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.
[6]
Geetha S, Geetha RG. Trust model based on bayesian statistical method for aomdv in manet. Appl Clin Inform 2014; 69(1): 172-81.
[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.
[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.
[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.
[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.
[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.