Countering of Black Hole Attack on Manet with AODV Protocol

Security in mobile ad-hoc network (MANET) is the most serious issue impacting performance of network. In general, routing methods is one of the complicated and exciting analysis places. In black hole attack, a harmful node uses its routing technique to be able to promote itself for having the quickest direction to the place node or to the bundle it wants to identify. In this research, performance of one of the most efficient solutions for preventing single black hole attack in MANET using AODV routing protocol will be investigated in terms of packet delivery ratio, packet loss percentage, average end-to-end delay, and route request overhead. This chapter describes the introduction, background of the study, research objectives and questions, the scope of the study and its primary objectives.

Security Techniques in MANET: A Study

MANET (mobile ad hoc network) is considered to be a network with no centralized control. This network typically faces two major challenges related to routing and security. Both these issues affect the performance of this network to a large extent. The black hole attack belongs to the category of active attacks that are launched to reduce the network throughput and other parameters. The research works carried out in the past used different techniques to isolate malicious nodes, but with the inclusion of extra hardware and software tools. The various techniques for the security in MANET are analyzed in terms of certain parameters. Keywords: MANET, Black Hole, Security Techniques

Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET

Mobile Ad-Hoc Networks (MANETs) due to their reconfigurable nature are being integrated into new and futuristic knowledge such as Internet of Things (IoT), cloud, reconfigurable networks, etc. To attain such credibility of integration, the routing protocols associated with these mobile nodes have to connect, perform and facilitate routing that offers a high level of security and resistance to all possible threats and security issues that may emanate in the network. One of the solutions used to maintain network security is intrusion detection systems (IDSs). This article primarily emphasis on the network's susceptibility to a suction assault known as a black hole attack. The investigations about the employment of intelligent agents called Honeypot Agent-based detection scheme (HPAS) with Long-Short Term Memory (LSTM) in identifying such assaults. Hence, the proposed method is named HPAS-LSTM, where honeypots are roaming virtual software managers that create Route Request (RREQ) packets to attract and entrap black hole attackers. Extensive model results utilizing the ns-2 simulator are used to demonstrate the presence of the suggested detection technique. The simulation outcomes demonstrate that the suggested technique outperforms current black hole detection methods in terms of throughput (TH), packet loss rate (PLR), packet delivery ratio (PDR), and total network delay (TND).