Advancing cybersecurity with artificial intelligence and machine learning: Architectures, algorithms, and future directions in threat detection and mitigation

The ever-growing number of and development of more elaborate threats require different levels of protection than formal regulation. AI & ML technology provide a promising outlook that even exists in the security domain and has become universal to design better, more dynamic security measures with better preparedness. In particular, the current paper discusses the correlation between AI, ML, and cybersecurity regarding architectures, algorithms, and potential for further development. The chosen AI-based architectures are captured here, like deep learning models, federated learning frameworks, and graph-based techniques to detect malware, phishing, ransomware, and insider threats. The paper then moves to discuss methods of improving anomalous behavior identification, Intrusion detection systems (IDS), and real-time threat analysis, especially focusing on supervised, unsupervised, and reinforcement types of learning. Three burgeoning fields of interest, explainable AI (XAI), adversarial machine learning, and incorporating blockchain into AI methodology, have been identified as crucial in responding to new challenges like adversarial attacks and data protection. However, AI and ML have limitations, including high computational demand, lack of data, and bias; hence, future work is needed. This paper outlines a possible interdisciplinary research agenda for enhancing AI in cybersecurity involving integrated platforms, technology case data, and an ethical dimension. Crossing the methods of theoretical analysis and real-life examples, this paper highlights the significance of AI and ML in constructing the further development of reliable and protected ICT environments.