Intrusion Detection Systems (IDS) for Network Security: Emerging Trends and Future Directions

Abstract

With the rapid evolution of cyber threats and increasing complexity of network architectures, Intrusion Detection Systems (IDS) have become a crucial component of modern network security. IDS monitor, analyze, and respond to malicious activities in real-time, helping organizations prevent data breaches and ensure the integrity of their systems. Traditional IDS approaches such as signature-based and anomaly-based methods have demonstrated effectiveness but face limitations in detecting zero-day attacks and sophisticated threats. Recent advancements in artificial intelligence (AI), machine learning (ML), and deep learning (DL) have revolutionized IDS capabilities, enabling more accurate and adaptive detection mechanisms. This paper explores the emerging trends in IDS, highlighting the integration of AI techniques, cloud based IDS solutions, and Blockchain technologies. It also discusses the challenges associated with IDS implementation and presents future directions for enhancing IDS performance to safeguard networks against evolving threats.

Keywords: Intrusion Detection Systems, Network Security, Artificial Intelligence, Anomaly Detection, Future Directions.