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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

Random Black Hole Attack Modelling and Mitigation Using Trust- Confidence Aware OLSR in MANETs for Private Data Communications

Author(s): Kirti A. Adoni*, Anil S. Tavildar and Krishna K. Warhade

Volume 10, Issue 2, 2020

Page: [112 - 122] Pages: 11

DOI: 10.2174/2210327909666190327173502

Price: $65

Abstract

Background and Objective: Random Black Hole (BH) attack significantly degrades MANET’s performance. For strategic applications, the performance parameters like Packet Delivery Ratio, Routing Overheads, etc. are important. The objectives are: (a) To model random BH attack, (b) To propose a routing strategy for the protocol to mitigate random BH attack, (c) To evaluate and compare the network performance of modified protocol with the standard protocol.

Methods: The random BH attack is modelled probabilistically. The analysis is carried out by varying Black Hole Attack (BHA) time as Early, Median, Late occurrences and mix of these three categories. The blocking performance is also analysed by varying the percentages of malicious presence in the network. Normal Optimized Link State Routing (OLSR) protocol is used to simulate the MANET performance using a typical medium size network. The protocol has then been modified using Trust- Confidence aware routing strategy, named as TCAOLSR, with a view to combat the degradations due to the random BH attack.

Results: The random behavior of Black Hole attack is analyzed with all the possible random parameters, like deployment of mobile nodes, number of malicious nodes and timing instances at which these nodes change their state. From the results of individual type- Early, Median and Late, it is observed that the TCAOLSR protocol gives stable performance for Packet Delivery Ratio (PDR) and Routing Overheads (RO), whereas for OLSR protocol PDR gradually reduces and RO increases. For individual and mix type, Average Energy Consumption (AEC) per node increases marginally for TCAOLSR protocol. For the mix type, PDR for TCAOLSR is 40-60% better whereas RO for TCAOLSR is very less compared to OLSR protocol. The efficacy of the TCAOLSR protocol remains stable for different categories of BH attack with various percentages of malicious nodes compared to OLSR with the same environment.

Conclusion: Simulations reveal that the modified protocol TCAOLSR, effectively mitigates the network degradation for Packet Delivery Ratio and Routing Overheads considerably, at the cost of a slight increase in Average Energy Consumption per node of the network. Efficacy of the OLSR and TCAOLSR protocols has also been defined and compared to prove robustness of the TCAOLSR protocol.

Keywords: Black hole attack, OLSR framework, probabilistic modeling, strategic networks, trust-confidence aware routing, packet delivery ratio.

Graphical Abstract
[1]
Clausen T, Jacquet P. Trust model based on bayesian statistical method for aomdv in manet. BMC Med Inform Decis Mak 2003; 69(1): 172-81.
[2]
Rajaram A, Palaniswami S. Malicious node detection system for mobile ad hoc networks; (IJCSIT). BMC Med Inform Decis Mak 2010; 1(2): 77-85.
[3]
Tseng FH, Chou LD, Chao HC. A survey of black hole attacks in wireless mobile ad hoc networks. Hum Cent Comput Info 2011; 1(4): 1-16.
[4]
Aarohi S. Strategic modelling of malicious behavior due to detour attack in olsr protocol in manet. Recent Res Elect Eng 2014; 2014: 276-83.
[5]
Khaled AAO. the impact of node misbehavior on the performance of routing protocols in manet. Int J Mach Learn Comput (IJCNC) 2016; 8(2): 103-12.
[http://dx.doi.org/10.5121/ijcnc.2016.8209]
[6]
Geetha S, Geetha RG. Trust model based on bayesian statistical method for aomdv in manet. Appl Clin Inform 2014; 69(1): 172-81.
[7]
Janakiraman S, Rajendiran M. A futuristic trust coefficient-based semi-markov prediction model for mitigating, selfish nodes in MANETs. EURASIP J Wirel Commun Netw 2015; 158: 1-13.
[http://dx.doi.org/10.1186/s13638-015-0384-4]
[8]
Satoshi K, Hidehisa N, Nei K, Abbas J, Yoshiaki N. Detecting blackhole attack on aodv-based mobile ad hoc networks by dynamic learning method. Int J Netw Secur 2007; 5(3): 338-46.
[9]
Su MY. Prevention of selective black hole attacks on mobile ad hoc networks through intrusion detection systems. Comput Commun 2011; 34: 107-17.
[http://dx.doi.org/10.1016/j.comcom.2010.08.007]
[10]
Kaurav A, Kumar KA. Detection and prevention of black hole attack in wireless sensor network using ns-2.35 simulator. Eng Inform Technol IJSR CSEIT 2017; 2(3): 717-22.
[11]
Zougagh H, Toumanari A, Latif R, Elmourabit N, Idboufker Y. Modified olsr protocol for detection and prevention of packet dropping attack in manet. Int J Comput Appl 2014; 100(17): 32-8.
[12]
Shreenath KN, Manasa VM. Black hole attack detection in zone based wsn. Int J Curr Trends Sci Tech 2017; 5(4): 148-51.
[13]
Otoum S, Kantarci B, Mouftah HT. Hierarchical trust based, black hole detection in WSN based Smart Grids IEEE international conference on Communications (ICC), Paris, France 2017 May.21-25: 1-6.
[14]
Kaur G, Jain VK, Yogesh C. Detection and prevention of black hole attacks in WSN International Conference on Intelligent, Secure Dependable Systems in Distributed and Cloud Environments (ISDDC). Vancouver, BC, Canada.Springer . 2017; pp. 118-26.
[15]
Saurabh S, Sapna G. Cluster and reputation based cooperative mali-cious node detection & removal scheme in manets. IEEE 11th international conference on intelligent systems and control (ISCO) Coimbatore, India, 2017.
[16]
Aljumah A, Ahanger TA. Futuristic method to detect and prevent black-hole attack in wireless sensor networks. Int J Comput Sci Netw Secur 2017; 17(2): 194-201.
[17]
Kshirsagar VH, Kanthe AM, Simunic D. Trust based detection and elimination of packet drop attack in the mobile ad-hoc networks. wireless personal communication, springer sci-ence+business media, LLC, part of springer nature 2017 November; 100(2): 1-10.
[http://dx.doi.org/10.1007/s11277-017-5070-x]
[18]
Chhabra A, Vashishth V, Sharma DK. A game theory based secure model against black hole attacks in opportunistic networks. 2017 51st Annual Conference on Information Sciences and Systems (CISS) Baltimore, MD, USA, 2017.
[19]
Tyagi P, Dembla D. Advanced secured routing algorithm of vehicular ad-hoc network. Wirel Pers Commun 2018; 102(1): 41-60.
[http://dx.doi.org/10.1007/s11277-018-5824-0]
[20]
Ataie E, Movaghar A, Bastam M. A cooperation enforcement, malice detection and energy efficient mechanism for mobile ad hoc networks. Int J Sensors Wirel Commun Control 2013; 3(2): 78-84.
[http://dx.doi.org/10.2174/221032790302140505095700]
[21]
Dorri A. An EDRI-based approach for detecting and eliminating cooperative black hole nodes in MANET; springer wireless networks New York. Springer US 2017; 23(6): 1767-78.
[22]
Pavan KG, Madhu M. Improving security and detecting black hole attack in wireless sensing networks. Int Sci Eng Ethics 2017; 8(5): 260-5.
[23]
Ding Y, Hao Q, Guang L. Black hole attack model and simulation for mobile ad-hoc network. Int J Innov Comput, Inf Control 2015; 11(1): 203-11.
[24]
Badenhop CW. Ramsey benjamin and mullinsm barry: An analytical black hole attack model using a stochastic topology approximation technique for reactive ad-hoc routing protocols. Int J Netw Secur 2016; 18(4): 667-77.
[25]
Adoni KA, Joshi RD. Optimization of energy consumption for olsr routing protocol in MANET. International Sensors (Basel) 2012; 4(1): 251-62.
[http://dx.doi.org/10.5121/ijwmn.2012.4119]
[26]
Adoni KA, Tavildar AS. Probabilistic modeling and estimation of network blocking probability for selfish behavior attack in mobile ad hoc networks. Comput Soc Netw 2016; 5(4): 653-9.
[27]
Cheny ZQ. On the lifetime of wireless sensor networks. IEEE Commun Lett 2005; 9(11): 976-8.
[http://dx.doi.org/10.1109/LCOMM.2005.11010]

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