• Er. Sumanpreet Kaur

Mobile Ad Hoc Networks (MANETs) are self-configuring, infrastructure-less networks that are highly susceptible to security threats due to their dynamic topology and decentralized nature. Among the most severe attacks in MANETs are Black-Hole and Gray-Hole attacks, where malicious nodes disrupt data transmission by dropping packets—completely in the case of Black-Hole attacks, or selectively in Gray-Hole attacks—leading to severe degradation of network performance. Traditional Intrusion Detection Systems (IDS) often struggle to detect these attacks effectively in such dynamic environments. To address this challenge, intelligent IDS frameworks that leverage machine learning, artificial intelligence, and data mining techniques have emerged as promising solutions. These systems can analyse node behaviour patterns, detect anomalies in routing activities, and adapt to new attack strategies in real-time. These abstract reviews the design principles and operational mechanisms of intelligent IDS in detecting and mitigating Black-Hole and Gray-Hole attacks in MANETs. It also highlights the advantages of intelligent IDS over conventional methods in terms of detection accuracy, adaptability, and resilience. The study concludes by identifying future directions, including lightweight IDS models for energy efficiency and the integration of trust-based and collaborative approaches for improved detection and prevention. In this researcher of this, this study presents a unique intrusion detection system that uses fuzzy and feed-forward neural networks to identify routing attacks in wireless sensor networks. Comparing the suggested model to other methods such as support vector machine (SVM), decision tree (DT), and random forest (RF) models, the testing findings show that it achieves an average detecting rate of 97.7% and a maximum detecting accuracy of 98.9%.

Mobile Ad Hoc Networks (MANETs), Intrusion Detection System (IDS), Black-Hole Attack, Gray-Hole Attack, Intelligent Security Systems, Machine Learning, Anomaly Detection, Routing Attacks, Network Security, Wireless Ad Hoc Networks and Trust-Based Detection.

"INTELLIGENT INTRUSION DETECTION SYSTEMS FOR BLACK-HOLE AND GRAY- HOLE ATTACKS IN MANETs", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.f164-f173, October-2022, Available :http://www.jetir.org/papers/JETIR2210531.pdf

"INTELLIGENT INTRUSION DETECTION SYSTEMS FOR BLACK-HOLE AND GRAY- HOLE ATTACKS IN MANETs", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppf164-f173, October-2022, Available at : http://www.jetir.org/papers/JETIR2210531.pdf