Secure Low-Altitude Maritime Communications Via Intelligent Jamming

Low-altitude wireless networks LAWNs have emerged as a viable solution for maritime communications. In these maritime LAWNs, unmanned aerial vehicles UAVs serve as practical low-altitude platforms for wireless communications due to their flexibility and ease of deployment. However, the open and...

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...

Machine and Deep Learning for Indoor UWB Jammer Localization

Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...

Quantum Autoencoders for Anomaly Detection in Cybersecurity

Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggles in data-limited settings which quantum counterparts can potentially...

Security-Robustness Trade-Offs in Diffusion Steganography: A Comparative Analysis of Pixel-Space and VAE-Based Architectures

Current generative steganography research mainly pursues computationally expensive mappings to perfect Gaussian priors within single diffusion model architectures. This work introduces an efficient framework based on approximate Gaussian mapping governed by a scale factor calibrated through...

Collusion-Driven Impersonation Attack on Channel-Resistant RF Fingerprinting

Radio frequency fingerprint RFF is a promising device identification technology, with recent research shifting from robustness to security due to growing concerns over vulnerabilities. To date, while the security of RFF against basic spoofing such as MAC address tampering has been validated, its...

Self-Supervised Learning of Graph Representations for Network Intrusion Detection

Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged graph neural networks for network intrusion detection, they often decouple representation learning from anomaly detectio...

Hybrid Deep Learning-Federated Learning Powered Intrusion Detection System for IoT/5G Advanced Edge Computing Network

The exponential expansion of IoT and 5G-Advanced applications has enlarged the attack surface for DDoS, malware, and zero-day intrusions. We propose an intrusion detection system that fuses a convolutional neural network CNN, a bidirectional LSTM BiLSTM, and an autoencoder AE bottleneck within a...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...

Designing with Deception: ML- and Covert Gate-Enhanced Camouflaging to Thwart IC Reverse Engineering

Integrated circuits ICs are essential to modern electronic systems, yet they face significant risks from physical reverse engineering RE attacks that compromise intellectual property IP and overall system security. While IC camouflage techniques have emerged to mitigate these risks, existing...

GUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection

Modern in-vehicle networks face various cyber threats due to the lack of encryption and authentication in the Controller Area Network CAN. To address this security issue, this paper presents GUARD-CAN, an anomaly detection framework that combines graph-based representation learning with time-seri...

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...

Embedding Trust at Scale: Physics-Aware Neural Watermarking for Secure and Verifiable Data Pipelines

We present a robust neural watermarking framework for scientific data integrity, targeting high-dimensional fields common in climate modeling and fluid simulations. Using a convolutional autoencoder, binary messages are invisibly embedded into structured data such as temperature, vorticity, and...

Enclosing Prototypical Variational Autoencoder for Explainable Out-of-Distribution Detection

Understanding the decision-making and trusting the reliability of Deep Machine Learning Models is crucial for adopting such methods to safety-relevant applications. We extend self-explainable Prototypical Variational models with autoencoder-based out-of-distribution OOD detection: A Variational...

A Crack in the Bark: Leveraging Public Knowledge to Remove Tree-Ring Watermarks

We present a novel attack specifically designed against Tree-Ring, a watermarking technique for diffusion models known for its high imperceptibility and robustness against removal attacks. Unlike previous removal attacks, which rely on strong assumptions about attacker capabilities, our attack on...

ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing

Rogue Base Station RBS attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat as 5G Standalone SA deployments are still limited and User Equipment UE manufacturers continue to support legacy network connectivity. This work introduces ARGOS, a comprehensive...

SimProcess: High Fidelity Simulation of Noisy ICS Physical Processes

Industrial Control Systems ICS manage critical infrastructures like power grids and water treatment plants. Cyberattacks on ICSs can disrupt operations, causing severe economic, environmental, and safety issues. For example, undetected pollution in a water plant can put the lives of thousands at...

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

Internet of Vehicles IoV systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and...

Efficient Malicious UAV Detection Using Autoencoder-TSMamba Integration

Malicious Unmanned Aerial Vehicles UAVs present a significant threat to next-generation networks NGNs, posing risks such as unauthorized surveillance, data theft, and the delivery of hazardous materials. This paper proposes an integrated AE-classifier system to detect malicious UAVs. The proposed...