Contrast Duality of Adversarial Learningin Network Intrusion: A Review

AI systems are valuable for cybersecurity because they can detect patterns and anomalies in large amounts of data, but attackers can exploit these same AI capabilities to launch sophisticated attacks. Adversarial learning (using AI to trick or attack other AI systems) works in two ways: attackers use techniques like data poisoning (corrupting training data) and test time evasion (fooling a trained model with specially crafted inputs) to compromise security systems, while defenders use adversarial training (teaching AI to resist such attacks) to protect against these threats. The source identifies gaps in current research, including a lack of real-world attack data and limited evaluation of AI solutions for network traffic analysis.