Evaluating and Mitigating the Effects of Selfish MAC Layer Misbehavior in MANETs

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Evaluating and Mitigating the Effects ofSelfish MAC Layer Misbehavior in MANETs

Sam Jabbehdari, Anahita Sanandaji, and Nasser Modiri

Abstract —In mobile ad hoc networks, the IEEE 802.11 CSMA/CA is widely deployed as the primary MAC layer protocol to

schedule the access to the wireless medium. This protocol was designed with the assumption that nodes would never deviate

from the protocol. However, nodes may purposefully show misbehavior at MAC layer in order to obtain more bandwidth,

conserver its resources, degrade the network performance or disrupt the services of the network. In this paper, we introduce

several types of MAC layer misbehaviors, and evaluate their impact on performance of other well-behaved nodes using

extensive simulations. To mitigate the negative effects of misbehaving nodes we introduce a novel scheme, which is a

combination of detection and reaction mechanisms. Our scheme is robust against colluding nodes and provides an effective

mechanism to react against misbehaved nodes. Employing a misbehavior scenario in a simulated network, we study the

efficiency of our scheme. Particularly, we demonstrate that by implementing our solution, all nodes are able to gain a fair share

of throughput in network.

Index Terms —MANET, DSR, MAC layer misbehavior, Selfish misbehavior, Detection and reaction schemes.

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1 INTRODUCTION

N the last decades mobile ad hoc networks (MANETs)have become increasingly popular. MANETs can beeasily deployed and are ideally suitable for battlefield,

search, rescue and disaster relief operations. A MANET isa group of autonomous nodes that form a dynamic, muti-hop radio network in a decentralized way. The character-istics of ad hoc networks (including a changing networktopology, resource and bandwidth constraints, open net-work architecture and shared medium) have made it dif-ficult to establish a secure and reliable communication.Nodes must cooperate in a way to guarantee correct routeestablishment and obtaining a fair share of availablebandwidth. Nevertheless, due to their properties, MA-NETs are vulnerable to different security attacks at differ-ent layers (mainly at the transport, network, and data-linklayers) of the protocol stack [1].

As a result, many research activities focus on the net-work layer and securing ad hoc routing protocols andmechanisms. In the most of the proposed solutions, eachMANET node contains all the modules required to per-form the detection tasks of security attacks. Some pro-posals [2], [3] are based on the notion of credit, while thecredit value is often evaluated based on the transmissionbehavior of the nodes. The works in [4], [5], [6] are fo-

cused on events generated at the network layer, and theyare examples of reputation systems. In such systems, a“reputation value” (that is assigned to each node) in-creases when the node successfully assists with intrusion

detection tasks, and decreases if the node’s performanceduring intrusion detection is unsatisfactory.

Because of security issues in the IEEE 802.11 MAC [7]protocol, attacks in the MAC layer are easy to target. TheIEEE 802.11 distributed coordination function (DCF)mode combines carrier sensing with collision avoidanceand is introduced as one of the most popular MAC layeraccess protocols for wireless networks. The IEEE 802.11standard is designed with the assumption that all nodesare fully cooperative. However, some nodes may pur-posefully choose to deviate and show misbehavior at theMAC layer. The distributed behavior of 802.11 DCF andthe lack of a trusted centralized authority have madeMANETs vulnerable to MAC layer attacks. Any misbe-havior at this level has a negative impact on the networkperformance.

The strategies of creating MAC layer misbehavior forwireless networks have attracted much attention recentlyand thus there are some publications that propose newdetection and prevention solutions. On the other hand,little work has been done in the area of MANET securitythat focuses on the MAC layer.

In this paper, we introduce different misbehavior sce-narios that modify the proper function of MAC protocol.

We classify six types of such misbehavior and study theirimpact on network performance. In addition, we proposea novel scheme to detect misbehavior in MANETs andresponse by well-behaved nodes as a strategy to reacttowards misbehavior. Using simulations, we demonstratethat such an approach guarantees a fair share of through-put for all nodes in the network. We use DSR [8] as abasic routing protocol in our simulation environment.

The rest of the paper is organized as follows: In section2 we provide an overview of related research in this area.In section 3 an overview on IEEE 802.11 MAC protocoland its vulnerabilities is given. We also introduce six mis-behavior scenarios that are used for simulation analysis in

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Sam Jabbehdari is with the Computer Engineering Department, NorthTehran Branch, Islamic Azad University, Tehran, Iran.

Anahita Sanandaji is with the Computer Engineering Department, NorthTehran Branch, Islamic Azad University, Tehran, Iran.

Nasser Modiri is with the Computer Engineering Department, ZanjanBranch, Islamic Azad University, Zanjan, Iran.

I

JOURNAL OF COMPUTING, VOLUME 4, ISSUE 2, FEBRUARY 2012, ISSN 2151-9617

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next sections. In section 4 our mitigation scheme is intro-duced which is a combination of detection and reactionschemes. The detection scheme is implemented underthree different situations and the reaction scheme worksbased on using one of the detection methods.

In section 5, we evaluate the impact of MAC layer mis-behavior and the performance of our proposed scheme.We introduce future work and conclude the paper in Sec-tion 6.

2 RELATED WORK

In the last few years, several detection and preventionschemes have been proposed to protect wireless networksagainst MAC layer misbehavior. Most of these researchestend to concentrate on wireless networks in general butthe problem is more challenging in MANETs. Some of theproposed solutions for wireless networks have central-ized schemes that are implemented in Access Points (AP)accordingly. DOMINO [9] which does not require anymodification to the standard MAC protocol is imple-

mented at the AP which is assumed to be trustworthy.Authors in [10] introduce the concept of receiver as-

signed backoff, and authors in [11] propose modificationsto the MAC protocol in order to facilitate easy detectionand penalization of misbehaving sender. Some other ap-proaches try to apply game theory in order to mitigate theselfish behavior of the cheaters [12], [13].

In [14] a new backoff scheme is introduced that usesone-way function to generate the backoff values andmodifies the RTS frame format by piggybacking the DA-TA packet’s CRC value and the transmission attempt. Butcomputing CRC and the hash function has an overheadthat may lead to a decrease in the performance of the

network.Solutions that mainly use the concept of receiver as-signed backoff assume a trusted receiver. But the fact isthat the receiver might choose to send smaller Backoffvalues if it wants to benefit by receiving data more fre-quently. Besides most reaction schemes employed bygenuine nodes attempt to penalize [10], [11] or isolate [15]the selfish node. The overall objective of reaction schemesis to make it disadvantageous for any node to deviatefrom standard protocol behavior. The isolation of misbe-having nodes is not the best strategy to react.

To reach a practical method we use ideas of [10], [16][17] and introduces a mitigating scheme to detect misbe-having nodes and react in an effective way even in thecase of colluding nodes.

3 OVERVIEW ON IEEE 802.11 MAC PROTOCOL

AND ITS VULNERBILITIES

In this part, at first we review the operation of IEEE 80211MAC protocol. Then MAC layer misbehavior is over-viewed. At the end, we demonstrate six misbehavior sce-narios that are used in next sections for simulation.

3.1 IEEE 802.11 MAC Protocol Operation

In the IEEE 802.11 standard, a common medium access

control (MAC) layer is specified which provides a varietyof functions that support the operation of wireless net-works. The IEEE 802.11 MAC protocol supports twotypes of access methods:

The basic access method is the distributed coordina-tion function (DCF), which is a carrier sense multipleaccess with collision avoidance (CSMA/CA) mecha-nism. DCF is designed to support best-effort trafficthat does not require any service guarantees.

The optional access method is the point coordinationfunction (PCF) in which access point performs thepolling to determine which node has the right totransmit. This results to a contention free communi-cation. PCF method is generally used in scenarioswhere service guarantees are required.

The DCF is the primary access method in MANETs be-cause PCF is an optional access mechanism that can beused only in the presence of an access point. In the DCFmode, before any transmission a node must ensure thatthe medium is idle. A random backoff interval less thanor equal to the current contention window (CW) size is

selected. When the medium is sensed to be idle, thebackoff timer is decreased by one at each time slot. Anode may wait for DIFS (DCF Inter Frame Space) timeslot, when a successful transmission is occurred. In thecase of a collision, the node waits for an EIFS (ExtendedInter frame Space) period. If the medium is sensed to bebusy, the node freezes its backoff timer and sets its NAV(Network Allocation Value) to the transmission delayindicated in the received frame. When the backoff timerreaches zero, the transmission starts again. The nodechooses a backoff value form [0, CW ]. After each success-ful transmission, the size of CW is set to CW min. But in thecase of each unsuccessful attempt, the CW is doubled

until it reaches CW max. CW is reset under two conditions:(i) when the packet is received successfully or (ii) whenmaximum retry limit is reached, which leads to the dis-card of the packet.

3.2 MAC Layer Misbehavior Overview

We can classify misbehaviors generating at the MAC lay-er from two points of view:

First, classification of MAC layer misbehaviorsbased on the purpose of the host misbehavior.

Second, categorization of attacks based on themisbehaving node’s knowledge of the existingintrusion detection (IDS), prevention (IPS) andreaction (IRS) systems.

Host misbehaviors in MANETs can be classified intotwo categories of selfish [10] and malicious [18] behavior.Deviating from the MAC protocol may occur in order tomake it possible for a misbehaving node to gain morebandwidth over regularly behaving normal nodes or toconserve energy. To achieve this purpose, the misbehavenode should change the MAC layer parameters. Thesekinds of misbehaviors that aim at improving the node’sown performance are called greedy or selfish misbehav-iors. On the other hand, those misbehaviors with the pur-pose of disrupting normal operation of the network arecalled malicious misbehavior.






From the second viewpoint, MAC layer attacks can beclassified into Native and Smart attacks. In a native at-tack, a misbehaving node has no knowledge of detectionand prevention systems. A node uses simple attacks togain more bandwidth or conserve its energy. These at-tacks can severely degrade the performance of a network.However, they can be detected more easily in comparisonto the second type of attacks called smart attack. In asmart attack, a misbehaving node implements intelligenttechniques that help the node to act selfishly or malicious-ly without being detected easily.

Some of the misbehavior strategies that can be imple-mented are introduced as follow:

Backoff Manipulation: In this strategy, the selfishnode sets its backoff value to a small fixed value orselects it from a short interval such as [0, CW/2] ra-ther than [0, CW] where k is assumed to be positive.

CW Cheating: The misbehaving node does not dou-ble its CW after an unsuccessful transmission and,thus it gains more chance to reach the channel.

CTS Scrambling: The selfish node scrambles CTS or

ACK frames of well-behaved nodes to increase theirCW.

DIFS Value Reduction: In this strategy, the misbehav-ing node transmits before the required DIFS time slotelapse (e.g. it waits for a shorter DIFS called S-DIFS).

NAV Duration Increase: The selfish node increasesthe value of the duration field in RTS or data packetsin a way to force the receiver to update its NAV ac-cording to the received duration. In this case, themisbehaving node gets more chance to access thechannel, if it has more packets to send, since it startsdecreasing its backoff value before its neighbours.

Single Adversary and Colluding Adversary: A single

adversary attack (SAA) uses unauthorized datatransmission to inject enormous data packets intonormal nodes to deplete the limited channel capacityand decrease the node energy. In addition, a collud-ing adversary attack can deplete bandwidth withinits vicinity in order to prevent well-behaved nodesfrom normal communication.

Timeout Attack: This type of attack can purposefullydelay the transmission of MAC frames and forces awell-behaved node to drop the packets, while the ma-licious node itself completely adheres the protocol,and therefore hides from the detection system.

Adaptive Cheating: A clever cheater (which has some

knowledge about the deployed detection system)may choose to switch frequently between severalmisbehaviour strategies to avoid being detected. Alogical switch between different misbehaviour strate-gies without large deviation from protocol allows themisbehaving node to gain more network resourceswithout being detected.

Inter-layer Attack: A misbehaving node increases itschance to access the medium and therefore gainingmore bandwidth by launching a cross-layer attacktargeting at the routing protocols to decrease thenumber of contending nodes around it, without mod-ifying the other parameters.

3.3 MAC Layer Misbehviour Scenarios

In this part, to evaluate the effect of MAC layer misbehav-ior, we consider the following six types of misbehaviorscenarios associated with modifying the 802.11 MAC pro-tocol in MANET. These scenarios are demonstrated in away to evaluate the greedy behavior of a node when ittries to obtain an unfair share of bandwidth or refuse to

participate in network activity to conserve its resourses. CW manipulation using a variable: Instead of choos-ing the random backoff value from the interval [0 . ..CW], the selfish node chooses it at random from theinterval [0 . . .δ(CW)], where 0 < δ < 1 is for obtain-ing unfair share of bandwidth and 1 δ < 2 is for re-fusing to participate actively in network.

CW manipulation using fixed value: The misbehav-ing node sets its contention window to a fixed sizecalled CWfix, and always chooses its backoff value atrandom from the interval [0 . . .CWfix]. To increase itschances in accessing the channel, the misbehavednode chooses a small CWfix. Contradictory, in orderto save its resources, the misbehaved node set CWfix

to a larger value. CW Cheating upon unsuccessful transmission: Upon

an unsuccessful transmission, the misbehaving nodeinstead of setting its CW to be min{2 × CW, CWmax},sets its contention window as CW = max{CWmin, min{β×CW, CWmax}}, where 0 < β < 2. This type of misbe-havior covers purpose of obtaining more share ofbandwidth.

Backoff cheating using fixed value: The node choosesa deterministic, constant backoff value irrespective ofthe current contention window size. For example, thenode could always choose a very small backoff (say2), in any situation and gain preference over other

well-behaved nodes to gain the bandwidth. NAV duration manipulation: In this situation, the

misbehaved node changes its duration time in RTS ordata packets. As a result, well-behaved nodes areforced to change their NAV to a larger value and themisbehaved node increases its chance to access thechannel.

CW manipulation upon receiving a routing packetusing DSR protocol: As we are using DSR as routingprotocol for our MANET, this kind of misbehavior isoccurred when a node receives route request broad-casts. A selfish node can start misbehaving onlywhen it receives RREQ. In order to access the channel

faster than its neighbors to reply to RREQ, the misbe-haved node intentionally picks up a smaller cw uponreception of a routing packet. This leads to a kind of arushing attack [19]. Besides, in order to conserve en-ergy or force the source node to choose a longer routeto destination, a misbehaved node may choose alarger CW to prevent to be selected as a forwarder.The CW can be manipulated by using a variable or afixed value scenario.

4 PROPOSED MITIGATION METHOD

Despite the numerous works which have done in the lit-






erature, selfish or greedy misbehavior of nodes at MAClayer remains a challenging problem to solve in MA-NETS. With implementing any of the misbehavior scenar-ios mentioned in pervious section, the performance ofwell-behaved nodes degrades significantly. This fact willbe proved in our simulation implementation which isdemonstrated in next section.

To cope with this problem, we have introduced a set ofschemes that try to nullify the selfish attempt of thosemisbehaved nodes that deliberately disobey the IEEE802.11 MAC protocol. By proposing this scheme, we aimto achieve the objective of mitigating the negative effectsof MAC layer misbehavior in MANET.

In our assumption, a misbehavior node may use twoapproaches to reach two different aims. It may take upthe bandwidth unfairly or it may refuse to participate inthe networks actively for saving its resource, e.g., batterypower. To implement our detection and preventionschemes we define three different situations:

Sender Misbehaviour Receiver Misbehaviour

Sender and Receiver both MisbehaviourThen we define a reaction method which can be im-

plemented as a penalty based on any of the detectionschemes mentioned before. The scheme we proposed inthis paper is the extensions to those algorithms demon-strated in [10], [16], [17]. We also assume that DSR is usedas the basic routing protocol.

4.1 Situation 1: Detection Scheme Based onSender Misbehavior

In situation 1 we assume that the sender node (S) is mis-behaving while the destination node (R) is behaving nor-mally. After the initiation of the RTS by S, the receiver

replies with CTS and a safe random backoff value (it isdue to the assumption that R is well-behaved). R savesthis value for future uses.

S, despite of knowing the legitimate Backoff value, triesto send the data before or after the time slots are over.

The ‘n’ neighboring nodes of S observe the arrival ofthe CTS and the first attempt to send data. They separate-ly calculate the time slots that elapsed as the turnaroundtime (TR). R has the responsibility of determining wheth-er S is misbehaving or not. To achieve this purpose the ‘n’TRi that are calculated by ‘n’ neighbors are sent to R. Ritself also calculates the time elapsed when it receives thedata from S. We call this turnaround time as TR d. Then Rcalculates the average of the ‘n’ values received. The actu-al Backoff value is calculated according to (1).

n-1

i

i=1

act d

TR

B =α( )+β(TR )n

(1)

α and β are two weighting parameters. As the turna-round time (TRd) that is computed by R is more trusted, α and β are defined in a way that β > α, and α + β = 1.

After computing Bact, R checks whether one of the con-ditions in (2) or (3) holdes true or not.

If Bact < Bexp

(2)

Each time the condiftion in (2) holds true a counter isincremented by one. After each increment the counter iscompared with an upperbound threshold. If the countervalue exceeds the upperbound threshold then S is identi-fied as a selfish node which is trying to take up thebandwidth unfairly.

If Bact > Bexp (3)

Each time the condition in (3) holds true the counter isdecremented by one. After each decrement the counter iscompared with a lowerbound threshold. If the countervalue becomes less than lowerbound threshold then S isidentified as a misbehaved node that is refuseing to par-ticipate in the networks actively.

Whenever a node is identified as misbehaving, the in-formation about it, including actual backoff value Bact, isbroadcasted to all the nearby nodes and the reactionscheme is lunched.

4.2 Situation 2: Detection Scheme Based onReceiver Misbehavior

In this situation, we assume the receiver node (R) is mis-behaving but still assigns backoff value to well-behavednodes. So the sender (S) is considered as a well-behavednode. This misbehavior may occur as R need S to sendmore data to it or it may purposfuly wants to discoargenode S from participating in network activites.

In order to cover its misbehavior, R assigns a backoffvalue for S that is smaller or larger than what S expects.To evaluate the expected backoff, node S computes theaverage of backoff values it has recently assigned to othernodes and the average of the backoff values other nodeshave assigned to it as P1 and P2. Br is a backoff value nodeS has assigned to node i, and B s is the value node i has

assigned to S. n demonstrate number of participatingnodes.

(4)

n n

ri si

i=1 i=1

1 2

B B

P = and P =n n

The expected backoff value (Bexp) is the average of P1 and P2. This expected backoff is then compared with theactual Backoff value (Bact) that R has assigned to S. Toeliminate any mistake, a counter is dedicated and theconditions in (2) and (3) are checked the same as condi-tion 1. If R is detected to be misbehaving, then the reac-

tion method is lunched.

4.3 Situation 3: Detection Scheme Based onSender and Receiver Both Misbehavior

Instead of the receiver assigning the backoff values tosender node, in this situation, the backoff assignment isdone by a neighbor of the sender which is considered tobe the most trustful node. This scheme is inspired from[16] and is implemented in any situation in which collud-ing nodes exists. As a result, this scheme is more power-ful in comparison to the two previous ones.

To demonstrate which neighbor could be the trustfulnode, a trust value is assigned to each neighbor node. In






[16] the trust value (T = f (C, S)) is a function of credit andstability values of the nodes. Unlike [16], to reduce com-putation overhead, in our scheme the trust value is com-puted based on the transmission behavior of nodes. Con-sequently, the node that performs malicious behavior(i.e., dropping packets) has the smaller trust value.

One assumption is that the trustful node is in neigh-borhood of the Sender (S). In Fig. 1 nodes n1, n2 and n3are neighboring nodes. While node n1 can directly moni-tor n3, n2 as an intermediate node between n1 and n3 canalso provide n1 with information about n3.

Fig. 1. Neighboring concept in an example MANET.

To compute the trust value of each neighbor node thebelow conceptions are explained:

Pexp (j): Number of packets that are expected to beforwarded by node j. This is calculated according to(5), by subtracting the number of packets with node

j as their destination (Pdes (j)) from all incomingpackets to node j (Pin (j)). Node j can be a destina-tion of a packet or as an intermediate node it may

forward the packets to the destination. As thetransmission of the node is the baseline for compu-ting the trust value, only those packets are taken in-to consideration, that are forwarded by node j.

Pexp (j) = Pin (j) – Pdes (j) (5) Pact (j): Number of packets that are actually for-

warded by node j. This is calculated according to(6), by subtracting the number of packets with node

j as their source (Psrc (j)) from all outging packets ofnode j (Pout (j)). Node j can be a generator of a pack-et as a source node or as an intermediate node itcan forward the packets to the destination. Onlythose packets are taken into consideration, that are

forwarded by node j.Pact(j) = Pout (j) – Psrc (j) (6) The trust value computed by a node is defined as T(j).

act

exp

P (j)T(j)=

P (j)(7)

If node i wants to calculate the trust value of node j andhas n common neighbors with it then in order to calculatethe total trust value two parameters are defined:

Tmain (j): The main trust value that is directly calcu-lated by node i for node j.

Tsup (j): The supplementary trust value that is calcu-

lated by node k and is indirectly recommended tonode i

Consequently, the total trust value, Ttotal (j), is computedaccording (8).

n

total main sup

k=1

T (j)= αT (j) + β T (j) (8)

α and β are two weighting parameters. As the role ofTmain (j) in formula is bolder than Tsup (j), α and β are de-fined in a way that α > β and α + β = 1.

In this scheme, each node maintains a trust table con-taining its neighbor nodes’ trust values. The table is re-freshed based on neighbors’ behaviors. It is required thatthe sender node (S) searches through its trust table andchooses the neighbor that has the largest trust value (Ttotal)in the trust table as its trusted neighbor. Then S broad-casts the RTS to request the channel, and specifies the IDof the trusted neighbor. After receiving RTS from S, thetrusted neighbor replies the CTS with the random backoffvalue. It is the responsibility of the trusted node to detectany misbehavior. By using a scheme similar to what de-scribed in section 4.1 and by comparing the expectedbackoff value assigned by trusted node and the actualbackoff value used by S, the trusted neighbor will judgewhether S is a misbehaved node or not. If S is detected tobe misbehaving then the reaction scheme is triggered.

4.4 Reaction Scheme

After detecting a node as a misbehaving one, the reactionscheme is triggered. The primary goal of our reactionstrategy is to mitigate the negative effects of selfish nodein network (e.g. having an unfair share of bandwidth). Inour reaction method a response is triggered by all well-behaved nodes. One approach to achieve this goal would

be for the well-behaved nodes to accurately estimate thelevel of misbehavior of the selfish node, and try to repli-cate that misbehavior as a reaction response.

The proposed reaction method is inspired from themeaningful Nash equilibrium outlined in [13] and theaggressive reaction approach in [17]. In the case of anydetected misbehavior in network, well-behaved nodeschoose the same backoff value as the one the misbehavednode has chosen to cover its greedy goal.

To hinder network collapse two factors are taken intoconsideration:

Maximum Level of Misbehavior (MaxM): The max-imum tolerable misbehavior in network. After

reaching MaxM, the reaction strategy changes andthe misbehaving node is completely ignored byother nodes in any routing process. This factor iscomputed based on the actual backoff value.

Minimum Level of Misbehavior (MinM): The min-imum tolerable misbehavior in network. Afterreaching MinM, the reaction strategy changes andthe misbehaving node is completely ignored byother nodes in any routing process. This factor isalso computed based on the actual backoff value.

Assuming that the well-behaved nodes are able to detectthe level of misbehavior in the network, we analyze theimpact of the proposed reaction method on throughput of






all nodes and show the results in our simulations in nextsection.

5 SIMULATION RESULTS

In this section, we report our simulation results by usingthe OPNET 14.5 network simulator [20]. We consider anetwork consisting of 10 MANET nodes in a 100m x 100marea. Nodes are within transmission range of each other. 9out of 10 (named Node_1 to Node_8) nodes are generat-ing traffic to a destination node. There is one selfish node(named Node_Mis) in the network. The nodes (includingthe selfish one) are source of CBR traffic of packet size of512 bytes. Using DSR as the basic routing protocol in thisnetwork, we use random waypoint as mobility model.We also implement heavy load traffic that is correspond-ing to an exponential packet arrival rate of 100 packetsper second. The results are averaged over 20 simulations,180 seconds each.

In our simulation, at first we evaluate the impact ofMAC layer misbehavior scenarios, discussed in section

3.3. Then we show the performance of our proposed miti-gating scheme.

5.1 Evaluating MAC Layer Misbehavior Scenarios

To evaluate the impact of each type of misbehavior sce-narios mentioned previously on our network, we com-pute the fluctuation in the throughput of the selfish nodein comaprsion to the scenario when all the nodes are well-behaved as described in [17]. Efficiency rate is computedas (9) where Twell demonstrates the throughput of a nodein a network without any misbehavior, while Tmis showsthe throughput of that node when it is misbehavior.

mis well

well

T - Tε= ×100

T (9)

Fig. 2 depicts the efficiency rate achieved using CWmanipulation using a variable misbehavior type at vari-ous values of δ. We observe that there is a non-linear in-crease in efficiency rate with a decrease in the value of δ below 1. After a while, this gain saturates, as the node isnot able to get all its data across successfully. This showsthat further decreasing of the variable δ does not lead toany more throughput gains for the selfish node. From Fig.2, we can also conclude that for δ > 1, the throughput gainof the selfish node is decreased as ε becomes negative. Allthese observation are also hold true for other misbehaviortypes.

Fig. 3 shows the efficiency rate of misbehavior type ofCW manipulation using a fixed value. Under saturatedtraffic conditions with no misbehavior, the average valueof contention window used by a well-behaved node is 50.As a result when CWfix > 50 there is no throughput gainfor misbehaved node.

Fig.4 depicts the CW Cheating upon unsuccessfultransmission misbehavior. The efficiency rate for this sce-nario is increased with a decrease in β for 1 < β <2. As β is decreased below 1, the efficiency rate increases rapidlytill saturation.

Fig. 2. CW manipulation using a variable misbehavior.

Fig. 3. CW manipulation using f ixed value misbehavior.

Fig. 4. CW Cheating upon unsuccessful transmission misbehavior.

Fig. 5 demonstrates the ε achieved in Backoff cheatingusing fixed value misbehavior. A decrease in throughput

for the misbehaving node can be observed for values ofbackoff greater than 20.

In Fig. 6, we directly show the throughput decrease of awell-behaved node in NAV duration manipulation mis-behavior. By increasing the NAV duration of a well-behaved node, the throughput share of it decreases signif-icantly.

In Fig. 7, we show the effect of implementing CW ma-nipulation upon receiving a routing packet using DSRprotocol. The impact of this misbehavior is like other typeof misbehavior scenarios.






Fig. 5. Backoff cheating using fixed value misbehavior.

Fig. 6. NAV duration manipulation misbehavior.

Fig. 7. CW manipulation upon receiving a routing packet using DSRprotocol.

5.2 Evaluating the Performance of MitigationMethod

Assuming that the well-behaved nodes are able to detectthe level of misbehavior in the network, we analyze theimpact of the proposed mitigating scheme on throughputin the network. We introduce three scenarios:

Scenario I: All nodes are behaving normally Scenario II: One node is misbehaving when imple-

menting CW manipulation using a variable misbe-havior type.

Scenario III: The mitigating scheme is implemented

Fig. 8 depicts the throughput of each node for scenarioI. It can be observed that all nodes have almost the equalshare of throughput. The result of implementing CWmanipulation using a variable misbehavior type isdemonstrated in Fig. 9. As it can be observed, the misbe-having node has gained an obvious unfair share ofthroughput. In Fig. 10, the mitigating method is imple-mented, which is a combination of any detecting ap-proaches and the reaction scheme, we described previ-ously.

Fig. 8. Scenario I: All nodes are behaving normally.

Fig. 9. Scenario II: One node is misbehaving.

Fig. 10. Scenario III: The mitigating scheme is implemented.






The reaction response could degrade the overallthroughput, however, the misbehaving node’s through-put also reduces to the levels available to other well-behaved nodes and all the nodes in the network are ableto achieve a fair share of the throughput.

The simulation results of this part showed the efficiencyof our proposed scheme in mitigating MAC layer misbe-havior.

6 CONCLUSTION AND FUTURE WORKS

We introduced various types of MAC layer misbehaviorsand studied their negative impacts on throughput of oth-er nodes in MANET. To nullify misbehaviors resultingfrom deviation of the backoff computation rules in IEEE802.11 MAC protocol, we presented a mitigating solution.As a combination of detection and reaction schemes, theobjective of the proposed method is to ensure obtaining afair share of throughput by all the nodes in MANET.

Our method is resistant to the existence of colludingnodes. Besides, its efficient reaction technique has made it

disadvantageous for selfish nodes to deviate from MACprotocol without completely isolateing the node. Thesimulation results show that by implementing our meth-od, the negative effects of a misbehaved node is alleviatedeffectively and all nodes gain a fair share of network re-sources.

For future work, towards minimizing the incorrect de-tection of misbehaving nodes, we will enhance our meth-od to use throughput observes for the estimation of mis-behavior type. We also aim to optimize our solution in away that it could tackle the problem of not only selfishmisbehavior, but also malicious one in both MAC andnetwork layer. The computation overhead is also a chal-

lenging problem that is worthwhile for further researches.

REFERENCES

[1] D. Djenouri, L. Khelladi, N. Badache, “A survey of security issues

in mobile ad hoc networks,” IEEE Communications Surveys 7(4).

Fourth Quarter, 2005.

[2] L. Buttyan and J. Hubaux, “Nuglets: a virtual currency to stimu-

late cooperation in self-organized ad hoc networks,” Swiss Fed-

eral Institute of Technology, Lausanne, Department of Commu-

nication Systems, Tech. Rep. DSC/2001, 2001.

[3] S. Zhong, Y. Yang and J. Chen, “Sprite: A simple, cheat proof,

credit-based system for mobile ad hoc networks,” Proceedings of

IEEE INFOCOM’03, vol. 3, San Francisco, CA, pp. 1987–1997, 30

March–3 April 2003.[4] S. Buchegger and J.L. Boudec, “Performance analysis of the

CONFIDANT protocol: Cooperation of nodes fairness in dy-

namic ad-hoc networks,” Proceedings of IEEE/ACM Symposium

on Mobile Ad Hoc Networking and Computing (MobiHOC), Lau-

sanne, CH: IEEE, pp.226-236, June 2002.

[5] Q. He, D. Wu and P. Khosla, “SORI: A secure and objective

reputation-based incentive scheme for ad-hoc networks ,” Pro-

ceedings of IEEE Wireless Communications and Networking Confer-

ence (WCNC2004), vol. 2, pp. 825–830, IEEE, March 2004.

[6] P. Michiardi and R.Molva,”CORE: A collaborative reputation

mechanism to enforce node cooperation in mobile ad hoc net-

works,” Institut Eurecom, France, Tech. Rep. EURECOM+816,

December 2001.

[7] ISO/IEC 802.11, “IEEE Standard for Wireless LAN Medium

Access Control (MAC) and Physical Layer (PHY) Specifica-

tion,”1999.

[8] D.B. Johnson, D.A. Maltz and J. Broch, “DSR, The Dynamic

Source Routing Protocol for Multi-Hop Wireless Ad hoc Net-

works,” Ad Hoc Net. C. E. Perkins, ed., Addison-Wesley, pp.

139–172, 2001.[9] M. Raya, J.P. Hubaux, and I. Aad, “DOMINO: Detecting MAC

layer greedy behavior in IEEE 802.11 hotspots,” IEEE Transac-

tions on Mobile Computing, 5(12), 1691–1705, 2006.

[10] P. Kyasanur and N.H. Vaidya, “Selfish mac layer misbehavior

in wireless networks,” IEEE Transactions on Mobile Computing,

vol. 4, no. 5, pp. 502–516, Sep. 2005.

[11] L. Guang, C. Assi, and A. Benslimane, “Modeling and analysis

of predictable random backoff in selfish environments,” in Proc.

9th ACM international symposium on Modeling analysis and simula-

tion of wireless and mobile systems, Terromolinos, Spain, pp. 86–

90. 2006.

[12] L.Chen and J. Leneutre, “Selfishness, not always a nightmare:

Modeling selfish MAC behaviours on wireless mobile ad hocnetworks ,” In 27th International Conference on Distributed Compu-

ting Systems (ICDSC 07), pp.16, July 2007.

[13] M. Cagalj, S. Ganeriwal, I. Aad and J.P. Hubaux,” On selfish

behaviours in CSMA/CA networks,” Proc. IEEE INFOCOM ,

vol. 4. (pp.2513 – 2524), March 2005.

[14] S. Djahel, and F. Nait-Abdesselam, “Thwarting back-off rules

violation in tactical wireless ad hoc networks,” Proceeding of

IEEE Symposium on Computers and Communications (ISCC), pp.

417-422, June 2010.

[15] L. Guang, C. Assi, and Y. Ye, “Dream: A system for detection

and reaction against mac layer misbehavior in ad hoc net-

works,” Elsevier Computer Communications, vol. 30, no. 8, pp.

1841–1853, Jun. 2007.

[16] F. Shi, J. Baek, J. Song, and W. Liu,” A novel scheme to prevent

MAC layer misbehavior in IEEE 802.11 ad hoc networks ,” Tele-

communication Systems, Springer,2011, doi: 10.1007/s11235-011-

9552-y.

[17] V. R. Giri and N. Jaggi, “MAC layer misbehavior effectiveness

and collective aggressive reaction approach”, IEEE Sarnoff Sym-

posium, 2010.

[18] V. Gupta, S. Krishnamurthy, and M. Faloutsous, “Denial of

service attacks at the MAC layer in wireless ad hoc networks,”

Proc. MILCOM , vol.2. pp. 1118 – 1123, 2002.

[19] Y.C. Hu, A. Perrig and D.B. Johnson, “Rushing Attacks and

Defense in Wireless Ad Hoc Network Routing Protocols,”

Technical Report TR01-384, Department of Computer Science,

Rice University, 2002.

[20] Opnet Technologies., OPNET Modeler, http:// www.opent.com.

Dr. Sam Jabbehdari currently working as an assistant professor atthe department of Computer Engineering in IAU (Islamic Azad Uni-versity), North Tehran Branch, in Tehran, since 1993. He receivedhis both B.Sc. and M.Sc. degrees in Electrical Engineering Tele-communication from K.N.T (Khajeh Nasi Toosi) University of Tech-nology, and IAU, South Tehran branch in Tehran, Iran, in 1988through 1991 respectively. He was honored Ph.D. Degree in Com-puter Engineering from IAU, Science and Research Branch, Tehran,Iran in 2005. He was Head of Postgraduate Computer Engineering






Department IAU North Tehran Branch during 2008-2012. Dr.Jabbehdari has been a supervisor of 32 theses and has publishedextensively in many national and international conferences and jour-nals, with over 35 papers published. He also has written “AdvancedTopics in Computer Networks” book in Persian Language (Tehran,Classic, 2009). His current research interests are Scheduling, QoS,MANETs, Wireless Sensor Networks and Grid Computing Systems.

Anahita Sanandaji received her BSc degree in Computer SoftwareEngineering as a distinguished student from Islamic Azad UniversityNorth Tehran Branch (IAU-TNB), in 2008. She is now an MSc stu-dent at Computer Engineering Department of IAU-TNB. Her re-search interests include but are not limited to wireless and mobile adhoc networks and security. Most of her current work is about devel-oping efficient mechanisms for detecting and preventing misbehav-iors in mobile ad hoc networks at network and MAC layer.

Dr. Nasser Modiri received his M.S. Degree from the University ofSouthampton, U.K, and Ph.D. degree from the University of Sussex,U.K in 1986 and 1989, respectively. In 1988 he joined The Network-ing Center of Hemel Hempstead, and in 1989 he worked as a Princi-ple Engineer at System Telephone Company (STC) Telecommunica-tion Systems, U.K. Currently, Dr. Modiri is teaching actively MSccourses in network designing, software engineering and undertakingmany MSc projects. He also participates in developing applicationsfor virtual Universities, Virtual Parliaments, Virtual Organization,

ERP, GPS+GSM, GPRS, RFID, ISO/IEC 27000, ISO/IEC 15408technologies.




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Evaluating and Mitigating the Effects of Selfish MAC Layer Misbehavior in MANETs

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