Ai-Driven Cyber Threat Detection: Enhancing Security Through Intelligent Engineering Systems

Abstract

The rapid proliferation of digital technologies has significantly expanded the attack surface for cyber threats, making traditional security measures increasingly inadequate. Artificial Intelligence (AI)-driven cyber threat detection is emerging as a transformative approach to safeguarding digital ecosystems through intelligent engineering systems. This paper explores the integration of AI and machine learning (ML) techniques in cyber threat detection, focusing on how these advanced technologies enhance security, automate threat intelligence, and mitigate evolving cyber risks in real-time. AI-driven systems leverage sophisticated algorithms such as deep learning, neural networks, and anomaly detection models to identify and respond to cyber threats with unprecedented speed and accuracy. Unlike conventional rule-based security mechanisms, AI-powered threat detection continuously learns from vast datasets. This enables adaptive responses to new and sophisticated attack vectors, including zero-day exploits, ransomware, and advanced persistent threats (APTs). This paper discusses various AI methodologies, including supervised and unsupervised learning models, reinforcement learning, and hybrid AI frameworks that enhance threat identification and response automation. A key challenge in AI-driven cybersecurity is ensuring high detection accuracy while minimizing false positives, which can lead to operational inefficiencies. This study evaluates feature engineering techniques, adversarial AI threats, and explainable AI (XAI) approaches to enhance transparency in AI-based decision-making.

Additionally, the role of natural language processing (NLP) in analyzing threat intelligence feeds, social engineering detection, and predictive analytics for proactive threat prevention is examined. Furthermore, the research highlights real-world applications of AI-driven cyber defense in sectors such as finance, healthcare, and critical infrastructure, where cybersecurity breaches can have catastrophic consequences. The integration of AI in Security Operations Centers (SOCs) and its synergy with blockchain technology for enhanced authentication and data integrity is also discussed. Despite its potential, AI-driven cybersecurity faces limitations such as data privacy concerns, adversarial AI attacks, and the need for robust regulatory frameworks to ensure ethical AI usage. This paper presents a roadmap for future research in AI-driven threat detection, emphasizing the importance of collaboration between AI researchers, cybersecurity experts, and regulatory bodies to develop resilient and adaptive security solutions. By leveraging AI’s predictive and autonomous capabilities, organizations can fortify their cybersecurity posture, mitigate risks proactively, and enhance overall digital resilience. This research contributes to the ongoing discourse on intelligent cybersecurity solutions and provides insights into the next generation of AI-enhanced security frameworks.