Quantum Computing for Network Security Classification: Near-Term Classification and Long-Term Memory Efficiency

Quantum computing has already been explored in several network-security applications. However, how quantum computing may contribute to network-security classification in both the near term and the longer term has not been systematically discussed. This paper studies this question through two...

Machine Learning-Based Cyber Defense for Cloud Infrastructure: An Adaptive Deep Q-Network Architecture for Intelligent Intrusion Detection and Automated Threat Mitigation

With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection and autonomous response. In this paper, we propose a reinforcement learning-based dynamic cyber defense framework. We deploy a Deep Q-Network DQN to...

Adaptive Intrusion Detection System Using Transformer-Based Neural Networks and Continual Learning Approach with Adversarial Investigation

Network intrusion detection systems IDS trained on fixed traffic snapshots decay silently after deployment as threat distributions shift. Fine-tuning models on new attacks triggers catastrophic forgetting, while retraining from scratch is computationally infeasible. Replay-based continual learnin...

Boundary-Seeking GAN-Augmented TabTransformer for Adversarially Robust Intrusion Detection

Machine learning-based intrusion detection systems IDSs often suffer from class imbalance and vulnerability to adversarial attacks, leading to degraded detection performance and reduced robustness. This study proposes a TabTransformer framework augmented by the Boundary-Seeking Generative...

BARS: Benign-Anchored Ranking and Selection for False Alarm Reduction in Network Intrusion Detection

False alarms remain a major barrier to deploying network intrusion detection systems NIDS. In high-volume environments, even a sub-1% false positive rate can generate tens of thousands of daily alerts. Filter-based feature selection is attractive because it operates upstream of the classifier and...

QCL-IDS: Quantum Continual Learning for Intrusion Detection with Fidelity-Anchored Stability and Generative Replay

Continual intrusion detection must absorb newly emerging attack stages while retaining legacy detection capability under strict operational constraints, including bounded compute and qubit budgets and privacy rules that preclude long-term storage of raw telemetry. We propose QCL-IDS, a...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...