Advancing Cyber Threat Detection with Ai: Cutting-Edge Techniques and Future Trends

Abstract

The digital age has made cyberspace indispensable for economic, social, and governmental functions, thus intensifying the critical need for robust cybersecurity. Our increasing dependence on digital platforms has exposed systems to a wide array of sophisticated cyber threats, including malware, phishing, distributed denial-of-service (DDoS) attacks, ransomware, and insider threats, often motivated by financial gain, political agendas, or espionage. These challenges underscore the urgent requirement for flexible and resilient cybersecurity strategies. Traditional signature-based and rule-based detection methods, while historically foundational, are now insufficient against modern cyber risks due to their inability to detect novel and evolving threats. Consequently, recent research has focused on utilizing advanced technologies like artificial intelligence (AI), machine learning (ML), deep learning (DL), and metaheuristic algorithms. These technologies excel at processing large datasets, identifying subtle anomalies, and predicting potential vulnerabilities before they are exploited. This paper assesses the performance of a new Intrusion Detection System (IDS) developed to combat these challenges and compares its efficacy against existing systems across various network environments. Using datasets that simulate diverse network attack scenarios—including a general Network Attack Dataset, an IoT-specific attack dataset (Rt-IoT), and the UNSW-NB15 dataset the proposed IDS yielded promising results. Specifically, the system achieved high accuracy, reaching 95.95% on the Network Attack Dataset, 99.99% on the Rt-IoT dataset, and 95.35% on the UNSW-NB15 dataset. Moreover, the system demonstrated strong performance in terms of precision, recall, and F1-score across these datasets. This paper reviews the evolution of threat detection techniques, contrasting traditional methods with state-of-the-art AI-driven approaches, and integrating the performance results of our proposed IDS. It identifies key research gaps, such as scalability issues, the need for adaptive AI models capable of responding to emerging threats, and the complexities of managing diverse datasets. The study aims to guide future research, emphasizing the development of adaptive and proactive cybersecurity solutions to address the constantly changing landscape of cyber threats.