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Jamming Classification Algorithms in Mobile Ad-Hoc Network

The latest technologies in Mobile Ad-Hoc Network (MANET) allows networking infrastructure to be set up quickly and easily. Every node in MANET plays a role as a router that creates a sustainability access regardless of time or location. On the other hand, a study found that eighty one percent of attacks occurs at the physical and MAC layers of the protocol stack. Knowing the types and characteristics of jammers will enable researchers to develop defense techniques that can prevent jammer attacks. Thus, this paper proposes the development of algorithms to classify jamming attacks using a set of metrics on the physical layer and MAC in MANET. The methodology consists of four main stages. The first stage is to apply reverse engineering method to obtain the specific patterns of individual jammers. This creates the jamming classification metrics with threshold values. The second stage is to classify individual jammers according to the specific pattern and characteristics as defined in jamming classification metrics. The third stage involves development of Max-Min Rule-Based Classification Algorithm. Final stage is testing stage where accuracy test was conducted to evaluate the effectiveness of proposed algorithms based on classier model using SVM and WEKA. Results show that data with normalization and scaling for polynomial kernel with cross validation presented the highest accuracy assessment which is 76.142% compared to other classifiers. Therefore, jammer attack classification study will be provided with better knowledge and technology to develop more robust de-fence techniques in MANET.