-Satellite Links ISLs for autonomous mesh routing to provide low-latency telecommunication, Internet of Things IoT, and security services globally. As commercial operators and governments...

Evaluating Tabular Representation Learning for Network Intrusion Detection

Classic Network Intrusion Detection Systems NIDS often rely on manual feature engineering to extract meaningful patterns from network traffic data. However, this approach requires domain expertise and runs counter to the widely adopted principle of modern machine learning and neural networks: tha...

System-Aware Contextual Digital Twin for ICS Anomaly Diagnosis

Industrial Control Systems ICS integrate computing, physical processes, and communication to operate critical infrastructures such as power grids, water treatment plants, and oil and gas facilities. As ICS become increasingly targeted by cyberattacks, timely and reliable anomaly diagnosis is...

API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

Reference-Free EM Validation Flow for Detecting Triggered Hardware Trojans

Hardware Trojans HTs threaten the trust and reliability of integrated circuits ICs, particularly when triggered HTs remain dormant during standard testing and activate only under rare conditions. Existing electromagnetic EM side-channel-based detection techniques often rely on golden references o...

Engineering Attack Vectors and Detecting Anomalies in Additive Manufacturing

Additive manufacturing AM is rapidly integrating into critical sectors such as aerospace, automotive, and healthcare. However, this cyber-physical convergence introduces new attack surfaces, especially at the interface between computer-aided design CAD and machine execution layers. In this work, ...

Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification

Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and finance. While the research community has made significant progress in WebShell detection i.e., distinguishing malicious...

Hybrid Quantum-Classical Autoencoders for Unsupervised Network Intrusion Detection

Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical HQC autoencoders for this task. We construct a unified experimental framework that...

AutoGraphAD: A Novel Approach Using Variational Graph Autoencoders for Anomalous Network Flow Detection

Network Intrusion Detection Systems NIDS are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very...

LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection

Machine learning ML-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and time of manual labeling or sandbox analysis. Existing approaches mitigate this via drift detection and selective...

HYDRA: A Hybrid Heuristic-Guided Deep Representation Architecture for Predicting Latent Zero-Day Vulnerabilities in Patched Functions

Software security testing, particularly when enhanced with deep learning models, has become a powerful approach for improving software quality, enabling faster detection of known flaws in source code. However, many approaches miss post-fix latent vulnerabilities that remain even after patches...

BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting

Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of such a vast amount of malware is an unrealistic, time-consuming, and overwhelming task. To cope with this volume, there is a clear need to develop...

Characterizing Event-Themed Malicious Web Campaigns: A Case Study on War-Themed Websites

Cybercrimes such as online scams and fraud have become prevalent. Cybercriminals often abuse various global or regional events as themes of their fraudulent activities to breach user trust and attain a higher attack success rate. These attacks attempt to manipulate and deceive innocent people int...

An Unsupervised Learning Approach for a Reliable Profiling of Cyber Threat Actors Reported Globally Based on Complete Contextual Information of Cyber Attacks

Cyber attacks are rapidly increasing with the advancement of technology and there is no protection for our information. To prevent future cyberattacks it is critical to promptly recognize cyberattacks and establish strong defense mechanisms against them. To respond to cybersecurity threats...

Anomaly Detection in Network Flows Using Unsupervised Online Machine Learning

Nowadays, the volume of network traffic continues to grow, along with the frequency and sophistication of attacks. This scenario highlights the need for solutions capable of continuously adapting, since network behavior is dynamic and changes over time. This work presents an anomaly detection mod...

Human-AI Collaborative Bot Detection in MMORPGs

In Massively Multiplayer Online Role-Playing Games MMORPGs, auto-leveling bots exploit automated programs to level up characters at scale, undermining gameplay balance and fairness. Detecting such bots is challenging, not only because they mimic human behavior, but also because punitive actions...

FlowMalTrans: Unsupervised Binary Code Translation for Malware Detection Using Flow-Adapter Architecture

Applying deep learning to malware detection has drawn great attention due to its notable performance. With the increasing prevalence of cyberattacks targeting IoT devices, there is a parallel rise in the development of malware across various Instruction Set Architectures ISAs. It is thus importan...

BlindGuard: Safeguarding LLM-Based Multi-Agent Systems under Unknown Attacks

The security of LLM-based multi-agent systems MAS is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through inter-agent message interactions. While existing supervised defense methods demonstrate promising performance, they may be...

Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This...