Trust aware cooperative routing method for WANETs

Transcription

1 SECURITY AND COMMUNICATION NETWORKS Security Comm. Networks 2016; 9: Published online 7 February 2017 in Wiley Online Library (wileyonlinelibrary.com) RESEARCH ARTICLE Trust aware cooperative routing method for WANETs P. Raghu Vamsi* and Krishna Kant Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India ABSTRACT Wireless Ad-hoc NETworks are prone to various internal security attacks such as black hole, gray hole, packet modification, etc., because of decentralized and open network operations. Developing soft security systems using trust as the metric has gained significant attention in the security research. These models work in conjunction with routing protocols for mitigating internal security attacks. To this end, this paper presents a behavior-based trust model for cooperative routing in Wireless Ad-hoc NETworks. The proposed trust model has been used with ad-hoc on-demand distance vector (AODV) protocol (hereinafter Behavior based Trust-aware Adhoc On-Demand Distance Vector Routing (BT-AODV)) to identify and isolate malicious nodes from the routing process. The performance of BT-AODV is evaluated against recently proposed trust and energy based-aodv protocol through ns-2 simulations. The simulation results show that BT-AODV is robust in detecting malicious nodes, and the network performance metrics such as packet delivery ratio, routing load, end-to-end delay, and energy consumption have been significantly improved as compared with trust and energy based-aodv protocol. Copyright 2017 John Wiley & Sons, Ltd. KEYWORDS Ad-hoc networks; AODV; AOTDV; adaptive weighing; routing security; security attacks; trust models; TE-AODV; WANETs *Correspondence P. Raghu Vamsi, Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, India. prvonline@yahoo.co.in 1. INTRODUCTION Wireless Ad-hoc Networks (WANETs) are the special class of wireless networks popularly used to establish ad-hoc communication in the applications like disaster management, battlefield, etc. [1]. These networks are composed of wireless mobile nodes having limited transmission range and energy. The topology of the network is dynamic because of node mobility. Limited transmission range of nodes restricts them to follow multi-hop communication to route the packets from the source to the destination. It means that the intermediate nodes between the source and destination need to be cooperative to accomplish the routing decisions. However, the limitations of WANETs such as decentralized network operations, dynamic topology, openness, and remote deployment can raise various security vulnerabilities and prone to internal security attacks such as Blackhole, Gray-hole, packet modification, etc. [2]. Conventional cryptography methods are proven efficient in mitigating security attacks posed from outside of the networks [3 6]. Moreover, using cryptography methods, it is very difficult to identify if a valid and legitimate node misbehavior during the routing process. In recent years, developing soft security systems using a human behavior pattern called trust has received considerable attention in the security research to mitigate the internal attacks. Trust is used to define the degree of belief about the behavior of an entity [7]. In WANETs, a node assesses the trust of its neighboring nodes on the basis of cooperative behavior shown in packet forwarding. When a node forwards the packet to its neighbor, it observes the packet forwarding behavior of neighboring node using the promiscuous use of the network interface. The trust toward nodes is calculated using these observations. Trust value of a node increases with respect to the positive behavior (such as successful packet forwards) and decreases with respect to negative behavior (such as packet drops or tampering packet integrity). Finally, the calculated trust values are used in association with routing protocols to bypass the malicious nodes from routing path establishment. To this end, the current study presents behavior based trust-aware cooperative routing method for WANETs. It is an integrated trust model that calculates the Consolidated Trust Value (CTV) using direct and indirect observations. The proposed trust model has been incorporated into wellknown ad-hoc on-demand distance vector (AODV) routing protocol [8]. The features of the trust model are as follows: Copyright 2017 John Wiley & Sons, Ltd. 6189

2 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT Adaptive weight assessment to trust metrics. Calculating direct trust value using node behavior. Reporting indirect trust values without communication overhead. Calculating the indirect trust value by discarding false recommendation. CTV calculation using direct and indirect trust values. Identifying energy efficient and trusted path between source and destination using the CTV. The remainder of the paper is organized as follows. Section 2 presents the related work. Section 3 describes the network and the adversary model. The behavior-based trust model for cooperative routing in WANETs is presented in Section 4. The performance of the proposed trust model with AODV protocol is evaluated using simulation study in Section 5. Finally, Section 6 concludes the paper with the future scope. 2. RELATED WORK There are various trust models proposed in the literature for use in association with routing protocols. The concept of trust is not a new topic; however, it has been used in various fields such as psychology, sociology, anthropology, economics, political science, and computer science related fields such as e-commerce, social networks, etc. [9 11]. Trust concepts have received considerable attention in communication networks to ensure the security in the network operations such as routing, data aggregation, and others. Broadly, trust models can be classified into two categories such as direct and indirect trust models. In direct trust models, the trust value of a node is calculated solely based on direct observations. That means trust value is calculated with the subjective assessment of the node behavior. However, along with the packet forwarding, a node has to perform several other operations such as localization, route maintenance, cluster formation, etc. based on the protocol under use. Hence, a node may skip some observations while performing intended operations. In such cases, collecting indirect trust values help in strengthening the opinion toward a node. For this, a node can obtain trust information indirectly by collecting trust opinions of neighboring nodes in a distributed fashion or by receiving recommendations from trusted third parties in a centralized or hierarchical fashion. The goal of either of the models is to calculate consolidated trust values to mitigate potential risks such as malicious, dead, or ambiguous paths. The trust value can be useful to circulate a warning or alarm message among friend nodes. In case, if the trust value is very low then the node will be isolated from the network operations [12,13]. Co-operation of Nodes Fairness In Dynamic Ad-hoc Networks (CONFIDANT) [14] and A Collaborative Reputation Mechanism (CORE) [15] are the primary works toward developing trust models for secure ad-hoc routing. These models are composed of several components such as watchdog, path rater, trust manager, and reputation manager for trust derivation, trust computation, and trust application. In trust derivation, each node collects evidence related to the network activities carried out by its neighboring nodes. Trust ratings are computed with the collected evidence. Computed trust ratings are applied in the routing process to improve the capability of suspecting malicious nodes. The effectiveness of these models has been evaluated by incorporating them in Dynamic Source Routing (DSR) Protocol [16]. DSR protocol is composed of two mechanisms: route discovery and route maintenance. The route from the source to the destination is identified when it is required. The identified paths may not be consistent because of the mobility of nodes. Therefore, route maintenance mechanism is responsible for maintaining and identifying new routes in case of old routes fail. In this context, the calculated trust value is used in establishing and maintaining trusted paths. Pirzada et al. [17] proposed a direct trust model and evaluated its performance with reactive protocols such as AODV, DSR, and Temporally Ordered Routing Algorithm (TORA) [18]. It is observed from the simulation results that trusted TORA protocol is robust in routing packets by bypassing the malicious nodes. Venkatraman et al. [19] proposed vector autoregression-based trust model. It is a direct trust model, and it calculates the trust value using time series analysis. This method addresses multiple attacks such as packet forwarding, content modification, rushing, and flooding attacks. This method has been used with AODV and Optimized Link State Routing (OLSR) [20] protocols. In [21], the AODV protocol has modified to mitigate the Black-hole attacks. In this method, each node sets the timer to buffer route reply messages and analyze the messages once the timer expires. Node filters the false reply messages and blocks the nodes from which such messages are received. It has observed that the packet delivery ratio has been improved in the presence of Blackhole attacks; however, the end-to-end delay has been increased because of the buffering mechanism. Lie et al. [22] proposed ad-hoc on-demand trusted path distance vector (AOTDV) protocol. It is a direct-trust model that calculates the trust values using two trust metrics such as Control packets Forwarding Ratio (CFR) and Data packets Forwarding Ratio (DFR). Each node running AOTDV keeps track of these two trust metrics. Heuristic weight values are assigned to CFR and DFR. For each trust update interval, the total trust value is calculated as the sum of the products of trust metrics (CFR and DFR) and the corresponding weight values. The best trusted path is selected as the highest product of the trust values of nodes between source and destination. This way of path selection identifies multiple trusted paths between source and destination. Hence, the AOTDV protocol follows multipath strategy to route the packets. Recently, Venkanna et al. [23] proposed trust and energy based ad-hoc ondemand distance vector (TE-AODV) protocol to improve AOTDV protocol. In this model, each node calculates Final Trust Value using direct and indirect trust values. The trusted path between the source and destination is selected using Final Trust Value and hop count. Initially, the trust 6190 Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.

3 P. R. VAMSI AND K. KANT Trust aware cooperative routing method for WANETs Table I. Security features of AOTDV [22], TE-AODV[23], and BT-AODV(proposed). Security feature Trust model AOTDV TE-AODV BT-AODV Black-hole attack Yes Yes Yes Gray-hole attack Yes Yes Yes Modification attack Yes No Yes Bad-mouth attack No No Yes Lifetime consideration No Yes Yes False-energy attack No No Yes Selfish behavior No Yes Yes On-Off attacks No No Yes AOTDV, ad-hoc on-demand trusted path distance vector; TE-AODV, trust and energy based ad-hoc on-demand distance vector. value of each node is set to 0.5, and the value will be updated for every fixed trust update interval. Trust value is scaled in [0,1]. The direct trust value is calculated based on packet forwarding behavior of nodes. Indirect trust opinions are requested when a nodes trust value is less than 0.5. This model considers two potential security attacks such as Black-hole and Gray-hole attacks. However, broadcasting the indirect trust request (TRREQ messages) and receiving the recommendations from neighboring nodes (TRRES message) increases the communication overhead. Further, a bad-mouth attacker may provide false recommendations for benign nodes to pollute the indirect trust values. A Heuristic Approach based Trust Worthy Architecture for Wireless Sensor Networks (WSN) is proposed in [24]. Heuristic Approach based Trust Worthy Architecture considers the challenges of the trust system and focuses on the collaborative mechanism for trust evaluation and maintenance. This architecture is capable of fulfilling security, reliability, mobility, and performance requirements for reliable communication while being readily adaptable to different applications. Further, trust models are also developed to guard geographic routing protocols [25 28]. It is observed from the literature that each trust model has its own advantages and limitations. It means the trust model that addresses multiple attacks is limited. To this end, the current study presents an integrated trust model to address multiple attacks. The trust model has been incorporated in AODV protocol. It is named as BT-AODV protocol. The security features of AOTDV [22], TE-AODV [23] and the proposed BT-AODV protocols are given in Table I. 3. NETWORK MODEL, ADVERSARY MODEL, AND ASSUMPTIONS 3.1. Network model and assumptions An ad-hoc network is considered in which the network consists of mobile nodes with fixed transmission range. Each node communicates among them only if they are in communication range of each other. Each node periodically broadcast a hello message consisting of node identity, remaining energy value, and the sequence number of hello packets. The information provided in the hello packets are used to maintain neighbor table. Each node keeps track of the fulfillment of routing activities by their neighboring nodes via the promiscuous use of the network interface. Further, each node calculates the consolidated trust value using direct and indirect observations. The trust value of each node ranges in [0,1]. Initially, each node is assigned the trust value of 0.5. Nodes having trust value greater than or equal to 0.5 are regarded as benign nodes and less than 0.5 are considered as malicious nodes Adversary model It is assumed that there are no malicious nodes during the initial stages of the network operations. However, adversary activities start as the network operations progress. The most fundamental and active security attacks on routing are considered for the study. It is because ignoring them can lead to very powerful attacks such as wormhole attacks and show an adverse impact on the network performance metrics. Security attacks are [2] Black-hole attack : A malicious node creates an impression as the next node to forward packets. When it receives the packet, it drops. Packet-modification attack: A malicious node modifies the packet integrity by tampering its unique code or hash code so that a receiving node discards the packet as invalid. Gray-hole attack: It is a variant of Black-hole attack in which a malicious node selectively drops the packets and/or tampers the packet integrity. Selfish-behavior attack: It is a kind of non-cooperative behavior. A selfish node does not show interest to participate in routing process to save the resources such as energy and bandwidth. Bad-mouth attack: It is a severe threat to the reputation system. A bad-mouth attacker provides false recommendations to damage well-behaving node s reputation by continuously advertising poor trust value. False-energy attack: A malicious node reports false energy information to mislead a benign node from choosing an energy efficient path. On-Off attack: A malicious entity behaves good and bad alternatively to remain undetected while causing damage to the network. 4. TRUST-AWARE COOPERATIVE ROUTING METHOD 4.1. Trust metrics selection The trust metric is a parameter with which security attacks are identified. The cooperative behavior of nodes is a Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd. 6191

4 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT vital factor during the routing process. This behavior is assessed by careful observation of routing activities fulfilled by nodes. In order to detect routing attacks mentioned in Section 3.2, appropriate trust metrics has to be defined. As described, Black-hole, Selfish, and On-off attacks can be detected with the metrics such as sincerity in packet forwarding and providing network acknowledgment. It means with these metrics, dropping of packets can be identified. In addition to packet dropping, gray-hole, and packet modification attacks can be identified by observing the trust metric such as sincerity in maintaining packet integrity. Further, energy information is an important parameter when the trust model considers the lifetime of nodes. Periodic validation of energy information provided by nodes helps in avoiding dead paths, energy holes, and Selfish nodes. When a trust model receives recommendations (or indirect trust values) from its neighbors, it is very important to check the validity of recommended trust values. Utilizing the indirect trust values by discarding false recommendations helps in identifying bad-mouth attacks. The energy information verification and recommendation trust verification has been presented in the Sections 4.2 and 4.3, respectively. To summarize, the list of trust metrics considered for the study are sincerity in packet forwarding (m 1 ), maintaining packet integrity (m 2 ), network acknowledgments (m 3 ), energy information (m 4 ) and recommendations (m 5 ). The values of these trust metrics are initialized to 1. Direct trust calculation using these trust metrics is provided in the next section Direct trust calculation Consider a routing scenario shown in Figure 1. Let S, A, B, C, D, E, and F be the mobile nodes in which S is the source and D is the destination. The thick edge between nodes represents the trusted path and the dashed line represents a malicious path. When S has to send a packet to D, it chooses C as the next node to forward because next hop to C is D. When a route is available, S stores the copy of the forwarding packet, its corresponding sequence number, and time-stamp in its packet buffer. When A receives the packet from S, it first checks for packet integrity. If the integrity check is successful then node forwards further otherwise, it drops the packet as invalid. When A forwards the packet further, S passively listens the packet (because S is in the transmission range of A) and updates the fulfillment of trust metrics m 1, m 2 and m 3. To do this, each node maintains success (S count ) and failure (F count ) counters for each trust metric. When a node fulfilled a trust metric, then its corresponding S count is incremented, otherwise corresponding F count is incremented. Along with these metrics, each node periodically verifies the validity of energy information received in the hello packets. Let ET x, ET r, E 0, E c, and P c be the energy consumed for packet transmission, energy consumed for packet reception, initial energy, energy consumption information, and packet count (including control and data packets), respectively, then the inconsistencies in the energy information is identified as follows. E 0 E c <P c *(ET x + ET r ) (1) Node records energy information reported by a neighboring node as valid when the Equation (1) is satisfied and then increments m 4 corresponding S count, otherwise its F count will be incremented. Similarly, each node checks Figure 1. Routing scenario. RREP, route reply; RREQ, route request Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.

5 P. R. VAMSI AND K. KANT Trust aware cooperative routing method for WANETs for validity of recommendation trust values (presented in Section 4.3) and accordingly corresponding counter values of m 5 are incremented. In this way, each node updates the counters when they passively observe the forwarded packets. The direct trust value is calculated for every fixed time interval (also called trust update interval) using these counters. The Direct Trust (DT) value calculation consists of two components: (i) weight assessment; and (ii) expectation calculation. During weight assessment, a weight value will be assigned to each trust metric based on S count value as follows: W(m i )= S count (m i ) P 5i=1 S count (m i ) Where, W(m i ) is the weight to be assigned to the trust metric i. With Equation (2), a trust metric having highest success count will be given more weight as opposed to heuristic weight assignment. In this way, weight assessment provides the priority of a trust metric over remaining trust metrics. When multiple attacks are present in the network, each malicious node will be having independent attack profile. In such cases, the adaptive weighing mentioned in Equation (2) helps in improving the accuracy of trust calculation instead of assigning a fixed weight value to the trust metrics. To this end, it is also important to assess the expected behavior of a node with respect to a particular trust metric. To this end, the expected behavior is calculated using Beta expectation function [29] as follows: E(m i )= S count (m i )+1 S count (m i )+F count (m i )+2 Where, E(m i ) is the expectation value. Because weight value is the priority of trust metric over remaining metrics, and expectation is the behavior assessment pertaining to a trust metric, combining these two values will provide more effective trust value. So, using weight and expectation values, the DT value of a node j is calculated as follows: DT(j) = (2) (3) 5X W(m i )*E(m i ) (4) i=1 Where, DT(j) is the direct trust value. Because the weight and expectation values remain in [0,1], the DT value also remains in [0,1] Indirect trust calculation The direct trust value is sufficient to assess node behavior. However, during mobility, a node may discover new nodes, and its old neighbors may disappear. In such cases, assessing the behavior of new nodes may become difficult. In some cases, obtaining the second opinion about a node having less trust value can also help in assessing exact trust value. Hence, obtaining trust recommendations from neighboring nodes and combining them with the DT value helps in improving the quality of routing decisions. In the existing trust models, the recommendations are obtained in two ways: (i) using recommendation request (TRREQ) and response messages (TRRES); (ii) periodic broadcasting of recommendation values. However, these two methods increase the congestion in the network and hence lead to packet loss. Therefore, a lightweight method is required to report the indirect trust values. To this end, the current study presents a lightweight method to report the recommendations by piggybacking the recommendations along with outgoing data packets. At first, the calculated DT value of node j is rounded to an integer value as shown below: DR R (j) =ddt(j)*10e (5) Where, DT R (j) is the rounded integer value of DT(j). For example, if DT(j) is 0.68 then the DT R (j) becomes 7, to store or communicate this value, 4 bits are sufficient (because 4 bits supports from 0 to 15). The energy consumption of a node depends on the number of bits it transmits or receives. Therefore, this procedure significantly reduces the communication overhead and energy consumption. When a node has to forward the data packet, it prepares a list of nodes having trust value greater than the average trust value of its neighboring nodes and piggybacks the node identity and corresponding DT R values along with the data packets. Let n 1, n 2 :::n m be the neighboring nodes of a node j then the average trust value (DT avg ) is calculated as DT avg = P mk=1 DT(n k ) Because each node works with the promiscuous use of the network interface, it is easy to obtain the indirect trust values and update them by combining with DT values. It is also possible that a packet will route through badmouthing attackers. In such cases, the bad-mouth node can recommend false trust values. The quality of routing decisions can degrade if such recommendations are considered without validation. To this end, a simple validation method is presented in the current study. Consider that node j received the recommendations about one of its neighboring node k from node i, then j calculates the recommended trust RT(k) as follows DT(k)+DT(ji)* DT R (ik) 10 RT(k) = 1+DT(ji) Where, DT(k) is the DT value of node k, DT(ji) isthe DT of j on i, DT R (ik) is the recommended DT value of i on k. The reported value will be considered when the following condition holds m (6) (7) DT(k) RT(k) R th (8) Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd. 6193

6 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT Where, R th is the heuristic recommendation threshold value. Recall that the trust metric m 5 is related to sincerity in providing recommendations, when the Equation (8) is not satisfied, then the recommended value will be discarded and node j increments m 5 corresponding F count value for node i. Otherwise, m 5 corresponding S count value will be incremented. Further, recommended value is combined with DT value to calculate consolidated trust value Consolidated trust value calculation Once node validates the recommendation value, it is combined with DT value to calculate CTV as follows: CTV(k) =CF(k)*DT(k)+(1 CF(k))*RT(k)) (9) Where, CTV(k), DT(k), and RT(k), respectively, are the consolidated, direct, and recommended trust values of node k. CF(k) is the confidence factor on node k. In the current study, the CF value is inspired from the human trust model in which confidence on a person improves with respect to the number of positive interactions and degrades with respect to the number of negative interactions. Each node calculates the CF value using the total success count of the trust metrics under consideration as follows TS count (k) = 5X S count (m i ) (10) Where, TS count (k) is the total success count of node k. Using TS count value the CF value is calculated as follows: CF(k) = i=1 TS count (k) TS count (k)+1 (11) It can be observed from Equation (10) that the CF value increases with respect to increase in the total success count. Because CF and DT values remains in [0,1], the CTV value will also remain in [0,1]. The CTV is used in trusted path selection as described in the next section Trusted path selection Routing is the process of discovering route from the source to the destination. The CTV is used with routing protocol to identify the trusted path for making efficient routing decisions by isolating malicious nodes. To this end, the proposed trust model is used with well-known AODV protocol. The AODV protocol is a reactive routing protocol that discovers the route when it is required. In AODV protocol, the process of route establishment will be carried out using route request (RREQ) and route reply (RREP) messages. A node broadcasts RREQ message when it has to send the packet and the route to the destination is not available. The RREQ message consists of fields such as <broadcast ID, source address, source sequence number, destination address, destination sequence number, hop count>. Any node receives RREQ message searches in their routing table for destination address. If route to the destination is available then RREP message will be sent to the source node. Otherwise, node rebroadcasts RREQ packet by increasing the hop count. In this way, the RREQ message will be flooded in the network till it reaches the destination. The RREP message consists of fields such as <source address, source sequence number, destination address, destination sequence number, hop count, lifetime>. However, when a node receives multiple RREP message for a destination, it updates the route in its routing table having lowest hop count. The task of trust model is to calculate the CTV based on node s packet forwarding behavior and use it along with the route discovery process. Two new variables called total_trust and avg_trust are introduced in RREQ and RREP messages to record the trust values of intermediate nodes between source and destination. While flooding of RREQ message, the total_trust and avg_trust variables are set to 0 by each node. When the destination node receives RREQ message, it replies RREP message by initializing the total_trust and avg_trust variable to 1. All nodes in the reverse path from destination to source add the CTV and remaining energy information of the replying node to total_trust in RREP message and forward further. Let i =1,2,..m be the nodes in the reverse path, then total_trust and avg_trust are calculated as follows: total trust+ = mx CTV(i)+energy(i) (12) i=1 avg trust = total trust (13) m When a source node receives RREP messages, it updates the route in the routing table having highest avg_trust value in the routing table. In this way, trusted path will be selected from the source to the destination. Along with route discovery, AODV protocol performs route maintenance. It is initiated when route time is expired or link between two nodes is broken because of mobility. Nodes running the proposed trust model calculate the CTV of neighbors and label the nodes having CTV less than 0.5 as malicious in their neighbor table. During the route maintenance process, routes having such malicious nodes are identified and purged. A route error message will be sent out to initiate new route discovery between the source and destination. In this way, an energy efficient and trusted path will be maintained using route discovery and maintenance mechanisms. 5. SIMULATION STUDY The network simulator ns-2 [30] has been used to evaluate the performance of AODV [8], TE-AODV [23], and 6194 Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.

7 P. R. VAMSI AND K. KANT Trust aware cooperative routing method for WANETs BT-AODV (proposed) protocols. The following network performance metrics are considered for the evaluation. Packet delivery fraction: It is the ratio of the number of data packets received by the destination to the number of data packets sent by the source nodes. This metric attributes the dependability of the trust model. Routing load: It is the ratio of the number of control packets to the number of data packet generated in the network. End-to-end delay: It is defined as the time taken for a packet to reach the destination from its source. Energy consumption: It is the energy consumed by the nodes in the network in performing routing operations. Hop count: It is the number of hops traveled by the data packet between source and destination. Throughput: It is the average number of bits per second routed in the network. This metric shows the efficiency of the trust model Simulation setup Table II shows the important simulation parameters con- Table II. Simulation parameters. Simulator ns-2.35 [30] Examined Protocols AODV [8], TE-AODV[23], and BT-AODV Mac DCF Simulation Time 600 s Area 1kmx1km Nodes 50 Propagation model Two Ray Ground reflection Transmission range 250 m Initial energy 10 Joules Mobility model Random way point Maximum speed 5, 10, 15, and 20 m/s Traffic type CBR over UDP Maximum connections 15 Packet size 64 bytes Packet rate (drate) 4 packets/ond Maximum malicious 50% of total nodes nodes (i.e., 25 nodes) Type of attacks Security attacks addressed in Section 3.2. Trust update interval 0.02 s R th 0.15 AODV, ad-hoc on-demand distance vector; CBR, Constant Bit Rate; DCF, Distributed Coordinate Function TE-AODV, trust and energy based ad-hoc on-demand distance vector; UDP, Universal Datagram Protocol. sidered for the study. Two scenarios are considered for the evaluation by considering acceptable mobility speed (i.e., m/s in real environments [31]). The mobility scenarios are generated using setdest utility available in the ns-2 simulation tool. In scenario 1, the performance of protocols is evaluated in the presence of 50% of malicious nodes and with varying node mobility from 0 to 20 m/s with a step size of 5 m/s. In scenario 2, the performance of the protocols is evaluated with a fixed mobility speed of 20 m/s and with varying percentage of malicious nodes from 0% to 50% of the total nodes with a step size of 10%. All the security attacks addressed in Section 3.2 are considered for the study. The attacking nodes are chosen randomly. Each simulation scenario with all possible cases are simulated on 50 random graphs, and the average of the data obtained from simulation runs is presented in the result analysis Result analysis Scenario 1: 50% of malicious nodes with varying node mobility. Figure 2(a) (f) plots the node mobility speed versus performance metrics for AODV, TE-AODV, and BT- AODV protocols in the presence of 50% of malicious nodes in the network. The description of each performance metric is provided in the sequel. Figure 2(a) plots the packet delivery fraction (PDF). It can be seen from the graph that PDF of BT-AODV remains high across various mobility rates. It is because nodes running BT-AODV protocol establish trustworthy path using CTV and remaining energy of nodes. The TE-AODV protocol is not resistant to multiple attacks because it does not consider the bad-mouth attack, on-off attack, and verification of false energy information which disrupts choosing the best path. Because AODV protocol is trust unaware, it has resulted into low PDF. Hence, BT-AODV results into high PDF as compared with BT-AODV and AODV protocols. Figure 2(b) shows the routing load. It can be observed from the graph that routing load is high in TE-AODV as compared with BT-AODV and AODV protocols. It is because during the route maintenance phase, if a node observes malicious node in the routing table, then it purges the corresponding route and initiates the route discovery process. This procedure results in the generation of control packets. Hence, the control packets count in the network depends on the number of malicious nodes identified in the path. In addition, TE-AODV protocol sends out special request and response message to know about secondary trust values. To this end, BT-AODV protocol is placing efforts to identify the best path between source and destination when there is a maximum number of malicious nodes in the network. Hence, the routing load of BT-AODV protocol is low as compared with TE-AODV protocol. Figure 2(c) shows the end-to-end delay occurred in packet delivery. The end-to-end delay increases because Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd. 6195

8 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT Figure 2. Scenario 1: Performance metrics in the presence of 50% of malicious nodes and with varying node mobility.(a) Packet delivery fraction, (b) Routing load, (c) End-to-end delay, (d) Energy consumption, (e) Hop count, and (f) Throughput. AODV, ad-hoc on-demand distance vector; TE-AODV, trust and energy based ad-hoc on-demand distance vector. of the presence of selfish nodes and false information providers. It is because such malicious activities disrupt the route discovery process by not responding to control packets and misguiding the path discovery through energy holes. Nodes running BT-AODV protocol eliminates choosing malicious path using CTV and validated remaining energy information. BT-AODV protocol has the provision of filtering false recommendations and false energy information provided by the malicious nodes. Because such mechanism does not exist in TE-AODV protocol, it resulted in high end-to-end delay as compared with BT-AODV protocol. However, TE-AODV protocol can recognize black hole, gray hole, and selfish attacks; it can bypass such attackers from the routing path as compared with AODV protocol. Hence, the BT-AODV protocol results into low end-to-end delay as compared with TE-AODV and AODV protocols Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.

9 P. R. VAMSI AND K. KANT Trust aware cooperative routing method for WANETs Figure 2(d) shows the network energy consumption. It can be seen from the graph that the energy consumption is low in BT-AODV protocol across varying node mobility. BT-AODV protocol utilizes piggybacking method to report recommendation values. Further, direct trust values are rounded to the nearest integer values (using Equation (5)) to reduce the energy and communication overheads. Along with this, BT-AODV protocol can identify the false energy information provided by the malicious nodes. Because of these features, BT-AODV protocol is able to identify the energy efficient and trusted path between source and destination. TE-AODV protocol considers node energy information, but it has no provision of recognizing false energy information. Because AODV protocol is trust and energy unaware, the energy consumption is more across varying node mobility. Hence, BT-AODV protocol results into low energy consumption as compared with TE-AODV and AODV protocols. Figure 2(e) plots the hop count. The ability to identify the best path reflects the average number of hops traveled by the packets during the routing process. As explained before, and also observed from previous graphs. BT- AODV protocol has the ability to identify multiple attacks. It forwards packets via energy efficient path by isolating malicious nodes. This feature makes the packet travel through additional paths. Hence, it results in a marginal increase in hop count as compared with TE-AODV and AODV protocols. Figure 2(f) plots the network throughput. It can be observed from the graph that the network throughput depends on the PDF of the protocol under use. It can be observed from Figure 2(a) that the PDF of BT-AODV protocol is high when compared with TE-AODV and AODV protocol. Hence, high throughput can be observed with BT- AODV protocol as compared with TE-AODV and AODV protocols Scenario 2: 20 m/s node mobility with varying percentage of malicious nodes. Figure 3(a) (f) plots the percentage of malicious nodes versus performance metrics for AODV, TE-AODV, and BT-AODV protocols in 20 m/s node mobility in the network. The number of malicious nodes is varied in between 0% and 50% of the total nodes (i.e., 0 25 nodes) with a step size of 10%. The simulation has set in a way that the malicious nodes appear in an incremental fashion. The malicious nodes appear in the network from 60 s of the simulation time. From 0% malicious nodes, this percentage increases by 10% every 15 s. In this way, the malicious nodes population grows up to 50% of the total nodes. This simulation setting is used to test the dynamic behavior the proposed trust model. The description of each performance metric is provided in the sequel. Figure 3(a) plots the packet delivery fraction. It is apparent from the figure that the proposed BT-AODV protocol has shown high packet delivery across varying percentage of malicious nodes. Direct trust calculation with adaptive weight and expectation assessment with energy awareness resulted in the identification of malicious nodes dynamically by BT-AODV protocol. Any change in node behavior can be identified using adaptive weights and confidence factor. Further, the proposed trust model dynamically calculates the CTV using direct trust value, indirect trust value, and confidence factor. It results into choosing the paths dynamically by excluding malicious nodes during the route discovery process. AODV protocol has no method of trust calculation or bypassing the malicious nodes, and hence, it resulted into low PDF. Whereas, in TE-AODV protocol, the path selection is circumvented by fake information providers. It results into choosing the wrong paths and hence leads to low PDF as compared with BT-AODV protocol. It can be observed from Figure 3(b) that routing load of TE-AODV protocol keeps increasing with respect to increasing the percentage of malicious nodes. It is because of special reputation request and response messages. When malicious nodes are identified in the existing paths, TE- AODV protocol purges such paths and initiates a new route discovery process. Route discovery involves an exchange of control packets (such as RREQ and RRES messages) and thereby results in an increase in routing load. With this, it can be noted that the number of route discovery initiations increases with respect to identification of malicious nodes in already established paths. BT-AODV protocol reports the secondary trust opinions via piggybacking. Hence, such consideration results to less number of control packets generation and thereby results in low routing load as compared with TE-AODV protocol. AODV protocol considers each node as trustworthy (irrespective of malicious nodes) and establishes the path. Hence, it is completely unaware of malicious activities thereby results into general route discovery initiations. Hence, BT- AODV protocol incurred low routing load as compared with TE-AODV and AODV protocols. Figure 3(c) plots the end-to-end delay. It can be observed from the graph that AODV protocol has a high end-to-end delay. It is because of packet dropping by malicious nodes in the network. The packet retransmissions increase with respect to the number of packet drops and hence increase in end-to-end delay. In TE-AODV protocol, in addition to general route discovery and route maintenance phases, it requests for secondary trust opinion about nodes having trust value below 0.5 by sending special control messages called reputation request (TRREQ) and reputation response (TRRES) messages. Each node after sending TRREQ message has to wait for some amount of time to receive TRRES messages. In addition, because TE-AODV protocol has no provision of verifying false recommendations, it is easy for an adversary to appear in the routing path. The procedure of sending out special control messages for obtaining reputation values, waiting for reputation responses and waiting for the besttrusted path (i.e., path with high final trust value) increases the end-to-end delay as compared with BT-AODV protocol. However, BT-AODV protocol conveys the secondary Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd. 6197

10 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT Figure 3. Scenario 2: Performance metrics with varying percentage of malicious nodes. (a) Packet delivery fraction, (b) Routing load, (c) End-to-end delay, (d) Energy consumption, (e) Hop count, and (f) Throughput. AODV, ad-hoc on-demand distance vector; TE-AODV, trust and energy based ad-hoc on-demand distance vector. trust opinions by piggybacking them with outgoing data packets. Because each node establishes path by identifying benign nodes, the secondary opinions provided by each node in the path can be considered as trustworthy. Hence, there will be less chance of occurring bad-mouth attacks. However, if any such attackers are present, then the inconsistency check (using Equation (8)) can easily filter the false recommendations. Hence, BT-AODV protocol results into low end-to-end delay as compared with TE- AODV and AODV protocols. Figure 3(d) shows the network energy consumption. The energy consumption is directly proportional to the number of packets sent and received. It can be seen from the graph that AODV protocol has high-energy consumption as compared with TE-AODV and BT-AODV protocol. It is due to the number of packet retransmissions occurred because of packet drops by malicious nodes in the network. Although TE-AODV protocol considers the remaining energy information of nodes, because of the additional control messages sent for obtaining secondary trust values 6198 Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.

11 P. R. VAMSI AND K. KANT Trust aware cooperative routing method for WANETs (TRREQ and TRRES messages), the energy consumption is high as compared with BT-AODV protocol. As the number of malicious nodes increases in the network, a node initiates more secondary trust value requests and hence results in high-energy consumption. Whereas, in BT-AODV protocol, the secondary trust values of nodes having the trust value above the threshold are sent by piggybacking along the data packets once for every two trust update intervals. Because each node operates in promiscuous mode, they can easily validate and calculate the CTV using confidence factor. This procedure reduces the energy consumption. It can be observed from the figure that energy consumption of BT-AODV protocol keeps decreasing until 40% of malicious nodes. When 50% of malicious nodes are present in the network, BT-AODV protocol has recorded a hike in energy consumption because when there is a maximum number of malicious nodes BT-AODV protocol is putting best efforts to identify trusted path. In search of such trusted path, an additional amount of energy is consumed in the network. However, as compared with AODV and TE-AODV protocols, BT-AODV protocol has resulted in low energy consumption. Figure 3(e) plots the average number of hops traveled by the packets during the routing process. It can be observed from the graph that AODV protocol has high hop count because of the absence of trust calculations. It is also due to the reason that the attackers are disrupting the route establishment phase by making the protocol choose the longest path. In TE-AODV protocol, trusted path selection with final trust values and hop count helps in reducing the number of hops traveled by the packets. However, BT- AODV protocol is robust in recognizing multiple attacks; it searches for energy efficient and trusted path between source and destination for the best of effort delivery. The route length is increased in order to establish such energy efficient paths. Hence, it results in a marginal increase in high hop count in BT-AODV protocol as compared with TE-AODV protocol. Figure 3(f) plots the network throughput. It can be seen from the figure that high throughput is recorded in the network when BT-AODV protocol is employed. This is due to high packet delivery fraction by BT-AODV protocol. The throughput is proportional to the number of packets routed in the network. That means BT-AODV protocol is efficient in detecting and isolating malicious nodes from the routing path. With this, it can be concluded that network running BT-AODV protocol has resulted into high throughput across varying percentage of malicious nodes as compared with TE-AODV and AODV protocols Result summary The dependability and efficiency of any trust model is assessed based on its performance in the presence of a maximum number of malicious nodes present in the network. Table III shows the performance metrics such as packet delivery fraction, end-to-end delay, energy consumption, and throughput of AODV, TE-AODV, and BT- Table III. Performance comparison of AODV, TE-AODV, and BT-AODV protocols. Trust model Metrics AODV TE-AODV BT-AODV Packet delivery fraction Routing load End-to-end delay (sec) Energy consumption (Joules) Hop count Throughput (Kbps) AODV, ad-hoc on-demand distance vector; TE-AODV, trust and energy based ad-hoc on-demand distance vector. AODV protocol in the presence of as maximum of 50% of malicious nodes in the network in a mobile network with 20 m/s maximum node mobility. It can be interpreted from Table III that the packet delivery fraction of BT-AODV protocol has increased by 46.6% as compared with AODV protocol and 19.6% as compared with TE-AODV protocol. The routing load of BT-AODV protocol has recorded low as compared with TE-AODV protocol. It is due to the dynamic identification of malicious nodes. The process of purging malicious paths and special messages for obtaining secondary trust values increased the routing load in TE-AODV protocol. However, the end-to-end delay has recorded a substantial improvement when BT-AODV protocol is employed. It can be observed that end-to-end delay has improved by 66% as compared with TE-AODV protocol. In the same way, BT-AODV protocol delivered the packet with low energy consumption as compared with TE-AODV protocol. This is due to the feature of piggybacking the secondary trust values in BT-AODV protocol. In search of trusted and energy efficient path, the number of hops traveled by the packets has recorded a marginal improvement in BT-AODV protocol as compared with TE-AODV protocol. Finally, the BT-AODV protocol can be termed as efficient because it has recorded high throughput as compared with TE-AODV and AODV protocols. The throughput has increased by 19.6% (in accordance with PDF) as compared with TE-AODV protocol. However, marginal improvement in routing load and hop count can be admitted because of the accurate elimination of malicious nodes from routing path and care taken during the selection of the routing path. Hence, it can be concluded that BT-AODV protocol is more dependable and efficient for establishing cooperative routing in WANETs. 6. CONCLUSION AND FUTURE WORK In this paper, behavior-based trust model for cooperative routing in the wireless ad-hoc network has been proposed. 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12 Trust aware cooperative routing method for WANETs P. R. VAMSI AND K. KANT The proposed method has been integrated with the wellknown AODV protocol. It is said to be BT-AODV protocol. BT-AODV protocol calculates the Consolidated Trust Value (CTV) of nodes by combining Direct Trust (DT) and Indirect Trust values. The features of BT-AODV protocol such as adaptive weight assignment and expectation calculation for each trust metric enabled it to detect and isolate multiple attacks during the routing process. The CTV is used to identify trustworthy nodes to establish trusted and energy efficient path for cooperative data forwarding between source and destination. A simulation study using the network simulator NS-2 has been conducted to analyze the performance of the BT-AODV protocol. BT-AODV protocol has been compared against the recently proposed TE-AODV protocol and standard AODV protocol. It has been observed that using BT-AODV protocol important network performance metrics such as packet delivery fraction, end-to-end delay, energy consumption, and network throughput has recorded a significant improvement as compared with TE-AODV and AODV protocols. As a future work, the proposed trust model will be extended to detect malicious activities on routing such as wormhole and Sybil attacks. REFERENCES 1. Kiess W, Mauve M. A survey on real-world implementations of mobile ad-hoc networks. Ad Hoc Networks 2007; 5(3): Kannhavong B, Nakayama H, Nemoto Y, Kato N, Jamalipour A. A survey of routing attacks in mobile ad hoc networks. IEEE Wireless communications 2007; 14(5): Sanzgiri K, Dahill B, Levine BN, Shields C, Belding- Royer EM. A secure routing protocol for ad hoc networks. In Proceedings. 10th IEEE International Conference on Network Protocols, 2002, IEEE, Paris, France, 2002; Zapata MG. Secure ad hoc on-demand distance vector routing. ACM SIGMOBILE Mobile Computing and Communications Review 2002; 6(3): Li Q, Hu Y-C, Zhao M, Perrig A, Walker J, Trappe W. Sear: a secure efficient ad hoc on demand routing protocol for wireless networks. In Proceedings of the 2008 ACM Symposium on Information, Computer and Communications Security, ACM, Tokyo, Japan, 2008; Cerri D, Ghioni A. Securing AODV: the A-SAODV secure routing prototype. IEEE Communications Magazine 2008; 46(2): Capra L. Engineering human trust in mobile system collaborations. ACM SIGSOFT Software Engineering Notes, Vol. 29, ACM, New York, USA, 2004; Perkins C, Belding-Royer E, Das S. Ad hoc ondemand distance vector (aodv) routing. Technical Report, IETF (Internet Engineering Task Force) RFC 3561, Jøsang A, Ismail R, Boyd C. A survey of trust and reputation systems for online service provision. Decision support systems 2007; 43(2): Vamsi PR, Kant K. Systematic design of trust management systems for wireless sensor networks: a review. In Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), IEEE, Rohtak, India, 2014; Govindan K, Mohapatra P. Trust computations and trust dynamics in mobile adhoc networks: a survey. IEEE Communications Surveys & Tutorials, 2012; 14 (2): Mejia M, Pena N, Munoz JL, Esparza O. A review of trust modeling in ad hoc networks. Internet Research 2009; 19(1): Marti S, Giuli TJ, Lai K, Baker M. Mitigating routing misbehavior in mobile ad hoc networks. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, ACM, Boston, MA, USA, 2000; Buchegger S, Le Boudec J-Y. Performance analysis of the confidant protocol. Proceedings of the 3rd acm international symposium on mobile ad hoc networking & computing, Lausanne, Switzerland, 2002; Michiardi P, Molva R. Core: A Collaborative Reputation Mechanism to Enforce Node Cooperation in Mobile Ad Hoc Networks. In Advanced Communications and Multimedia Security. Springer: Portoroz, Solvenia, 2002; Johnson DB, Maltz DA. Dynamic Source Routing in Ad Hoc Wireless Networks. In Mobile Computing, Vol The Kluwer International Series in Engineering and Computer Science, Springer, 1996; Pirzada AA, McDonald C, Datta A. Performance comparison of trust-based reactive routing protocols. IEEE Transactions on Mobile Computing 2006; 5(6): Park VD, Corson MS. A highly adaptive distributed routing algorithm for mobile wireless networks. In Proceedings IEEE INFOCOM 97. Sixteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Driving the Information Revolution, Vol, 3, Kobe, Japan, 1997; Venkataraman R, Pushpalatha M, Rama Rao T. Regression-based trust model for mobile ad hoc networks. IET Information Security 2012; 6 (3): Security Comm. Networks 2016; 9: John Wiley & Sons, Ltd.