AI on the Defensive and Offensive: Securing Multi-Environment Networks from AI Agents

The role of artificial intelligence (AI) in cybersecu-rity has grown due to increasing threats from malicious actors. It aids in threat detection, behavioral analysis, malware detection, phishing identification, and enhancing security measures. However, AI can also be weaponized for cyberattacks, as malicious actors use AI-based tools for sophisticated and adaptable assaults on security systems. This study contributes to cybersecurity by defending against AI-based threats. Machine learning models were trained on diverse, complex datasets to counter sophisticated AI-based attacks in multi-environments (M-En). We have utilized auto-encoders to generate our M-En dataset by combining two benchmark datasets: UNSW-NB15 and IoTID-20, that represent traditional IP-based and IoT-based traffic, respectively. Three generative models (CTGAN, CopulaGAN, and TVAE) produced AI-based traffic, leading to a dataset comprising traditional and AI-generated traffic. Machine learning and deep learning models were deployed on this M-En dataset. The ensemble Extra Trees classifier achieved the highest accuracy score of 0.983 for binary classification and 0.968 for multiclass problems. Our proposed approach demonstrates its effectiveness in countering AI-based traffic as well as traditional network traffic within the M-En networks.