Leveraging AI and Machine Learning for Cyber Threat Analysis

Abstract

This paper explores how AI and Machine Learning is becoming a key participant in cybersecurity's emergence and constantly shifting nature. It disillusioned traditional forms of detection, such as signature-based detection and rule-based systems, with the idea that they are suitable for dealing with new complex threats like zero-day vulnerabilities, APTs, and social engineering attacks. AI and ML provide enhanced features for accurate time operation, real-time threat identification tools, anomaly identification tools, behavioral tools, and predictive analysis, and therefore, have become helpful in the modern context and central to the cybersecurity architecture of many organizations. The paper fleshes out the strengths of AI in intrusion detection, dynamic malware analysis, and phishing detection, among others. The following AI methods are covered in detail: Supervised and unsupervised learning algorithms are used
in processing datasets, and autoencoders are used. Deep learning models are suitable for processing big data in real-time. The issues of AI incorporating false positive outcomes, adversarial AI, and data quality problems are also addressed, along with ethical and privacy considerations. The roles of automated incident reporting and self-learning security systems, as forecasted for AI's contributions in managing cyber threats, are designated as crucial. Finally, the paper concludes by stressing the need for organizations to adopt AI solutions for the preemptive approach in the cybersecurity domain.