Memory Poisoning Attack and Defense on Memory Based LLM-Agents

Large language model agents equipped with persistent memory are vulnerable to memory poisoning attacks, where adversaries inject malicious instructions through query only interactions that corrupt the agents long term memory and influence future responses. Recent work demonstrated that the MINJA...

Comparative Evaluation of VAE, GAN, and SMOTE for Tor Detection in Encrypted Network Traffic

Encrypted network traffic poses significant challenges for intrusion detection due to the lack of payload visibility, limited labeled datasets, and high class imbalance between benign and malicious activities. Traditional data augmentation methods struggle to preserve the complex temporal and...

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, ...

Towards Eco Friendly Cybersecurity: Machine Learning Based Anomaly Detection with Carbon and Energy Metrics

The rising energy footprint of artificial intelligence has become a measurable component of US data center emissions, yet cybersecurity research seldom considers its environmental cost. This study introduces an eco aware anomaly detection framework that unifies machine learning based network...

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...

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...

FiD-QAE: A Fidelity-Driven Quantum Autoencoder for Credit Card Fraud Detection

Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated ...

Quantum-Augmented AI/ML for O-RAN: Hierarchical Threat Detection with Synergistic Intelligence and Interpretability (Technical Report)

Open Radio Access Networks O-RAN enhance modularity and telemetry granularity but also widen the cybersecurity attack surface across disaggregated control, user and management planes. We propose a hierarchical defense framework with three coordinated layers-anomaly detection, intrusion...

Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses

The modern web stack, which is dominated by browser-based applications and API-first backends, now operates under an adversarial equilibrium where automated, AI-assisted attacks evolve continuously. Content Delivery Networks CDNs and edge computing place programmable defenses closest to users and...

BGPFuzz: Automated Configuration Fuzzing of the Border Gateway Protocol

Telecommunications networks rely on configurations to define routing behavior, especially in the Border Gateway Protocol BGP, where misconfigurations can lead to severe outages and security breaches, as demonstrated by the 2021 Facebook outage. Unlike existing approaches that rely on synthesis or...

A Novel Trust-Based DDoS Cyberattack Detection Model for Smart Business Environments

As the frequency and complexity of Distributed Denial-of-Service DDoS attacks continue to increase, the level of threats posed to Smart Internet of Things SIoT business environments have also increased. These environments generally have several interconnected SIoT systems and devices that are...

AI-Driven Cybersecurity Testbed for Nuclear Infrastructure: Comprehensive Evaluation Using METL Operational Data

Advanced nuclear reactor systems face increasing cybersecurity threats as sophisticated attackers exploit cyber-physical interfaces to manipulate control systems while evading traditional IT security measures. This research presents a comprehensive evaluation of artificial intelligence approaches...

SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks

The increasing complexity of IoT edge networks presents significant challenges for anomaly detection, particularly in identifying sophisticated Denial-of-Service DoS attacks and zero-day exploits under highly dynamic and imbalanced traffic conditions. This paper proposes SD-CGAN, a Conditional...

Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison

The rapid expansion of Internet of Things IoT deployments across diverse sectors has significantly enhanced operational efficiency, yet concurrently elevated cybersecurity vulnerabilities due to increased exposure to cyber threats. Given the limitations of traditional signature-based Anomaly...

Federated Anomaly Detection and Mitigation for EV Charging Forecasting under Cyberattacks

Electric Vehicle EV charging infrastructure faces escalating cybersecurity threats that can severely compromise operational efficiency and grid stability. Existing forecasting techniques are limited by the lack of combined robust anomaly mitigation solutions and data privacy preservation...

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...

Securing AI Agents against Prompt Injection Attacks

Retrieval-augmented generation RAG systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled A...

From Topology to Behavioral Semantics: Enhancing BGP Security by Understanding BGP's Language with LLMs

The trust-based nature of Border Gateway Protocol BGP makes it vulnerable to disruptions like prefix hijacking and misconfigurations, threatening routing stability. Traditional detection relies on manual inspection with limited scalability. Machine/Deep Learning M/DL approaches automate detection...

LogPurge: Log Data Purification for Anomaly Detection Via Rule-Enhanced Filtering

Log anomaly detection, which is critical for identifying system failures and preempting security breaches, detects irregular patterns within large volumes of log data, and impacts domains such as service reliability, performance optimization, and database log analysis. Modern log anomaly detectio...

Scalable Hierarchical AI-Blockchain Framework for Real-Time Anomaly Detection in Large-Scale Autonomous Vehicle Networks

The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes...