AI-Powered Systems for Cybersecurity: Techniques and Applications for Threat Detection

Abstract

The rapid advancement of artificial intelligence (AI) has revolutionized cybersecurity, enabling automated threat detection, real-time anomaly detection, and predictive analytics to counter evolving cyber threats. AI-powered cybersecurity systems leverage machine learning, deep learning, and natural language processing to analyze vast amounts of data, identify malicious activities, and mitigate potential security breaches before they escalate. These systems enhance traditional cybersecurity frameworks by reducing false positives, improving detection accuracy, and enabling adaptive threat response mechanisms. Key applications include intrusion detection, malware analysis, phishing prevention, and behavioral analytics for insider threat detection (Russell & Norvig, 2021).

Machine learning algorithms, such as support vector machines (SVM), decision trees, and artificial neural networks, play a crucial role in detecting sophisticated attacks, including zero-day vulnerabilities and advanced persistent threats (APTs) (Goodfellow et al., 2016). AI-powered threat intelligence systems use big data analytics to predict cyberattacks and enhance digital forensics capabilities (Buczak & Guven, 2016). However, challenges such as adversarial AI attacks, data privacy concerns, and algorithmic biases present significant obstacles to the widespread adoption of AI-driven cybersecurity solutions (Papernot et al., 2018).

This research explores AI-based techniques for threat detection, evaluates their effectiveness in cybersecurity, and discusses the ethical implications of AI-driven security measures. By integrating AI with blockchain technology, quantum computing, and federated learning, future cybersecurity frameworks can enhance resilience against cyber threats and ensure robust digital protection in an era of increasing cyber risks (Sharma et al., 2020).