Next -Generation Network Intrusion Detection: Innovations and Future Directions

Network Intrusion Detection Systems (NIDS) play a vital role in modern cybersecurity by identifying and mitigating cyber threats such as malware, denial-of-service (DoS) attacks, and unauthorized access. However, the increasing complexity of networks, the growing adoption of encryption, and sophisticated evasion techniques pose significant challenges to NIDS effectiveness. Traditional signature-based detection methods struggle with high false positive and false negative rates, while anomaly-based approaches face difficulties in distinguishing legitimate traffic from malicious activities. Additionally, the rapid evolution of zero-day attacks and adversarial machine learning further complicates intrusion detection. This paper explores the key challenges faced by NIDS, including encrypted traffic analysis, evasion tactics, scalability in high-speed networks, and the lack of high-quality datasets for training detection models. We review existing solutions, including signature-based, anomaly-based, and hybrid approaches, as well as machine learning and deep learning techniques. Furthermore, we discuss future directions such as AI-driven self-learning NIDS, privacy-preserving analysis of encrypted traffic, quantum-resistant security mechanisms, and distributed edge-based architectures. By integrating cutting-edge technologies, NIDS can enhance real-time threat detection, reduce false positives, and improve scalability. This study aims to contribute to the development of more adaptive, intelligent, and efficient intrusion detection systems capable of defending against the ever-evolving landscape of cyber threats.

کلیدواژه ها:

NIDS ، Cybersecurity ، ML ، Cybersecurity Challenges