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...

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...

$AutoGuardX$: a Comprehensive Cybersecurity Framework for Connected Vehicles

The rapid integration of Internet of Things IoT and interconnected systems in modern vehicles not only introduced a new era of convenience, automation, and connected vehicles but also elevated their exposure to sophisticated cyber threats. This is especially evident in US and Canada, where...

A Comprehensive Review of Denial of Wallet Attacks in Serverless Architectures

The Denial of Wallet DoW attack poses a unique and growing threat to serverless architectures that rely on Function-as-a-Service FaaS models, exploiting the cost structure of pay-as-you-go billing to financially burden application owners. Unlike traditional Denial of Service DoS attacks, which ai...

Adaptive Anomaly Detection in Evolving Network Environments

Distribution shift, a change in the statistical properties of data over time, poses a critical challenge for deep learning anomaly detection systems. Existing anomaly detection systems often struggle to adapt to these shifts. Specifically, systems based on supervised learning require costly manua...

Addressing Side-Channel Threats in Quantum Key Distribution Via Deep Anomaly Detection

Traditional countermeasures against security side channels in quantum key distribution QKD systems often suffer from poor compatibility with deployed infrastructure, the risk of introducing new vulnerabilities, and limited applicability to specific types of attacks. In this work, we propose an...

MPOCryptoML: Multi-Pattern Based Off-Chain Crypto Money Laundering Detection

Recent advancements in money laundering detection have demonstrated the potential of using graph neural networks to capture laundering patterns accurately. However, existing models are not explicitly designed to detect the diverse patterns of off-chain cryptocurrency money laundering. Neglecting...

Causal Graph Profiling Via Structural Divergence for Robust Anomaly Detection in Cyber-Physical Systems

With the growing complexity of cyberattacks targeting critical infrastructures such as water treatment networks, there is a pressing need for robust anomaly detection strategies that account for both system vulnerabilities and evolving attack patterns. Traditional methods -- statistical,...

Generative AI for Cybersecurity of Energy Management Systems: Methods, Challenges, and Future Directions

This paper elaborates on an extensive security framework specifically designed for energy management systems EMSs, which effectively tackles the dynamic environment of cybersecurity vulnerabilities and/or system problems SPs, accomplished through the incorporation of novel methodologies. A...

Generative AI for Critical Infrastructure in Smart Grids: a Unified Framework for Synthetic Data Generation and Anomaly Detection

In digital substations, security events pose significant challenges to the sustained operation of power systems. To mitigate these challenges, the implementation of robust defense strategies is critically important. A thorough process of anomaly identification and detection in information and...

Membership Inference Attack with Partial Features

Machine learning models have been shown to be susceptible to membership inference attack, which can be used to determine whether a given sample appears in the training data. Existing membership inference methods commonly assume that the adversary has full access to the features of the target...

Log2Sig: Frequency-Aware Insider Threat Detection Via Multivariate Behavioral Signal Decomposition

Insider threat detection presents a significant challenge due to the deceptive nature of malicious behaviors, which often resemble legitimate user operations. However, existing approaches typically model system logs as flat event sequences, thereby failing to capture the inherent frequency dynami...

MambaITD: an Efficient Cross-Modal Mamba Network for Insider Threat Detection

Enterprises are facing increasing risks of insider threats, while existing detection methods are unable to effectively address these challenges due to reasons such as insufficient temporal dynamic feature modeling, computational efficiency and real-time bottlenecks and cross-modal information...

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...

WBHT: a Generative Attention Architecture for Detecting Black Hole Anomalies in Backbone Networks

We propose the Wasserstein Black Hole Transformer WBHT framework for detecting black hole BH anomalies in communication networks. These anomalies cause packet loss without failure notifications, disrupting connectivity and leading to financial losses. WBHT combines generative modeling, sequential...

HumanSAM: Classifying Human-Centric Forgery Videos in Human Spatial, Appearance, and Motion Anomaly

Numerous synthesized videos from generative models, especially human-centric ones that simulate realistic human actions, pose significant threats to human information security and authenticity. While progress has been made in binary forgery video detection, the lack of fine-grained understanding ...