Timeout Misbehavior Detection Algorithm

ISSN 1828-6003 Vol. 8 N. 5 May 2013

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Segmentation of Cerebrospinal Fluid and Internal Brain Nuclei in Brain Magnetic Resonance Images by D. Selvaraj, R. Dhanasekaran

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Clustering Network Traffic Utilization by Nazli Mohd. Khairudin, Zaiton Muda, Aida Mustapha, Yogeswaran Nagarathinam, Mohd. Sidek Salleh

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Genetic Algorithm Based Optimization of Vertical Links for Efficient 3D NoC Multicore Crypto Processor by A. Vino Vilmet Rose, R. Seshasayanan Ramachandran

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Timeout Misbehavior Detection Algorithm: a Novel Method for the Detection of MANET Misbehavior by R. Kalaiarasi, D. Sridharan

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An Efficient Iterative Modulo Scheduling Approach for Improved Resource Allocation for Effective Multimedia Communication on Grid Computing Environment by G. Saravanan, V. Gopalakrishnan

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Intrusion Detection System Using Artificial Immune System and New Multi Core Technology by Anas Bakla, Rand El-Koutly

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International Review on Computers and Software (I.RE.CO.S.), Vol. 8, N. 5 ISSN 1828-6003 May 2013

Timeout Misbehavior Detection Algorithm: a Novel Method for the Detection of MANET Misbehavior R. Kalaiarasi, D. Sridharan Abstract – The performance of the mobile ad hoc network is subject to degradation in the

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presence of misbehaving nodes, due to the timeout misbehavior of the MAC layer. In order to rectify this drawback, the Timeout Misbehavior Detection Algorithm (TMDA) is proposed. The RTS frame adds an additional field called TimeOut Bit (TOB), which gives the information about whether the particular node is misbehaving or not. According to that, the detection and correction procedure has been carried out. The proposed method minimizes the throughput degradation, jitter, frame delay and increases the frame delivery ratio. Copyright © 2013 Praise Worthy Prize S.r.l. - All rights reserved.

Keywords: MANET, Timeout Misbehavior, TMDA Algorithm, Misbehavior Detection,

I.

Introduction

The following section describes the IEEE 802.11 MAC protocol. Section 3 describes the related work, section 4 describes the details of our proposed model section 5 describes the simulation environment, result and discussion, finally conclusion is given in section 6.

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A mobile ad hoc network (MANET) is an infrastructure less network, which can communicate via intermediate nodes within their transmission range [1]. In MANET, network is established instantly at any place, at any time without any fixed infrastructure. MANETs are being used in emergency operations like earthquake, military mobile networks, disaster relief and mine site operations [2]. In recent years, wireless technologies have opened up new challenges in MANET due to limited wireless links and bandwidth, mobility, and battery power. Each node acts as a router and forwards frames to its neighbor in a multihop manner. The node may forward the frames to far away nodes with the cooperation of the intermediate nodes. In these networks, Medium Access Control (MAC) protocols are responsible for the coordination of the nodes to achieve successful frame delivery ratio. The mobile nodes are free to move anywhere, and new nodes can enter into the network without prior notice [3]. Misbehavior can be classified into selfish misbehavior and greedy misbehavior. The primary goal of a selfish user is to improve its own performance, but its greedy misbehavior usually results in performance degradation of honest hosts [4]. Selfish nodes may choose small backoff interval, does not double the Contention Window (CW) after collision and refuse to forward frames on behalf of other hosts in order to conserve energy. The greedy nodes may be modified by using the parameters such as Denials of Service (DOS) attack, timeout mechanism, Network Allocation Vector (NAV) and Short Distributed Inter Frame Space (S-DIFS). This paper presents methods to detect and penalize the malicious misbehaving nodes using the TMDA. The rest of the paper is organized as follows.

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Throughput Improvement

Manuscript received and revised April 2013, accepted May 2013

II.

IEEE 802.11 MAC

The IEEE 802.11 MAC uses two types of coordination function to access the wireless networks, namely Distributed Coordination Function (DCF) and optional Point Coordination Function (PCF). DCF uses the Carrier Sensing Multiple Access with Collision Avoidance (CSMA/CA) [5] to access the nodes as shown in Fig. 1. Any node before accessing the channel should wait for DIFS time. If the channel senses idle for DIFS time, then it enters into the contention mode. The node waits for an additional time called contention period. Contention period can be calculated using the Eq. (1): BT=B·aSlotTime

(1)

where, BT represents backoff time, B is the backoff timer which is a randomly chosen integer from a uniform distribution over the interval between zero and the current contention window size CW, and aSlotTime is the length of a unit time slot [6]. During this period, backoff time is chosen uniformly in the interval of [0 to CW-1] [7]. The CW is doubled after every unsuccessful transmission up to the maximum contention window (CWMAX). If the data transmission is successful, CW is reset to CWMIN. RTS/CTS control frames are used here to reserve the channel and minimize the collisions. After the

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successful transmission of RTS/CTS frames the sender sends the data frame. At the completion of the data transmission the receiver sends the ACK frame. Other stations that overhear the RTS/CTS control frames, adjust their Network Allocation Vector (NAV) based on the duration field value.

The receiver sends the CTS frame at the duration specified by the IEEE 802.11 standard (TOCTS). As the sender does not receive the CTS frame within the TOCTS’ time, it drops the CTS frame and retries sending RTS frame again [9]. TOCTS can be calculated using the following equation: TOCTS=TRTS+2  +SIFS+TCTS

(2)

Fig. 1. IEEE 802.11 RTS/CTS mechanism

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where, TRTS and TCTS are the transmission time of RTS and CTS control frames and is the maximum propagation delay. Here S is the sender node and R (M) is defined as the misbehaving receiver. Sender will not know about the receiver misbehavior. Malicious receiver chooses a larger SIFS value (SIFS*) so that sender receives only CTS after the timeout period. Due to the expiration time of CTS, sender drops the frame as shown in Fig. 3. Sender will not know about the receiver misbehavior. Sender assumes that a collision has occurred and repeats sending the RTS control frame instead of the DATA frame [9]. TO CTS*

TO CTS

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R TO CTS ’

S (M)

CTS (1)

RTS (3)

RTS (2)

RTS (1)

R SIFS’ SIFS

Fig. 2. Sender side misbehavior

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CTS (1)

RTS (3)

TO CTS

RTS (2)

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S

RTS (1)

In MANET, node misbehavior can occur either at the sender side or receiver side. Misbehaving nodes could interrupt either contention based or reservation based MAC protocols. Misbehaving sender or receiver could intentionally not follow protocol specification defined in IEEE 802.11 MAC protocol. If the MAC protocol is implemented as software instead of hardware, it is easy to alter the protocol by the selfish or malicious nodes [8]. A misbehaved receiver could transmit the CTS after DIFS instead of Short Inter Frame Space (SIFS) without any change of the standard parameters. This type of attack is defined as TimeOut (TO) attack. The malicious sender sets its SIFS value to a smaller value than the standard IEEE 802.11 SIFS value. Due to this, the expected time for a CTS frame to arrive from the receiver to the sender becomes less. This value is set as TOCTS’ as shown from the Fig. 2. Here S (M) is defined as misbehaving sender and R is the receiver node.

R (M) SIFS SIFS*

Fig. 3. Receiver side misbehavior

III. Related Work V. N. Lolla, L. K. Law, S. V. Krishnamoorthy, C. Ravishankar and D. Manjunath [10] have modified the IEEE 802.11 MAC protocol and proposed a method to detect the MAC layer back off timer violations in MANET. This protocol exchanges the state of random number generator of each of the neighbors and then uses the Wilcoxon rank sum test to compare the difference between analytically computed samples and the observed sample to detect the misbehavior. The main disadvantage of this approach is the usage of fixed sample size to detect the misbehaving node and also it does not handle collusion between the nodes. Minimax Robust Misbehavior detection [11] constitutes the theoretical worst case attack framework to detect the misbehaving nodes. It is a robust misbehavior detection technique. Here all the transmissions are observable during communication. However, in the case of collision, it is not possible to obtain the details of nodes involved in the communication. There is no operational method International Review on Computers and Software, Vol. 8, N. 5

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proposed to detect the misbehaving node. Toledo and Wang [12] utilizes the Kolmogorov-Smirnov (K-S) test to address the detection problem. The misbehavior detection can be made by comparing the distribution of sampled traffic data with the normal behavior distribution estimated on line. This method needs fixed size data samples to perform the operation which are not suitable for real time detection. The modified sequential truncated K-S test has been used to predefine the number of samples before the test starts in order to get a proper significant level for each test step [13]. Utpal Paul, Anand Kashyap, Ritesh Maheshwari and Samir R. Das [14] used the tool to estimate the interference between nodes and links in a wireless traffic and to demonstrate the detection of selfish carrier sense behavior in an IEEE 802.11 network. It can be accommodated with high computational cost where as it needs to provide sniffer associated with the sender to monitor the frames.

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Proposed Model

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A new algorithm is developed to detect both sender

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IV.

misbehavior and receiver misbehavior is shown in Fig. 4. Initially the expected TOCTS value (TOCTS) is calculated as given in Eq. (2). Then during frame transmission, the algorithm computes the TOCTS actual value and at each iteration, this value is compared with TOCTS value. If they are equal then there is no misbehavior and algorithm will stop. If it is not the same, then misbehavior is detected and the algorithm monitors the communication of the particular node for a particular duration which is set based on a threshold value. The threshold value is determined by the number of times a node’s misbehavior can be pardoned. If the number of misbehaviors equals to the threshold value, then the node is deviated from the network. The RTS frame format is modified from the standard IEEE 802.11 RTS format. It is shown in Fig. 5. An additional field TimeOut Bit (TOB) is added to it. The TOB represents the presence or absence of misbehavior node.

Fig. 4. Flowchart depicts TimeOut misbehavior detection algorithm

Fig. 5. T-RTS frame format

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IV.1. Sender Misbehavior Detection

given time period [17]. The effect of network performance in terms of number of nodes and the throughput are shown in Fig. 6. It has been observed from the figure that the IEEE 802.11 MAC throughput degrades when the number of nodes exceed above 70. It has been reported that similar degradation behavior was observed by Antonis Athanasopoulos, Evangelos Topalis, Christos Antonopoulos, Stavros Koubias [18], due to RTS/CTS exchange signaling, which in turn added to the extra overhead in the network.

If the TOCTS actual is equal to TOCTS then, set the bit as 0 and it represents that there is no misbehavior by the sender. If it is not equal, TOB is set to 1 and it assures sender misbehavior: If TOCTS > TOCTS’ – sender misbehavior The TMDA algorithm is run to detect the sender misbehavior. This algorithm computes the TOCTS actual value of the sender and compares it with expected TOCTS value. If it is less, then the TOB bit is set. After detecting the sender misbehavior, call the adjustment procedure for the correction of misbehaving node: Adjust SIFS’= SIFS

If the TOCTS’ time is greater than the TOCTS time then the receiver is a misbehaving node because malicious receiver purposely chooses larger SIFS. The sender waits for a CTS frame till TOCTS time and on not receiving it, tries to retransmit the RTS frame:

After detecting, the receiver to be misbehaving the algorithm adjusts the SIFS value:

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Adjust SIFS* = SIFS

Then check the number of misbehaviors to a threshold value and deactivate the receiver node if the number of misbehavior is greater than the threshold.

V.

Proposed Model

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We have simulated this algorithm using Network Simulator-2 (ns-2) [15]. Hundred nodes are deployed in a field of area 2000×2000 m2 randomly. Simulation has been run for 200 seconds. The propagation channel of two ray ground reflection model is assumed with the data rate of 2Mbps. Source transmits constant Bit Rate (CBR) with UDP traffic at two frames per second and data payload of each frame is 210 bytes long. Source destination pairs are randomly selected. Mobile nodes are moved randomly according to the random waypoint mobility model with the node speed of 2 m/s. AODV routing protocol is used to find the path for a given source-destination pair [16]. V.1.

Fig. 6. Effect of throughput with the number of nodes using TMDA algorithm

This also occurs due to large network size which results in a higher collision probability and significant performance degradation [19]. However, by using the TMDA algorithm, the throughput value is increased to 1.98 Mbps. This indicates the significant performance enhancement in the throughput compared to IEEE 802.11 MAC protocol. In addition, the degradation behavior is eliminated after 70 nodes, thereby proving it to be a better method. Frame delivery ratio is defined as the ratio of the number of data frames successfully delivered to the destination over the number of data frames sent by the source. Fig. 7 shows the effect of frame delivery ratio with the number of nodes. As per the IEEE 802.11, the FDR was decreased when the number of nodes exceeds 70 is shown in Fig. 7. It is observed that on applying the TMDA algorithm, the FDR value is increased linearly with the increasing number of nodes. For instance, the IEEE 802.11 showed the FDR value of 97.9 whereas, the TMDA showed nearly 98.8. Hence, FDR value was increased significantly using TMDA.

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If TOCTS < TOCTS’ - receiver misbehavior

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IV.2. Receiver Misbehavior Detection

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Then check the number of misbehaviors to a threshold value and deactivate the sender node if the number of misbehavior is greater than the threshold.

Results and Discussion

Throughput is defined as the amount of data stimulated successfully from source to destination in a

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Fig. 7. Effect of FDR with the number of nodes using TMDA

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In general, the variation in frame delay is expressed as jitter. Fig. 8 shows that the TMDA algorithm decreases the jitter value significantly as the number of nodes increases. The delay is the time difference between when a frame is generated and until it is delivered to the receiving application at the destination node [20].

Fig. 10. Effect of misdetection and correct detection ratio

VI.

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This paper discussed about the misbehaving mobile nodes and their impact on the degradation of MANET performance. An algorithm (TMDA) is proposed here to reduce the misbehaving occurring due to timeout attack. TMDA algorithm detects and corrects the misbehaving nodes if possible or it deactivates the nodes from the network. Simulation study shows that the performance of the network has increased considerably after the implementation of TMDA.

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Fig. 8. Effect of jitter with the number of nodes using TMDA algorithm

Acknowledgements

This work was supported by the Department of Science and Technology, New Delhi, India, under Grant SR/WOS-A/ET-31/2009.

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It is seen from the graph (Fig. 9) that there is not much effect of delay with the number of nodes on using TMDA algorithm as compared to IEEE 802.11. In IEEE 802.11 algorithm delay occurs due to the misbehaving behavior of the nodes. In TMDA delay occurs due to implementation of detection and correction algorithm. It is observed that this delay in TMDA is balanced due to the reduction of misbehaving nodes, which in turn increases the overall network performance.

Conclusion

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Fig. 9. Effect of Delay with the number of nodes using TMDA algorithm

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[3]

Correct detection is the ratio of the number of misbehaved nodes that are correctly marked by the detection systems suspects to the total number of active misbehaved nodes in the network. Misdetection is the ratio of the number of wellbehaved nodes that is incorrectly diagnosed as suspects to the total number of well-behaved nodes in the network [21]. Fig. 10 shows the correct detection ratio is nearly 80%. On the other hand, misdetection ratio is nearly 20%. This is due to the fact that well behaved nodes can be misdiagnosed due to collision. Misdetection and correct detection ratio is significantly increased when the number of nodes increases.

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[4]

[5]

[6]

[7]

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References

Kok Seng Ting, Gee Keng Ee, Chee Kyun Ng, Nor Kamariah Noordin and Borhanuddin Mohd. Ali, The Performance Evaluation of IEEE 802.11 against IEEE 802.15.4 with Low Transmission Power, 17th Asia-Pacific Conference on Communications, APCC (Page: 850- 855, Malaysia, October 2011, Avaliable:http://dx.doi.10.1109/APCC.2011.6152927). R. Kalaiarasi, Getsy S. Sara S. Neelavathy Pari and D. Sridharan, Performance Analysis of Contention Window Cheating Misbehaviors in Mobile Ad hoc Networks, International journal of computer science & information Technology (IJCSIT) Vol.2, no.5, pp. 31-42, 2010. S. Priyadarsini and Umashankar S, TSRD-RL Algorithm Based Secured Route Discovery for MANET with Improved Route Lifetime, (2012) International Review on Computers and Software (IRECOS), 7 (2), pp. 499-504. Ghazale Hosseinabadi and Nitin Vaidya, Selfish Misbehavior in Scheduling Algorithms of Wireless Networks, IEEE 29th International Conference on Performance Computing and Communications Conference , IPCCC, (Page 214 - 221, 2010, Avaliable:http://dx.doi.10.1109/PCCC.2010.5682306). Nidal A. Al-Dmour, Studying the Impact of DIFS on the QoS Parameters for Wireless Networks, (2011) International Review on Computers and Software (IRECOS), 6 (3), pp. 384-388. Somkiat Pornchaiwiwat and Watit Benjapolakul, Coordinate Assigning Contention Window in Ad Hoc Network, 2006 IEEE Region 10 Conference, (Page 1-4, Nov.2006, ,Avaliable:http://dx.doi.10.1109/TENCON.2006.343780). Ting-Yu Lin, Ching-Yi Tsai, and Kun-Ru Wu, EARC: Enhanced Adaptation of Link Rate and Contention Window for IEEE 802.11 Multi-Rate Wireless Networks, IEEE Transactions on Communications, Vol. 60, no. 9, pp. 2623-2634, 2012. M. Raya, J.P. Hubaux, I. Aad, DOMINO: a system to detect greedy behavior in ieee 802.11 hotspots, in: Proc. of ACM

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Dr. D. Sridharan was born in Tirupattur, Vellore district, India on May 3rd, 1968. He received his B.Tech. degree and M.E.degree in Electronics Engineering from Madras Institute of Technology, Anna University, India in the year 1991 and 1993 respectively. He got his Ph.D degree in the Faculty of Information and Communication Engineering, Anna University in 2005. He is currently working as Associate Professor in the Department of Electronics and Communication Engineering, CEG Campus, Anna University, Chennai, India. He was awarded the Young Scientist Research Fellowship by SERC of Department of Science and Technology, Government of India. His present research interests include Internet Technology, Network Security, Distributed Computing and Wireless Sensor Networks.

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[21]

R. Kalaiarasi was born in Namakkal, India on July 7th, 1976. She received her B.Sc degree in Computer Science from Bharathiar University, India in the year 1996 and Master of Computer Applications from University of Madras in the year 2004. Currently she is pursuing her Ph.D degree in the Faculty of Science & Humanities, Anna University, Chennai, India. Her research field includes wireless ad hoc networks, MAC Layer, Quality of Service and communication networks. She has published 4 papers in peer reviewed journals and 3 international conference proceedings.

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Abstracting and Indexing Information: Cambridge Scientific Abstracts (CSA/CIG) Academic Search Complete (EBSCO Information Services) Elsevier Bibliographic Database - SCOPUS Index Copernicus (Journal Master List): Impact Factor 6.14 Autorizzazione del Tribunale di Napoli n. 59 del 30/06/2006

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