AI-Driven Detection of AI-Generated Cyber Attacks: A Framework for Defending Against Generative Adversarial Threats

With the advent of proliferation around generative artificial intelligence, opponents have the capability to develop advanced phishing concepts, produce polymorphic malware, and develop deep fake material to use to social engineer on an entirely new scale. This essay is an introduction to an all-encompassing detection model named AEGIS (Adaptive Engine for Generative Intrusion Sensing), which seeks to detect AI-created cyberattack. We use a combination of transformer-based linguistics to identify phishing messages generated by LLM, multimodal deepfake detection to identify synthetic media, and behavioural analysis to identify AI-assisted code obfuscation. Our new adversarial training framework has the benefit of sustaining detection strength in parallel with the growth of generative models to remedy the inherent problem of co-evolution between detectors and generators. Testing performed on a curated collection of 125,000 attack samples shows that AEGIS is able to detect spear-phishing emails by LLMs with 96.7% accuracy, detect deepfakes of voice/video attacks with 94.3% accuracy, and detect AIobfuscated malware with 91.8% accuracy with a falsepositiveness rate of less than 2.1%. Our framework sets a new defensive AI systems paradigm of adapting to the changing ability of adversarial generative models.