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Mobile Ad hock Networks (MANETs) are currently used for developing the privacy and accuracy of modern networks. Furthermore, MANET applications are fit to be data-oriented systems, that introduce a secure and more robust data transmission protocol making it a topmost priority in the design. The lack of infrastructure in the existence of dynamic topology as well as limited resources of MANET is a major challenge facing those interested in the field. Further, the nonexistence of a formerly authorized trust relationship within the connected nodes produces instability of the detection process in MANETs. Basically, by adding adapted LEACH routing protocol to MANET, enhancement of the preserved nodes vitality will be achieved, moreover, the load balancing with data loss reduction provides MANET ability to tracks along with shortest and limited paths. This paper proposes a newly developed detection scheme for both active and passive black-hole attacks in MANETs. Moreover, the scheme deals with assessing a group of selected features for each node-based AdaBoost-SVM algorithm. These features are collected from cluster members nodes based on Ad hoc On-demand Multipath Distance Vector (OMDV) with LEACH routing protocol clustering approaches. Although SVM is considered a more stable classifier, there are great influences of the AdaBoost weight adaption algorithm to enhance the classification process in terms of strengthening the weights of extracted features. This hybrid algorithm is essential for active black-hole attacks as well as for identifying passive black-hole attacks in MANET. The proposed scheme is tested against the effect of mobility variation to determine the accuracy of the detection process including the routing overhead protocol. The experimental results investigated that the accuracy of detecting both active and passive black-holes attacks in MANET reached 97% with a promising time complexity for different mobility conditions. Moreover, the proposed scheme provides an accurate decision about malicious vs benign node dropping behavior using an adjustable threshold value. [ABSTRACT FROM AUTHOR]

The existing multi-class classification algorithms based on support vector machine (SVM) generally decompose the original problem into smaller subproblems. However, the decomposition approach raises the problems of unreliable and unbalanced training of individual classifiers. The aim of this work is to alleviate these problems. In this paper, a novel multiclass classification algorithm based on one-class SVM is presented. The distinguishing point of our proposed algorithm is that the algorithm solves a one-class classification problem rather than decomposing the problem into several smaller subproblems. The proposed algorithm solves a multi-class classification problem by expanding the training input vector with the class label and constructing a one-class SVM with the expanded input vectors. In the test phase, test input vectors are assessed by fit to the decision boundary under the assumption of belonging to a class. We conducted experiments on several real-world benchmark datasets. Experimental results showed that the proposed algorithm outperforms other SVM-based multi-class classification algorithms. [ABSTRACT FROM AUTHOR]