Developing Robust AI-Based Cybersecurity Alerting and Intelligence Systems Against Adversarial Attacks

Abstract

The increasing reliance on Artificial Intelligence (AI) in cybersecurity has significantly enhanced detection and defense mechanisms. But, adversarial machine learning (AML) presents critical vulnerabilities that undermine reliability of AI-driven security systems. Adversaries craft subtle perturbations to inputs, deceiving models into misclassifications, thereby bypassing intrusion detection systems, malware classifiers, and other defense mechanisms. This reasearch explores the two-fold nature of artificial intelligence in the field of cybersecurity, both as an enabler of robust defense and as target for adversarial attacks. Focusing on intrusion detection and malware classification, we propose a hybrid defense framework that combines adversarial training, model distillation, and explainable AI (XAI) to counter adversarial threats. By integrating dual datasets (CSE-CIC-IDS2018 and Microsoft Malware Dataset) and evaluating them under various adversarial attack strategies, the framework enhances both robustness and interpretability of AI models. Additionally, this is deployed in real-time cloud environments to ensure scalability and operational efficiency. The proposed methodology is aim to provide reliable cybersecurity solutions capable of withstanding sophisticated adversarial attacks while maintaining high levels of transparency for security analysts. This research contributes to advancing resilient, scalable, and explainable AI-driven cybersecurity frameworks for modern digital infrastructures.