Detection and Prevention from Black Hole Attack in AODV Protocol for MANET

Sharma, Abhilasha; Singh, Rajdeep; Pandey, Ghanshyam

doi:10.5120/7764-0837

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…It is defined as the ratio of the total number of received routing packets to the number of received data packets [30] and is given by: NRL = ∑ total number of routing packets received ∑ total number of data packets received…”

Section: Normalized Routing Loadmentioning

confidence: 99%

Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV

Al-Shabi

2022

Int. j. commun. netw. inf. secur.

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The so-called Black Hole Attack is among the most perilous and widespread security attacks in MANET nets, researchers have been tasked with developing strategies to detect it. Two of these methods are the Intrusion Detection System AODV (IDSAODV) as well as the Extended AODV. The present paper attempts to investigate the impact of a Black Hole Attack on the functionality of the network in the existence of single or more attackers. It also evaluates the Extended AODV and IDSAODV in a net in order to see how effectively they could detect and mitigate the attack. For the aim of evaluating its performance, the researchers utilized Throughput, Normalized Routing Load (NRL), and Packet Delivery Ratio (PDR). The comprehensive simulation results show that the IDSAODV application decreased the effect of the attacks. However, it raised the rate of packet delivery to sixty eight percent at the identical time. Reverse AODV, on the other hand, provided superior outcomes, with a PDR of 100%, but also resulted in an exceedingly higher NRL than the IDSAODV. Likewise, the simulation findings demonstrated that the attacking node's position tormented the IDSAODV's functionality.

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Section: Normalized Routing Loadmentioning

confidence: 99%

Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV

Al-Shabi

2022

Int. j. commun. netw. inf. secur.

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“…This attack makes a harmful node falsely promotes best paths to the destination node in the process of path finding or in the route update messages. The purpose of the harmful node is to cause difficulty for route investigating procedure [6] or to suspend the data(information) packets which are being sent to the target node which is concerned.…”

Section: Black Hole Attackmentioning

confidence: 99%

Security Attacks in Wireless Sensor Networks

Gurajada¹,

Rajaputra²,

Gogineni³

et al. 2018

IJET

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In today’s world, Security is the prime concern for each application. Network security and Computer protection are the serious issues. A broad range of Wireless Sensor Networks (WSNs) applications[1][15] are widely used in military, researches etc. The key subject of computing is to provide network security several types of attacks are increasing day by day. The attacks present in different layers are produced due to the susceptibilities present in the layers. The Seven layers in OSI model are formulated to convey the information from highest layer to lowest layer. Broadcast of data gets modified according to the layer in which it lies. Individually every layer is weak to different types of attacks. This paper models about the different attacks present in each layer of OSI.

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“…MANET is vulnerable to different types of attacks that affect its functionality and connectivity [5,6]. One of the nodes attacks is the black-hole attack is seriously declining the performance of the network, due to the malicious node activity of dropping all incoming packets [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Avoid Suspicious Route of Blackhole Nodes in MANET’s: Using A Cooperative Trapping

Esaid¹,

Agoyi²

2023

Computer Systems Science and Engineering

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Mobile Ad hoc Network (MANET) is decentralized wireless network and can communicate without existing infrastructure in many areas. MANET is vulnerable to various attacks that affect its performance such as blackhole attack. Blackhole attacker, inject fault routing information to persuade the source node to select the path with malicious node as the shortest path. To eliminate malicious nodes from launching any collaborative attack. A cooperative Trapping Approach (CTA) was proposed based on modifying Ad-hoc On-demand Distance Vector (AODV) routing protocol and trapping the malicious nodes by responding to the trap request message. The approach aims to eliminate and rule out both single and collaborative malicious blackhole nodes from any attack. The approach realizes a backward tracking mechanism to perform the elimination process. The proposed algorithm (CTA) was executed using NS-2 network simulator. The performance metrics that has been considered to evaluate the performance of the proposed algorithm such as throughput, end to end delay, packet delivery ratio, and consuming energy. The experimental results have shown the performance metrics of the proposed approach outperformed other state of at algorithms.

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Detection and Prevention from Black Hole Attack in AODV Protocol for MANET

Cited by 9 publications

References 4 publications

Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV

Performance Ameliorations of AODV by Black Hole Attack Detection Utilizing IDSAODV as Well as Reverse AODV

Security Attacks in Wireless Sensor Networks

Avoid Suspicious Route of Blackhole Nodes in MANET’s: Using A Cooperative Trapping

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