Abstract:
The traditional signature-based measures of cybersecurity faced growing challenges due to advanced cyber threats. Cyber AI, on the other hand, aided in automating dynamic and adaptive threat mitigation frameworks that can negate both known and unknown risks in real time. This paper explores the application of machine learning (ML), deep learning (DL), and natural language processing (NLP) in the context of AI-powered threat detection in current cybersecurity infrastructures. This paper starts off by identifying gaps around conventional detection tools that relied on static heuristics and rule-based methods, and didn‘t perform well against zero-day attacks, polymorphic malware, or advanced persistent threats (APTs) encounters. Also, integrating AI into these frameworks allows the use of predictive analytics and behavioural modelling to automate counteractive measures that identify, classify, and neutralise exploits. The examined methodologies also include malware classification using supervised and unsupervised learning algorithms, intrusion detection using neural networks, and analysing threat intelligence from phishing emails using NLP. The fast growth of cyber threats in their style, size, and smart tactics has made normal rulebased safety measures less useful. As a result, Artificial Intelligence (AI) is now seen as a game changer in finding dangers; it provides flexible, smart, and quick solutions that can spot and reduce both familiar and unfamiliar risks instantly. This paper reviews in detail AIdriven threat discovery, emphasising the use of machine learning (ML), deep learning (DL), and natural language processing(NLP) methods within current frameworks. The study begins by contextualising where conventional threat detection methods, rule-based systems and static heuristics fall short in combating zero-day exploits. malware and advanced persisten threats (APTs). Contrarily, AI-driven approaches use predictive analytics, behavioural modelling, and automated response mechanisms for anomaly recognition as well as classification of malicious activities to threats neutralisation prior to escalation. Major methodologies covered include: i) the supervised and unsupervised ML algorithms for malware classification; ii) neural networks for intrusion detection; and iii) NLP for threat intelligence analysis from sources like phishing emails or even dark web forums. It also examines recent developments in deep learning, including CNNs for image-based malware analysis and RNNs for identifying structured attack patterns in network traffic. It also addresses the aspect of how it considers generative adversarial networks in the process of simulating attacks on reinforcing defence systems. Also, this piece of work describes the improved outcome achieved from integrating AI with Security Information and Event Management systems, where threat correlation by machines and real-time response to incidents significantly lower detection and remediation time. Significant challenges that AI-based threat detection confronts in spite of its multiple advantages include adversarial attacks meant to mislead the ML models, limited training data leading to scarcity for creating strong systems, and the "black-box" nature of AI decisionmaking, coupled with lack of transparency and accountability. The moral consequences on potential biases in threat categorisation as well as privacy considerations of ubiquitous AI surveillance, are thoroughly examined.
