Invincible AODV to detect black hole and gray hole attacks in mobile ad hoc networks

Abstract

SummaryToday's communication world is majorly driven by mobile nodes that demand wireless systems for their data relay. One such network is mobile ad hoc network, which is a purely wireless network with which communication is feasible instantly without any aid of preexisting infrastructure; due to this magnificent feature, it has a wide variety of applications. Mobile ad hoc network hinges on cooperative nature of the mobile nodes for relaying data. But at the same time, nodes relaying data for others may compromise, leading to various security attacks. Two main security attacks that drastically bring down the performance of mobile ad hoc network are black hole and gray hole attacks. In this paper, we propose 2 versions of invincible Ad hoc On‐Demand Distance Vector protocol to detect black hole and gray hole nodes that have bypassed preventive mechanism during route discovery process. First is the basic version, which is based on node‐to‐node frame check sequence tracking mechanism, and second is the enhanced version, which is based on signed frame check sequence tracking mechanism. They create a deterrent environment addressing all kinds of black and gray hole attacks. They also provide reliable data transmission to all the nonmalicious nodes in the network by using end‐to‐end authentication mechanism. Simulation results show better performance in packet delivery ratio when compared with other contemporary solutions while addressing all kinds of black and gray hole attacks. It shows significant improvement in end‐to‐end delay and normalized routing load over Ad hoc On‐Demand Distance Vector under black hole or gray hole attacks and also shows better throughput and packet delivery ratio than the existing solution.

An Efficient Signal Processing Model for Malicious Signal Identification and Energy Consumption Reduction for Improving Data Transmission Rate

“…Sandhya Venu and Avula [12] introduced a Secure Objective Reputation Based Incentive (SORI) framework which controls the malicious signals. Their technique comprised of neighbors checking the neighbor signals for their signal sending conduct and a neighbor signal list comprising of complexity of all the neighbor signals are being kept up by every signal in the framework.…”

“…The Ad-Hoc Network is a transient, wirelessly connected network with no fixed infrastructure made of nodes [11]. Network security plays a key role here and is no longer effective and sufficient in traditional approach to ensure network protection through firewalls and encrypted software [12]. Sensitive information can be shared in the ad hoc networks, so care need to be taken to provide security to the data from attackers or malicious signals that degrade the performance levels [13].…”

Neural Networks and Blackhole Optimization in a Cooperative IDS for IoT

“…Because of the free or movable nature of the nodes in the network, it is feasible for a variety of assaults to be carried out, including the blackhole attack [10], the greyhole attack [11], and the selective packet drop attack [12]. Attacks known as black holes and attacks that discard some packets [13] are related types of attacks in which just some of the data packets in a transmission are lost. Because black hole nodes act normally throughout the route finding phase, they are not immediately noticeable as mallicious nodes [14].…”