Towards Ultra-Low Latency: Binarized Neural Network Architectures for In-Vehicle Network Intrusion Detection

The Control Area Network CAN protocol is essential for in-vehicle communication, facilitating high-speed data exchange among Electronic Control Units ECUs. However, its inherent design lacks robust security features, rendering vehicles susceptible to cyberattacks. While recent research has...

An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process

Wireless Sensor Networks forms the backbone of modern cyber physical systems used in various applications such as environmental monitoring, healthcare monitoring, industrial automation, and smart infrastructure. Ensuring the reliability of data collected through these networks is essential as the...

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience. Graph-based approaches have become increasingly important for modeling entity interactions, yet most rely on homogeneou...

Packet Fence 15.0.0

PacketFence is a network access control NAC system. It is actively maintained and has been deployed in numerous large-scale institutions. It can be used to effectively secure networks, from small to very large heterogeneous networks. PacketFence provides NAC-oriented features such as registration...

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...

Securing IoT Communications Via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method

The rapid growth of the Internet of Things IoT has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service DoS attacks, anomalous traffic, and data manipulation due to decentralized...

A Novel GPT-Based Framework for Anomaly Detection in System Logs

Identification of anomalous events within system logs constitutes a pivotal element within the frame- work of cybersecurity defense strategies. However, this process faces numerous challenges, including the management of substantial data volumes, the distribution of anomalies, and the precision o...

Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection

The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems IDS often struggle to maintain high accuracy across both frequent and rare...

EUVD-2023-28005

Malicious code in bioql PyPI...

SoK: Systematic Analysis of Adversarial Threats against Deep Learning Approaches for Autonomous Anomaly Detection Systems in SDN-IoT Networks

Integrating SDN and the IoT enhances network control and flexibility. DL-based AAD systems improve security by enabling real-time threat detection in SDN-IoT networks. However, these systems remain vulnerable to adversarial attacks that manipulate input data or exploit model weaknesses,...

Red Teaming Quantum-Resistant Cryptographic Standards: A Penetration Testing Framework Integrating AI and Quantum Security

This study presents a structured approach to evaluating vulnerabilities within quantum cryptographic protocols, focusing on the BB84 quantum key distribution method and National Institute of Standards and Technology NIST approved quantum-resistant algorithms. By integrating AI-driven red teaming,...

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...

A Practical Adversarial Attack against Sequence-Based Deep Learning Malware Classifiers

Sequence-based deep learning models e.g., RNNs, can detect malware by analyzing its behavioral sequences. Meanwhile, these models are susceptible to adversarial attacks. Attackers can create adversarial samples that alter the sequence characteristics of behavior sequences to deceive malware...

Anomaly Detection in Industrial Control Systems Based on Cross-Domain Representation Learning

Industrial control systems ICSs are widely used in industry, and their security and stability are very important. Once the ICS is attacked, it may cause serious damage. Therefore, it is very important to detect anomalies in ICSs. ICS can monitor and manage physical devices remotely using...

ALPHA: LLM-Enabled Active Learning for Human-Free Network Anomaly Detection

Network log data analysis plays a critical role in detecting security threats and operational anomalies. Traditional log analysis methods for anomaly detection and root cause analysis rely heavily on expert knowledge or fully supervised learning models, both of which require extensive labeled dat...

LogGuardQ: a Cognitive-Enhanced Reinforcement Learning Framework for Cybersecurity Anomaly Detection in Security Logs

Reinforcement learning RL has transformed sequential decision-making, but traditional algorithms like Deep Q-Networks DQNs and Proximal Policy Optimization PPO often struggle with efficient exploration, stability, and adaptability in dynamic environments. This study presents LogGuardQ Adaptive Lo...

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...

Hybrid Cryptographic Monitoring System for Side-Channel Attack Detection on PYNQ SoCs

AES-128 encryption is theoretically secure but vulnerable in practical deployments due to timing and fault injection attacks on embedded systems. This work presents a lightweight dual-detection framework combining statistical thresholding and machine learning ML for real-time anomaly detection. B...

Addressing Weak Authentication like RFID, NFC in EVs and EVCs Using AI-Powered Adaptive Authentication

The rapid expansion of the Electric Vehicles EVs and Electric Vehicle Charging Systems EVCs has introduced new cybersecurity challenges, specifically in authentication protocols that protect vehicles, users, and energy infrastructure. Although widely adopted for convenience, traditional...