Abstract:
A mobile ad-hoc network (MANET) is an infrastructure-less wireless network and
self-organized. During communication MANETs don’t use any proper infrastructure so
MANET is prone to various sorts of attacks like distributed denial-of-service(DDoS),
Bot,Secure Socket Shell(SSH-Bruteforce), and FTP-BruteForce.To provide adequate se curity against multi-level attacks detection-based schemes should be incorporated ad ditionally to traditionally used prevention techniques because prevention-based tech niques cannot prevent the attacks from compromised internal nodes. In this paper, a
hybrid machine learning model with a new feature selection method is proposed for
better performance of the Intrusion Detection System. In this proposed model, the In trusion Detection System is built with a combination of supervised and unsupervised
machine learning models.The obtained results show that the proposed intrusion detec tion is effective in detecting the DDoS, Bot, SSH-Bruteforce, and FTP-BruteForce type
attacks with a high detection rate. The results show KNN (99.99% accuracy), K Means
Clustering(99.99% accuracy), Decision Tree (99.99% accuracy and the hybrid also
99.99% accuracy . Finally, the paper concludes with a variety of future research direc tions within the design and implementation of intrusion detection systems for MANETs
