| Title | Classification of Misbehaving nodes in MANETS using Machine Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | S, Kanthimathi, Prathuri, Jhansi Rani |
| Conference Name | 2020 2nd PhD Colloquium on Ethically Driven Innovation and Technology for Society (PhD EDITS) |
| Date Published | nov |
| Keywords | Ad hoc networks, Analytical models, BPNN, compositionality, machine learning, malicious node, MANET security, Metrics, Mobile Ad-Hoc Network (MANET), mobile computing, Neural networks, pubcrawl, Resiliency, Support vector machines, SVM, Throughput |
| Abstract | Classification of Misbehaving Nodes in wireless mobile adhoc networks (MANET) by applying machine learning techniques is an attempt to enhance security by detecting the presence of malicious nodes. MANETs are prone to many security vulnerabilities due to its significant features. The paper compares two machine learning techniques namely Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN) and finds out the best technique to detect the misbehaving nodes. This paper is simulated with an on-demand routing protocol in NS2.35 and the results can be compared using parameters like packet Delivery Ratio (PDR), End-To-End delay, Average Throughput. |
| DOI | 10.1109/PhDEDITS51180.2020.9315311 |
| Citation Key | s_classification_2020 |
Classification of Misbehaving nodes in MANETS using Machine Learning Techniques