Introspective Intrusion Detection System Through Explainable AI

The vulnerability of modern systems allows attackers to launch distributed denial-of-service (DDoS) attacks and use sophisticated techniques to compromise IDS, posing significant threats to intrusion detection systems (IDS) integrity, sensitive data, and critical network processes. Attackers can exploit these weaknesses to launch complex attacks that bypass existing defenses. As such incidents increase, developing advanced security measures is crucial. Combining DDoS and adversarial attacks requires a robust, adaptive security response to mitigate risks and protect infrastructure. To address this, we aim to analyze malicious behavior and develop robust technologies to detect and respond to DDoS and adversary attacks. Attackers often use countermeasures to reduce the effectiveness of machine learning (ML)-based IDSs and enhance DDoS attack impacts. To counter these threats, we propose an adversarial detection and mitigation approach using (XAI) for real-time IDS performance assessment. Our comprehensive XAI-based framework includes enhancing transparency and detection capabilities, analyzing the impact of adversarial examples on ML models, and implementing a zero-touch detection scheme to improve IDS efficiency. This proactive approach strengthens IDS security and capabilities, enabling it to identify and respond to future threats and ensure long-term protection and reliability.