Performance analysis of black hole and worm hole attacks in MANETs

A Mobile Ad Hoc Network MANET is composed of a freely and mobility set of mobile nodes. They form a temporary dynamic wireless network without any infrastructure. Since the nodes act as both host and router in their communication, they act as a router provide connectivity by forwarding data packets among intermediate nodes to the destination. The routing protocol is used to grove their communication and connectivity as example, the Ad On-demand distance vector (AODV) routing protocol. However, due to the lack of security vulnerabilities of routing protocols and the absence of infrastructure, MANET is vulnerable to various security threats and attacks. This paper examines the impact of two types of attacks on AODV routing protocol using Network Simulator version 2 (NS2) environment. These attacks are Blackhole and Wormhole Attacks. The aim of both of them is to prevent data packets to reach the destination node and dropping all the traffic.

Quasi-reflection learning arithmetic optimization algorithm firefly search for feature selection

With the whirlwind evolution of technology, the quantity of stored data within datasets is rapidly expanding. As a result, extracting crucial and relevant information from said datasets is a gruelling task. Feature selection is a critical preprocessing task for machine learning to reduce the excess data in a set. This research presents a novel quasi-reflection learning arithmetic optimization algorithm - firefly search, an enhanced version of the original arithmetic optimization algorithm. Quasi-reflection learning mechanism was implemented for enhancement of population diversity, while firefly algorithm metaheuristics were used to improve the exploitation abilities of the original arithmetic optimization algorithm. The aim of this wrapper-based method is to tackle a specific classification problem by selecting an optimal feature subset. The proposed algorithm is tested and compared with various well-known methods on ten unconstrained benchmark functions, then on twenty-one standard datasets gathered from the University of California, Irvine Repository and Arizona State University. Additionally, the proposed approach is applied to the Corona disease dataset. The experimental results verify the improvements of the presented method and their statistical significance