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International Journal of Sensors, Wireless Communications and Control

Editor-in-Chief

ISSN (Print): 2210-3279
ISSN (Online): 2210-3287

Research Article

DRI Table Based Traffic-Behaviour Analysis Approach for Detection of Blackhole Attack

Author(s): Kapil Juneja*

Volume 10, Issue 1, 2020

Page: [79 - 93] Pages: 15

DOI: 10.2174/2210327909666190208154847

Price: $65

Abstract

Background: The blackhole infection can affect the collaborative communication in mobile networks. It is man-in-middle attack that seizes and deflects the route and avoids packet-forwarding in the network. The occurrence of collaborative-blackhole reduces the trust and trustworthiness over the network.

Objectives: A probabilistic and weighted analysis based protocol is proposed in this research for detection of cooperative blackhole nodes and generating the preventing route over the network. The aim of the work is to improve the communication reliability.

Methods: In this paper, the communication behaviour is analyzed under associated and probabilistic measures using Data Routing Information (DRI) table to discover the blackhole attack. It applies a dual check based on participation and communication constraints to estimate the node criticality. The evaluation is performed by neighbours and neighbour-on-neighbour nodes with weights and threshold specific decisions. These measures are evaluated through composite and integrated measures and presented as decision metrics. The parametric and probabilistic checks are conducted as a comprehensive evaluation within the proposed PSAODV (Probabilistic Secure Adhoc On Demand Distance Vector) protocol.

Results: The simulation of PSAODV protocol is conducted in NS2 environment on various scenarios with mobility, density and traffic type variations. The scenarios are defined with a higher density of blackhole nodes within the network. The adaptive weights are identified by simulating the network with different weight combinations. These weights are employed within the PSAODV protocol to configure it with the maximum benefits. The analytical evaluations are taken against AODV and SAODV protocols and identified the performance enhancement in terms of Packet Delivery Ratio (PDR) Ratio, delay, attack detection ratio parameters.

Conclusion: A significant improvement in attack detection is achieved by this proposed PSAODV protocol. The proposed protocol improved the reliability and effectiveness of mobile network.

Keywords: AODV, blackhole attack, DRI, mobile network, probabilistic, threats.

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[1]
Bedwal T, Saini A, Tayal AS. Black hole attack’s effect mobile ad-hoc networks (MANET). Int Conf Adv Comp Engr Appli Ghaziabad India . 2015; pp. 966-70.
[2]
Cai RJ, Chong PHJ. A neighborhood connectivity-based trust scheme to identify active black hole attacks. IEEE Int Conf Commun Sys 2014; pp. 543-8.
[3]
Mejaele L, Ochola EO. Effect of varying node mobility in the analysis of black hole attack on MANET reactive routing protocols Info Security South Africa. ISSA 2016; pp. 62-8.
[4]
Baadache A, Belmehdi A. Struggling against simple and cooperative black hole attacks in multi-hop wireless ad hoc networks. Comput Netw 2014; 73: 173-84.
[5]
Dorri A, Nikdel H. A new approach for detecting and eliminating cooperative black hole nodes in MANET. 7th Conf Info Knowl Tech (IKT) Urmia, 2015; 1-6.
[6]
Elsabrouty M, Shoukry A, Sherif A. A novel taxonomy of blackhole attack detection techniques in mobile ad-hoc network (MANET). IEEE 16th Int Conf Computat Sci Engr Sydney, 2013; 346-52.
[7]
Mejaele L, Ochola EO. Analysing the impact of black hole attack on DSR-based MANET: the hidden network destructor. 2nd Int Conf Info Security Cyber Forensics (InfoSec) Cape Town, 2015; 140-4.
[8]
Syed UH, Umar AI, Khurshid F. Avoidance of black hole affected routes in AODV-based MANET. Int Conf Open Source Sys Tech Lahore 2014; pp. 182-5.
[9]
Kumar J, Kulkarni M, Gupta D, Indu S. Secure route discovery in AODV in presence of blackhole attack. CSI Transact ICT 32-4, 2015; 91-8.
[10]
Gururaj HL, Ramesh B, Praveen KS. Comparative analysis of black hole attack in ad hoc network using AODV and OLSR protocols. Procedia Comput Sci 2016; 85: 325-30.
[11]
Er-rouidi M, Mouncif H, Hadadi BE, Moudni H. Modified AODV routing protocol to improve security and performance against black hole attack. Int Conf Info Tech Organiz Develop (IT4OD) Fez, 2016; 1-7.
[12]
Tamilselvi P, Babu CG. An efficient approach to circumvent black hole nodes in manets. Cluster Comput 2017; 1-9.
[13]
Nafaa M, Salim G, Abdelaziz AK. Survey of routing attacks and countermeasures in mobile ad hoc networks. UKSim 15th Int Conf Comp Model Simulat Cambridge 2013; 693-8.
[14]
Abdullah AH, Ismail AS, Haron H, Ngadi MA, Coulibaly Y, Mandala S. A review of blackhole attack in mobile adhoc network. 3rd Int Conf Instrument, Commun, Info Tech Biomed Engr (ICICI-BME) Bandung. 2013; 339-44.
[15]
Sharma S, Saini M, Dhama S. Black hole attack detection and prevention mechanism for mobile ad-hoc networks. 3rd Int Conf Comput Sustainable Global Develop (INDIACom) New Delhi,India. 2016; 2993-6.
[16]
Fogwell TE, Ochola EO. Location based analysis of AODV performance in the presence of black hole nodes Int Conf Adv Comput Commun Engr. (ICACCE) Durban,. 2016; pp. 23-8.
[17]
Welch I, Winston KG, von Mulert SJ. Security threats and solutions in MANETs: a case study using AODV and SAODV. J Netw Comput Appl 2012; 35(4): 1249-59.
[18]
Pattanayak BK, Panda N. Analysis of blackhole attack in AODV and DSR. Int J Electr Comp Engr (IJECE) 2018; 8(5): 3093-102.
[19]
Li G, Yan Z, Fu Y. A study and simulation research of blackhole attack on mobile adhoc network. IEEE Conf Commun Netw Security (CNS) Beijing. 2018; 1-6.
[20]
Khanna N, Sachdeva M. A comprehensive review of mitigation techniques for blackhole attack in AODV routing protocol in MANETs. Int J Adv Sci Tech 2018; 111: 11-22.
[21]
Khanna N, Sachdeva M. Critical review of techniques for detection and mitigation of co-operative blackhole attack in MANET. Int J Adv Sci Tech 2018; 110: 1-12.
[22]
Thebiga M, Pramila RS. A survey to monitor and defend against blackhole attacks in mobile adhoc networks. Int J Engr Tech 2018; 7(3.6): 337-42.
[23]
Muthaiah R, Kavitha T. Review on adaptations to AODV routing protocol to mitigate blackhole attacks in mobile ad hoc networks. Int J Mech Engr Tech(IJMET) 2017; 8(8): 406-10.
[24]
Nigam S, Bajpai A. Normalized scores for routes in MANET to analyze and detect collaborative blackhole attack. Int Conf Next Generat Comput Tech 2017; pp. 371-80.
[25]
Wakode NG. Defending blackhole attack by using acknowledge based approach in MANETs . Int Conf IoT Appli (ICIOT) Nagapattinam. 2017; pp. 1-6.
[26]
Shashwat Y, Pandey P, Arya KV, Kumar S. A modified AODV protocol for preventing blackhole attack in MANETs. Info Security J: A Global Persp 2017; 26(5): 240-8.
[27]
Thillaikarasi R, Mary S, Bhanu S. An efficient DSR protocol to detect blackhole attacks in WMN using cross layer approach. Wirel Pers Commun 2017; 95(3): 3477-92.
[28]
Dharmar V, Subramanian BR. Design and analysis of cross layer approach in the detection of blackhole attacks using adaptive neuro-fuzzy inference system. ARPN J Engr Applied Sci 2017; 12(13): 4032-9.
[29]
Wazid M, Das AK. A secure group-based blackhole node detection scheme for hierarchical wireless sensor networks. Wirel Pers Commun 2017; 94(3): 1165-91.
[30]
Singh O, Singh J, Singh R. MRWDP: multipoint relays based watch dog monitoring and prevention for blackhole attack. Mobile Adhoc Netw 2017; 95(6): 1391-409.
[31]
Kavitha T, Muthaiah R. Mitigation of blackhole attack using neighbor coverage. Int J Mech Engr Tech (IJMET) 2017; 8(8): 423-7.
[32]
Chaitanya K, Venkateswarlu S. Detection of blackhole & greyhole attacks in MANETs based on acknowledgement based approach. J Theoretical Applied Info Tech 2016; 89(1): 228-35.
[33]
Abdelshafy MA, King PJB. Resisting blackhole attacks on MANETs. 13th IEEE Annual Consum Commun Netw Conf (CCNC) Las Vegas, NV 2016; 1048-53.
[34]
Kamel MBM, Alameri I, Onaizah AN. STAODV: a secure and trust based approach to mitigate blackhole attack on AODV based MANET. IEEE 2nd Adv Info Tech, Electron Automat Cont Conf (IAEAC) Chongqing, 2017; 1278-82.
[35]
Hiremath PS, Anuradha T, Pattan P. Adaptive fuzzy inference system for detection and prevention of cooperative black hole attack in MANETs. Int Conf Info Sci Kochi 2016; pp. 245-51.
[36]
Er-Rouidi M, Mouncif H, El Hadadi B, Moudni H. Attacks against AODV routing protocol in mobile ad-hoc networks. 13th Int Conf Comp Graphics, Imaging Visualiz (CGiV) Beni Mellal, 2016; 385-9.
[37]
Panos C, Ntantogian C, Malliaros S, Xenakis C. Analyzing, quantifying, and detecting the blackhole attack in infrastructure-less networks. Comput Netw 2017; 113: 94-110.
[38]
Amutha S, Balasubramanian K. Secured energy optimized Ad hoc on-demand distance vector routing protocol. Comp Electr Engr 2017.
[39]
Khamayseh YM, Aljawarneh SA, Asaad AE. Ensuring survivability against black hole attacks in MANETS for preserving energy efficiency. Sustainable Comput: Informatics Sys 2017.
[40]
Ming-Yang S. Prevention of selective black hole attacks on mobile ad hoc networks through intrusion detection systems. Comput Commun 2011; 34(1): 107-17.
[41]
Zhang L. Effectiveness of HT-assisted sinkhole and blackhole denial of service attacks targeting mesh networks-on-chip. J Systems Archit 2018; 89: 84-94.
[42]
Jagadeesan S, Parthasarathy V. Design and implement a Cross Layer Verification Framework (CLVF) for detecting and preventing blackhole and wormhole attack in wireless ad-hoc networks for cloud environment. Cluster Comput 2018; 1-12.
[43]
Gupta AK, Mandal JK, Bhattacharya I. Mitigating selfish, blackhole and wormhole attacks in DTN in a secure, cooperative way. Int J Inform Comput Secur 2017; 9(1/2): 130-55.
[44]
Pham TND, Yeo CK. Detecting colluding blackhole and greyhole attacks in delay tolerant networks. IEEE Trans Mobile Comput 2016; 15(5): 1116-29.

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