2013 IEEE 9th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)
Trust-Based Security Protocol Against Blackhole Attacks in Opportunistic Networks Sahil Gupta Division of Computer Engineering Netaji Subhas Institute of Technology University of Delhi, Delhi, India E-mail:
[email protected]
Isaac Woungang Department of Computer Science Ryerson University Toronto, Ontario, Canada E-mail:
[email protected] Arun Kumar CAITFS, Division of Information Technology Netaji Subhas Institute of Technology University of Delhi, Delhi, India E-mail:
[email protected]
Sanjay Kumar Dhurandher CAITFS, Division of Information Technology Netaji Subhas Institute of Technology University of Delhi, Delhi, India E-mail:
[email protected]
Mohammed S. Obaidat, Fellow of IEEE and Fellow of SCS Department of Computer Science and Software Engineering Monmouth University, W. L. Branch, NJ 07764, USA E-mail:
[email protected] Abstract—Opportunistic networks (Oppnets) are a kind of wireless networks that provide the opportunity to have social interaction and obtain data that can be used for message passing decision. The increase observed in the number of people with PDAs and other handset devices equipped with wireless technologies makes the forwarding paradigm and Oppnets scenarios more interesting and challenging. The main challenge in Oppnets is to take efficient routing decisions on securing the delivery of messages to the destination. Cooperation and trust between nodes in the network saves them from malicious attacks. The trust of a node is a basic value that symbolizes the magnitude of its social responsibility in the network, which include helping groups of nodes in message delivery, saving these nodes from malicious attacks, just to name a few. This paper focuses on blackhole attack against the PRoPHET routing protocol for Oppnets. A Trust-based Security Protocol (TSP) is proposed to secure Oppnets against blackhole attacks. Simulation results are provided to support the effectiveness of our proposed TSP approach, in the sense that considerable control is observed in the number of dropped packets, number of messages captured bythe malicious nodes (socalled malicious count) and overhead ratio. Keywords—Opportunistic networks, blackhole attack, trust, security, PRoPHET routing, opportunistic routing.
I.
INTRODUCTION
Oppnets is going to be the future technology of new generation people with limitless applications and scopes. Basically, in times of war where network becomes sparse or in remote areas of developing countries where there is limited access to internet, the oppnet routing protocols
978-1-4799-0428-0/13/$31.00 ©2013 IEEE
724
promise a better message delivery. Oppnets is mainly characterized by store, carry and forward paradigm [1]. With the recent growth in wireless devices, there is a huge opportunity of message delivery where every node can become a participant. But routing in oppnets is challenging since it is not known in advance as to when a node will get the opportunity to deliver message to its right next candidate node. Network topology is also unknown to every node in the network and it changes dynamically. Even if an appropriate routing methodology is chosen, it is hard to know whether a candidate node behaves appropriately or maliciously in the system. Thus, node cooperation is required in a systematic manner. This requires some techniques that let the routing node know the exact behavior of other nodes through exchange of certain social parameters. This helps in identifying the malicious behavior of nodes in the network. A malicious behavior leads to a considerable delay in the message delivery or no delivery at all in the network under consideration. When the node routes a message to its best next hop, it requires building a certain value that gives creditability to the routing node so that the other nodes can trust that node for the delivery of a message. The creditability is the trust which ensures the magnitude of cooperation in the delivery of message in the network. This paper secures the network from blackhole attack through the proposed TSP method. A blackhole attack is a simple form of byzantine attack, where the adversary node advertises itself as honest, and attempts to provide forged information to attract and intercept the packets, preventing them from reaching their destinations.
time t). Each node (A or B) can then use its tickets to reveal its contact history to another future encountered node. A cost is associated for this operation so that the cost incurred by an attacker who launches blackhole attacks will increase compared to the cost incurred by a wellbehaved node. In [7], Al Hinai et al. proposed a trust-based secured routing framework (called TB-SnW) for mitigating blackhole threat in delay tolerant networks. Their scheme uses a distributed trust management method that allows each node to maintain a trust list of all its encounters. Based on these trust levels, node authentication is facilitated, including the detection of blackhole nodes. In [10], Na Li et al. proposed a reputation-based system to secure the network that uses a Positive Feedback Message (PFM) to calculate the first hand (FHI) and second hand (SHI) information. Further FHI, SHI and an ageing concept is used to derive the reputation engine. The trust calculation in this is based only on the number of bundles forwarded by the nodes. In [11], Frey at al. proposed a user centric and social aware approach to secure the network that uses Reputation Ticket (RT) for self-check and community check, which further help in building a trusted environment. The trust calculation is determined using only the number of bundles forwarded and a beta distribution density function.
The rest of the paper is organized as follows. Section II describes some related works. In Section III, the proposed Trust-based Security Protocol (TSP)is described. In Section IV, the simulation results are presented. Finally, Section V concludes our work. II.
RELATED WORK
There are many types of attacks against routing in ad hoc networks that Oppnet scenarios can inherit [1]. These include routing attacks, sleep deprivation, blackhole attacks, eavesdropping, traffic analysis, denial of service, sybil attacks, to name a few. In this section, we describe representative trust-based secured routing schemes for Oppnets [2, 3, 4, 5, 6, 7, 11, 12] that deal with various forms of blackhole attacks. In [2], Tamez et al. proposed a basic framework called COmposite trust and Trust management in Opportunistic Networks (COTTON) for trust management in Oppnets. The COTTON model uses a classification of nodes into four helper categories (i.e. private unknown helpers, public unknown helpers, trusted known helpers, and Oppnet reservists) to assign trust to nodes based on the behavior of the group in which it belongs and recommendations from all nodes in that group. Whenever a new node joins a group and a matching agent is deployed on that node, its behavior is constantly monitored and its position is updated. In [3], Goncalves et al. proposed a trust management system for Oppnets based on a biological-model based ontology. When a node, say A has to trust another node, say B, the direct as well as indirect reputations of B is computed. A composite reputation is then calculated from these reputation values. Based on this result, nodes are qualified as being "very untrustworthy", "untrustworthy", "trustworthy", or "no opinion". In [4], Mtibaa and Harras studied a set of social-based trust filters to identify a subset of contacts between nodes that should be allowed to take part of the forwarding path. Explicit real human mobility traces and social information are used to establish a trustworthy communication (in the form of a relay-to-relay based trust or a source-to-relay based trust) between nodes. In [5], Trifunovic et al. proposed two complementary approaches for social trust establishment, i.e. explicit social trust and implicit social trust. The implicit social trust is meant to approve whether the node is honest or dishonest; whereas the explicit social trust method is designed to determine how much connected the nodes are with respect to handling the message passing via them. In [6], Li et al. introduced the notion of encounter tickets for the purpose of routing and packet forwarding. In their secured routing scheme for mitigating blackhole attacks in delay tolerant networks, two nodes (node A and node B) that meet are requested to generate a cryptography-based encounter ticket each (known as a piece of evidence certifying that these nodes have been in contact at a certain
III.
PROPOSED TRUST-BASED SECURITY PROTOCOL
In contrast to the works discussed above, in the proposed TSP protocol, the trust is not only based on the number of successfully transferred messages, but also on three fundamental pillars: SGV, Creadits and Hop count. The proposed TSP scheme relies on the calculation of a social group value and a trust distribution technique that has been described as follows. A. Social Group Value and Trust Distribution Nodes in the network are divided into various groups. An individual group is assigned with a priority number called the social group value (SGV).We have assumed that a particular node belongs to an individual that is a part of static society. Static society means people that belong to a group of businessman, high class politicians, military people etc. An exact group classification is not done in this work. Each group with different Social Group Value (SGV) indicates the social importance of groups relative to each other. It is a concern for an oppnet designer to give justified values of SGV according to the importance of the group. The community group considered here is different from the communities that have been made by Clique or other Community detection algorithms. However, these algorithms can be integrated in this work to generate suitable SGV values. In the current work these values are static and arbitrarily chosen at appropriate intervals. Trust is
725
calculated by the destination node and distributed to each node according to its hop number in the message. The idea of trust distribution in our proposed protocol has been taken from the ancient Indian ruling system in which whenever a good message from other kingdom comes through various messengers, then the king (i.e. destination) will distribute some dinar (gold coins) to the main rajdoot (direct messenger to the king) and he/she will further distribute them among his/her other mates who participated in that process. In our protocol, trust is considered as a function of social group value because it describes and includes the importance of the participation of a node in the message passing procedure. As such, a message maintains a vector consisting of hops through which it passes. The destination does the necessary calculation of the trust value for each hop in the message vector. Trust is distributed among other pears that have participated in the delivery process. The destination node of the message uses a backward path to achieve this distribution. Through this security feature, a malicious node can be quickly identified since its trust value will never increase due to the fact this node does not participate in the routing operation.
To understand how the trust of a node is calculated, let’s consider the example depicted in Fig. 1, where it is assumed that the message is to be transferred from the source node to the destination node via intermediate nodes N1, N2 and N3. Source
N1
N2
N3
Destination
Figure 1: Path between source and destination.
The trust values of these intermediate nodes are obtained as: Trust (N3) for Src =
ሺோଵሻൈఊൈௗ௧௦
Trust (N2) for Src =
(1)
ଵ ሺோଵሻൈఊൈௗ௧௦
(2)
ଶ ሺோଵሻൈఊൈௗ௧௦
Trust (N1) for Src = (3) ଷ where Src denotes the source node, R1is the social group value, ߛ
