scholarly journals A Proposed AODV Black hole Detection Model using the Fuzzy Inference Method in MANET Topology

2021 ◽  
Vol 9 (6) ◽  
pp. 690-694
Keyword(s):  
Black Hole ◽  
Fuzzy Inference ◽  
Detection System ◽  
Parameter Extraction ◽  
Security Measures ◽  
Inference Method ◽  
Black Hole Attack ◽  
Detection Model ◽  
Mobile Adhoc Network ◽  
Fuzzy Parameter

Mobile Adhoc Network (MANET)as a network of mobile routers that self-configures has raised some security concerns which one of them has been the Black Hole Attack (BHA). Studies have shown various detection and mitigation techniques in line with the black hole attack of MANET technology. However, some of the proposed models in this direction have paid less attention to how fuzzy systems could be integrated in Adhoc On-Demand Distance Vector (AODV)protocol, to further guide its detection abilities against a possible black hole attack. Hence the study proposed a model that integrates the Fuzzy Inference Method (FIM)on the existing AODV security measures in order to strengthen further its detection metrics. Also,an Intrusion Detection System (IDS) was introduced for an early check against possible BHA along with the Fuzzy Parameter Extraction module which performs all necessary parameter checks before passing it on to the FIM. The parameters into consideration were adapted from the study of Narang (2013), with this study modifying further its proposed algorithm using the added concepts earlier mentioned. The proposed model presented tends to perform better than the existing models in this direction with evidence from the theoretical underpinnings of the functionality of the added modules in the proposed system

scholarly journals Detection and Interception of Black Hole Attack with Justification using Anomaly based Intrusion Detection System in MANETs

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2392-2398
Keyword(s):  
Black Hole ◽  
Nearest Neighbor ◽  
Detection System ◽  
Support Vector ◽  
Delivery Ratio ◽  
K Nearest Neighbor ◽  
Black Hole Attack ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Active Attacks

Mobile adhoc network, a derivative of the adhoc network is sensitive to heterogeneous forms of attacks in particular passive and active attacks. Black hole attack is one such continually prevailing threat in mobile adhoc networks (MANETs), where specific nodes operate spitefully in the course of data transmission. Throughout this work, we intend to present an effectual approach to detect and intercept this attack taking into account Dynamic MANET on-demand (DYMO) routing protocol. This work presupposes working in three modulesplanting, detection and ultimately the interception against the black hole attack. An IDS is initiated on the notion of machine leaning using MATLAB software. A relative scrutiny of IDS grounded on classifiers like K-Nearest Neighbor, Support Vector Machine, Decision tree and neural network is also conducted to make it certain that the best feasible classifier is settled on for administering the IDS. The analysis of the put forward work is subsequently accomplished taking miscellaneous metrics covering packet drop rate, average transmission delay, Packet Delivery Ratio along with throughput.

scholarly journals A Survey on Assorted Subsisting Approaches to Recognize and Preclude Black Hole Attacks in Mobile Adhoc Networks

2020 ◽  
Vol 14 (01) ◽  
pp. 96 ◽  
Author(s):  
Thebiga M ◽  
Suji Pramila
Keyword(s):  
Black Hole ◽  
Routing Protocol ◽  
Target Node ◽  
Black Hole Attack ◽  
Mobile Adhoc Networks ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Pros And Cons ◽  
Adhoc Networks ◽  
Entire Network

<p>Ensuring collateral is the most substantial snag in Mobile Adhoc Networks which crash the efficacy of the network. Without regard to all different networks, the Mobile Adhoc network is stuffed with more safety hindrances and the Adhoc on Demand Vector Routing Protocol is more comprehensively utilized protocol in MANETS. This type of network is more exposed to assorted number of attacks and among those, the black hole attack and its variant pull off critical detriment to the entire network .In this type of attack, named black hole attack, the noxious node utilizes its routing principles, with the view to annunciate itself, that it has the briefest route to the target node. In this paper, we have investigated all the subsisting techniques and graded the solution with a table to understand their pros and cons.<strong></strong></p>

scholarly journals Efficient Detection of Black Hole attack in Mobile Adhoc Networks using a TRUST based Scheme

2019 ◽  
Vol 8 (4) ◽  
pp. 2740-2744
Keyword(s):  
Black Hole ◽  
Central Administration ◽  
Black Hole Attack ◽  
Mobile Adhoc Networks ◽  
Mobile Adhoc Network ◽  
Adhoc Network ◽  
Efficient Detection ◽  
Security Problem ◽  
Adhoc Networks ◽  
Fixed Structure

In this paper, the security problem of black hole node in mobile adhoc networks (MANET) is discussed. Mobile adhoc network is a kind of network in which there is no fixed structure of the network. All the nodes are mobile i.e. they are free to move where ever they want to move. Mobility of the nodes and lack of any central administration in the network leads to complex security problem and black hole attack is one such security problem. In this paper, some of the best work which are already exist in this field and the limitations are disused in brief. A Relative TRUST based approach to eradicate the problem of black hole is proposed in this paper.

scholarly journals Remove the black-hole attack in mobile ADHOC network using Threshold Based Technique

2016 ◽  
Vol 15 (12) ◽  
pp. 7316-7321
Author(s):  
Sanjay Yadav ◽  
Kaptan Singh
Keyword(s):  
Black Hole ◽  
Routing Protocol ◽  
Selection Process ◽  
Delivery Ratio ◽  
Black Hole Attack ◽  
Node Selection ◽  
Hop Count ◽  
Reference Node ◽  
Mobile Adhoc Network ◽  
Aodv Routing Protocol

In this paper enhanced the AODV routing protocol for the prevention of black hole attack. The enhanced AODV routing protocol based on the principle of thresholding. The thresholding using the concept of reference node selection process. The reference node selection process creates two group of node one is path altered group and other is stable path with sensibility. The sensibility path estimates the hop count and packet sequence number. If the number of hop count is not changed or the sequence of packet is also not lost is called sensitivity path of network. The function of threshold generated by the node distance formula based on the Euclidean distance derivation. The enhanced AODV protocol simulate in NS2.34 simulators and measure some standard parameter such as PDR, throughput, overload and E2E delivery ratio.

scholarly journals Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Gholamreza Farahani
Keyword(s):  
Black Hole ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Nearest Neighbor ◽  
Fuzzy Inference ◽  
Cluster Head ◽  
Delivery Ratio ◽  
K Nearest Neighbor ◽  
Black Hole Attack ◽  
Mobile Ad Hoc

The characteristics of the mobile ad hoc network (MANET), such as no need for infrastructure, high speed in setting up the network, and no need for centralized management, have led to the increased popularity and application of this network in various fields. Security is one of the essential aspects of MANETs. Intrusion detection systems (IDSs) are one of the solutions used to ensure security in this network. Clustering-based IDSs are very popular in this network due to their features, such as proper scalability. This paper proposes a new algorithm in MANETs to detect black hole attack using the K-nearest neighbor (KNN) algorithm for clustering and fuzzy inference for selecting the cluster head. With the use of beta distribution and Josang mental logic, the trust of each node will be calculated. According to the reputation and remaining energy, fuzzy inference will select the cluster head. Finally, the trust server checks the destination node. If allowed, it notifies the cluster head; otherwise, it detects the node as a malicious node in the black hole attack in each cluster. The simulation results show that the proposed method has improved the packet loss rate, throughput, packet delivery ratio, total network delay, and normalized routing load parameters compared with recent black hole detection methods.

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