- Open Access
A unified approach for detecting and eliminating selfish nodes in MANETs using TBUT
© Josh Kumar et al. 2015
- Received: 29 January 2015
- Accepted: 24 April 2015
- Published: 23 May 2015
Recent years have witnessed the increasing efforts toward making architecture standardization for the secured wireless mobile ad hoc networks. In this scenario when a node actively utilizes the other node resources for communicating and refuses to help other nodes in their transmission or reception of data, it is called a selfish node. As the entire mobile ad hoc network (MANETs) depends on cooperation from neighboring nodes, it is very important to detect and eliminate selfish nodes from being part of the network. In this paper, token-based umpiring technique (TBUT) is proposed, where every node needs a token to participate in the network and the neighboring nodes act as umpire. This proposed TBUT is found to be very efficient with a reduced detection time and less overhead. The security analysis and experimental results have shown that TBUT is feasible for enhancing the security and network performance of real applications.
- Selfish node
- Performance and token-based umpiring technique (TBUT)
Recent years have witnessed the increasing efforts toward making architecture standardization for the secured wireless mobile ad hoc networks. Actually, the framework of secured mobile ad hoc networks (MANETs) is an important part of the next-generation network design [1-4]. Such an improved security network brings a bright foreground for large data communications, i.e., the demand for the large data applications like IPTV, VoIP, and video conference has grown tremendously [5-8]. Meanwhile, research topic on secured communications has also received much attention in the past decade, and many works have been proposed to design robust and efficient schemes for delivering secured content delivery over error-prone networks [1,5,6,9-13].
In the self-umpiring system, all the nodes have dual roles - packet forwarding and umpiring. In the forward path during data forwarding, each node monitors the performance of its immediate next node. That way, mobile node A can tell correctly whether mobile node B is forwarding the packet sent by it, by promiscuously hearing mobile node B’s transmissions. Similarly during reply process RREP, mobile node C can verify whether mobile node B is unicasting the Route Reply (RREP) and whether the hop count given by mobile node B is correct. Thus, during forward path, mobile node A is the umpire for mobile node B and mobile node C is the umpire for mobile node B during reverse path operations. When a node is found to be misbehaving, say dropping data packets, the corresponding umpire immediately changes the status bit of guilty node to “1” indicating a red flag.
A selfish node is a node that utilizes its limited resources, such as battery power, CPU time, and memory space purely for its own purpose. Because of its energy and storage constraints, all incoming data forwarding and route discovery packets are intensely not accepted by it, thereby it tries to save its own resources. The features of the selfish nodes are not forwarding the routing packets, not replying to the hello messages, postponing the route discovery packets, and not forwarding the data packets. The main objective of the proposed work is too detected and eliminates the selfish node in MANET using TBUT. The proposed method consists of a packet dropping detection mechanism and a selfish node quarantining mechanism. In packet dropping detection mechanism, the selfish node is traced and identified. A selfish node quarantine mechanism envisages marking the offending nodes so that they do not participate any further in the network activities. In this paper, we will explain our proposed TBUT. The rest of the paper is organized as follows: Section 2 provides models and assumptions, Section 3 gives an overview of the proposed model, Section 4 gives simulations and experimental results, in Section 5 we explore the related work, and Section 6 draws up conclusions.
In this section, we formulate the MANET network and security model and also describe the selfish attacks.
2.1 Network model
A MANET where nodes are free to move about or remain stand still at their will is assumed.
Each node may join or leave the network at any time.
Nodes may fail at any time.
The source and the destination node are not selfish nodes.
Every node in the network have neighbor list.
There exists a bi-directional communication link between any pair of nodes, which is a requirement for most wireless MAC layer protocols including IEEE 802.11 for reliable transmission.
Wireless interfaces support promiscuous mode of operation. Most of the existing IEEE 802.11-based wireless cards support such promiscuous mode of operations, to improve routing protocol performance.
The promiscuous mode, operation may incur additional communication overhead and energy utilization in order to process the transit packets. We do not address the energy efficiency in this work.
2.2 Security model
MANETs are vulnerable to security attacks due to their features of shared radio channel, insecure open medium, dynamic changing topology, lack of cooperative algorithms and centralized monitoring, limited resource availability, and physical vulnerability. Attacks on MANET can be classified into two categories, namely active attacks and passive attacks. An active attack attempts to destroy or alter the data packets and routing messages being exchanged in the network and it is very harmful to the network security. Passive attack does not disrupt the operation of the network. Our work focuses on passive attack, but we do not address nodes that eavesdrop and record other node transmissions, and we address only the selfish nodes that refuse to fully participate in the network routing operations. Our security model is implemented on top of the popular ad hoc on-demand distance vector (AODV) routing protocol.
In the TBUT, each node is issued with a token at its inception. The token consists of three fields: NodeID, status, and reputation. NodeID is assumed to be unique and deemed to be beyond manipulation; status is a single-bit flag. Initially, the status bit is preset to zero indicating a green flag. Initially, reputation value is zero, i.e., positive. The token with a green flag and positive reputation is a permit issued to each node, which confers it the freedom to participate in all network activities. Each node in order to participate in any network activity, say Route Request RREQ, has to announce its token status bit and reputation value. If token status bit is “1” indicating a “red flag,” protocol does not allow the node to participate in any network activity. Similarly, if reputation value is “−1” indicating a “negative reputation,” the protocol does not allow the node to participate in any network activity. Our study does not depend on the exact mobile node structure of the networks. For the sake of the result explanation, it will be assumed that the network consists of approximately 100 mobile nodes span in the flat space (i.e., its span size is roughly 1 km2) and that there are roughly 20 mobile nodes per service area region .
In the TBUT, all the nodes have dual roles - packet forwarding and umpire quarantining. In the forward path during data forwarding, each node monitors the performance of its immediate next node. That way, node A can tell correctly whether B is forwarding the packet sent by it, by promiscuously hearing B’s transmissions. Similarly during the reply process RREP, C can verify whether B is unicasting the route reply RREP and whether the hop count given by B is correct . Thus, during forward path, A is the umpire for B and C is the umpire for B during reverse path operations. When a node is found to be selfishly misbehaving, say dropping packets, the corresponding umpire immediately sends a M-ERROR message to the source and the status bit of guilty node is set to “1” - red flag using M-Flag message and reputation value is set to −1. In order to correctly correlate the overheard messages, an additional field next_hop has been introduced in all routing messages as done in ETUS . Though there are several kinds of misbehaviors that could be captured by promiscuous hearing, we are focusing only on selfish actions - dropping packets and not transmitting packets.
Our aim in designing the security system is to limit the overhead to as minimum as possible while getting a good improvement in throughput. The active path is specified by node source, node 1, . . node N i − 1, node N i . . node N m , and the destination node. Thus, there are N m + 2 nodes in the active path U 1, U 2 . . U i - 1 , U i + 1, . . U m and U m + 1 are umpiring nodes. Umpire U i is situated in the communication zones of nodes N i , N i − 1, U i − 1, and U i + 1. For node N i , the two umpires will be U i and U i + 1. The third umpire will be N i−1 in the forward path and N i + 1 in their reverse path. Thus, when N i is found to be misbehaving, say dropping packets or not forwarding control packets, umpire nodes U i , U i + 1, and N i - 1 in the forward path and N i + 1 in the reverse path sends a M-ERROR message to the source then sets the status bit of guilty node N i to “1” indicating red flag by M-Flag message and reputation value is set to −1. There are some other connected issues, which are being discussed in later sections.
3.1 Implementation of TBUT
We implement generic selfish attacks in TBUT on top of traditional AODV protocol, but its principal is applicable to other routing protocols as well. We modify the famous AODV routing protocol and add a new field, next_hop, in the routing messages, so that a node can correlate the overheard packets correctly. It is based on two algorithms. Algorithm 1 describes route discovery procedure and algorithm 2 describes selfish node quarantine procedure. Each node in order to participate in any network activity, says RREQ, RREP, and data forwarding, has to announce its token status and reputation value. If the node status bit is “1” indicating a red flag and negative reputation value, the protocol does not allow the node to participate in any network activity.
3.1.1 Route discovery
Destination node D should appoint first umpire node. The destination node D forwards its list of neighbors to the previous node;
The previous node has its own list of neighbors. Now, a previous node finds intersection of destination node and its own list of neighbors.
From among the intersection nodes, it appoints one node as umpire;
The umpire so appointed sends its neighbor list to the previous node and its adjacent umpire node;
We find the intersection of neighbor list from the previous node and umpire node. The new intersection list of neighbors is sent to the next previous node;
This operation is continued till the intersection list of neighbors reaches the source.
3.1.2 Selfish quarantine
The selfish quarantine mechanism in TBUT, the routing, and packet forwarding operations of each wireless node is done in a fully decentralized and localized manner. Each node overhears the channel in the promiscuous listening mode. Moreover, neighboring nodes and umpiring nodes cooperate with each other to improve the monitoring accuracy. During route discovery mobile, node X announces a new routing table update toward destination D with hop count as 1, claiming that its next hop is mobile node Y. Mobile node M can readily detect this routing misbehavior, because based on the route announced by mobile node Y, it can predict the correct distance from mobile node X to D via mobile node Y to be 3. The same idea can be applied to examine other fields in the routing updates as well.
Similarly during packet forwarding, when it overhears one packet sent to its neighbor wireless node, say mobile node P, it checks the buffer of the route entries announced by mobile node P and determines the next hop node to which mobile node P should forward the packet. If it has not overheard the packet being forwarded by mobile node P to the correct next hop node after a certain time, it considers this packet as being dropped. If the number of packets dropped by mobile node P exceeds a threshold value, mobile node M considers this as a selfish node and sets the status flag to “1” then turns the reputation value as negative and quarantines the particular node.
Random way point
Two-ray ground reflection
Before the simulation, we randomly selected 30% of the network population as selfish behavior nodes. Each flow did not change its source and destination for the lifetime of a simulation run. We have kept the simulation time as 1000s, so as to enable us to compare our results with that of ETUS.
4.1 Packet delivery ratio
4.2 Failure to detect (false negative) probability
4.3 False accusation (false positive) probability
4.4 Communication overhead
Communication overhead for Self_USS, plain AODV, ETUS, and TBUT
Communication overhead for selfish node = 30%
4.5 Analysis of results
Throughput for Self_USS, plain AODV, ETUS, and TBUT
Throughput for selfish node = 30%
We present a comparison of communication overhead for Self_USS, ETUS, TBUT, and plain AODV in the presence of 30% selfish nodes in Table 2. It is found that with TBUT, there is a decrease in the communication overhead ranging from 26.05% (Self_USS, 0 m/s mobility) to 15.60% (Self_USS, 20 m/s mobility). However, TBUT communication overhead is much higher compared to Self_USS. For example, with a mobility of 20 m/s, ETUS communication overhead is 17.21% as compared to Self_USS. As compared with ETUS and TBUT, our proposed TBUTs have less communication overhead. We find that our proposed TBUTs yield a much higher output as compared to all other system.
The key distribution center (KDC) architecture is the main stream in wired network because KDC has so many merits: efficient key management, including key generation, storage, and distribution and updating. The lack of a trusted third party (TTP) key management scheme is a big problem in ad hoc network [1,5]. Different types of attacks on MANET were discussed by Abhay Kumar Rai et al. ; they have designed a security mechanism by which they can minimize or completely remove many of those attacks. Sudha Rani et al.  proposed a detection and prevention of wormhole attack in stateless multicasting. Their scheme has no central administrator. They have shown that their schemes can handle wormhole attacks.
Leonidas Georgiadia et al.  made a survey of threats and possible solutions for resource allocation and cross-layer control in wireless networks. Raj et al.  proposed a solution for black hole attacks. It was implemented in prominent AODV protocol-based MANET. Tsou  developed a novel scheme BDSR to avoid black hole attack based on proactive and reactive architecture. Yu et al.  proposed a solution of a distributed and cooperative black hole node detection and elimination mechanism. Solda et al.  gave a solution for blacklisting attacks; in these papers, they studied the problem of forecasting attack sources based on past attack logs from several contributors. They formulated this problem as an implicit recommendation system [25,26].
Hernandez et al.  introduced a fast model to evaluate the selfish node detection in MANET using a watchdog approach. They estimated the time of detection and the overhead of collaborative watchdog approach for detecting one selfish node. Singh et al.  implemented a security-based algorithmic approach in MANETs. In this analysis, an empirical and effective approach was proposed to optimize the packet loss frequency. Jyoshna et al.  proposed a solution for byzantine attacks in ad hoc networks using SMT protocol that provides a way to secure message transmission by dispersing the message among several paths with minimal redundancy. Megha Arya and Yogendra Kumar Jain  gave a solution for gray hole attack. They use an intrusion detection system (IDS) to monitor the network or system, for selfish activities or policy violation, and produce reports to a management station. It takes over the sending of packets. Afterwards, the node just drops the packets to launch a (DoS) denial of service attack. If neighbor nodes that try to send packets over attacking nodes lose the connection to destination, then they may want to discover a route again by broadcasting RREQ messages [31-34].
B.B. Jayasingh and B. Swathi  proposed a mechanism that detects the jellyfish attacks at a single node and that can be effectively deployed at all other nodes in the ad hoc network. They gave a solution that detects the jellyfish reorder attack based on the reorder density which is a basis for developing a metric.
Timothy et al.’s  paper focuses on jamming at the transport/network layer. Jamming at this layer exploits AODV and TCP protocols and is shown to be very effective in simulated and real networks when it can sense victim packet types, but the encryption is assumed to mask the entire header and contents of the packet so that only packet size, timing, and sequence is available to the attacker for sensing .
Kurkure and Chaudhari  illustrated a comparative analysis of the selfish node detection methods based on detection time and message overhead. In this paper, a collaborative watchdog method was used to identify the selfish nodes and diminish the detection time and message overhead. Sahu and Sinha  suggested a cooperative approach for understanding the behavior of IDS in MANETs. In this paper, they described about various attacks and techniques used for intrusion detection which were proposed to provide high performance. Patel et al.  used an AODV protocol for trust-based routing in ad hoc networks. Ad hoc networks have limited physical security, less infrastructure, restricted power supply, mobility network, and changing network topology [40-44]. Jawhar et al. suggested a reliable routing protocol for enhanced reliability and security of communication in the MANET and sensor networks .
Various P2P media streaming systems have been deployed successfully, and corresponding theoretical investigations have been performed on such systems . In this paper,  thoroughly investigates the evolutionary dynamics of soft security mechanism, namely reciprocity-based incentive mechanism, in P2P systems based on evolutionary game theory (EGT). By soft security mechanism, it means social control mechanisms to overcome peers’ selfish (rational) behaviors and encourage cooperation in P2P systems.
Trust management plays an important role in IoT [48-54] for reliable data fusion and mining, qualified services with context awareness, and enhanced user privacy and information security . It helps people overcome perceptions of uncertainty and risk and engages in user acceptance and consumption on IoT services and applications [9,55,56]. However, current literature still lacks a comprehensive study on trust management in IoT . Authenticated key agreement protocol is a useful cryptographic primitive, which can be used to protect the confidentiality, integrity, and authenticity for transmitted data over insecure networks .
Built upon opportunistic routing and random linear network coding, CodePipe not only simplifies transmission coordination between nodes but also improves the multicast throughput significantly by exploiting both intra-batch and inter-batch coding opportunities . In particular, four key techniques, namely LP-based opportunistic routing structure, opportunistic feeding, fast batch moving, and inter-batch coding, are proposed to offer substantial improvement in throughput, energy efficiency, and fairness .
In the paper,  proposes a multi-constrained QoS multicast routing  method using the genetic algorithm. The proposal will be flooding limited  using the available resources and minimum computation time in a dynamic environment. By selecting the appropriate values for parameters such as crossover, mutation, and population size, the genetic algorithm improves and tries to optimize the routes.
For the author of this paper , they consider the assignment strategy with topology preservation by organizing the mesh nodes with available channels and aim at minimizing the co-channel interference in the network. The channel assignment with the topology preservation is proved to be NP-hard and to find the optimized solution in polynomial time is impossible. They have formulated a channel assignment algorithm named as DPSO-CA which is based on the discrete particle swarm optimization and can be used to find the approximate optimized solution [57,58].
All the above schemes only try to protect the system from the attacker, but not bother about quarantining attackers [3,16,59]. The TBUT systems not only detect the mischievous nodes but also prevent their further participation in the network.
The security considerations in a TBUT setting are still in their infancy phase and require a more thorough analysis by the research community. The misbehavior of selfish nodes is a major problem in wireless MANET. The selfish nodes do not participate in the routing and data transmission process, which intentionally drop the packets. These misbehaviors of the selfish nodes will impact availability, efficiency, reliability, and fairness. The selfish node utilizes the resources for its own purpose, and it neglects to share the resources to other nodes. So, it is important to detect the selfish nodes in MANET. We have conducted simulation studies to evaluate the performance of TBUT in the presence of 30% selfish nodes and have compared it with ETUS routing protocols. The results show that TBUT significantly improves the performance of ETUS in all metrics, packet delivery ratio, and control overhead. The security analysis and experimental results have shown that TBUT is feasible for enhancing the security and network performance of real applications. In the following, a number of potential research directions are introduced.
6.1 A. Cross-layer security schemes
Different layers in the MANETs need the authentication for their different functionalities. It is then possible to integrate the authentication from higher layers into the MAC-PHY layer. This approach will save the cost of communication and provide a unifying framework to address the authentication of sensing nodes as well as the sensing data, among other possibilities.
6.2 B. Reliable spectrum schemes
Perhaps, solutions to combat attacks against TBUT schemes have been studied more than any other security issues. Still a thorough analysis to compare and contrast existing techniques, such as trust weight fusion versus consensus-based algorithms, can provides further insights into the pros and cons of each scheme and might lead future researchers toward developing more robust solutions.
6.3 C. Incentive-based security schemes
We classified the agents posing security threats in a TBUT into three categories, namely adversaries, malicious nodes, and silent nodes. Each group will follow a different attack strategy. It is interesting to address the incentives for misbehaviors and attack against a TBUT so as to adopt incentive minimization schemes. As an example, a silent node is seeking to further enhance its own performance at the expense of other network nodes. Thus, fair resource allocation strategies will ensure that no single node can sustain superior performance in the network, which in turn eliminates the opportunity of misbehavior based on fraudulent reports. Our future work will focus on improving the TBUT performance, by minimizing the innocent node booking. Last but not least, several interesting open problems are pointed out with possible addressing ideas to trigger more research efforts in this emerging area.
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