Majeed Alajeely

Packet Faking Attack: A Novel Attack and Detection Mechanism in OppNets

Security is a major challenge in Opportunistic Networks (OppNets) due to its characteristics of being an open medium with dynamic topology, there is neither a centralized management nor clear lines of defence. A packet dropping attack is one of the major security threats in OppNets as neither source nodes nor destination nodes have any knowledge of when or where a packet will be dropped. In this paper, we present a novel attack and detection mechanism against a special type of packet dropping where the malicious node drops one packet or more and injects a new fake packet instead. Our novel detection mechanism is very powerful and has very high accuracy. It relies on a very simple yet powerful idea, the creation time of each packet. Significant results show this robust mechanism achieves a very high accuracy and detection rate.

Defense against packet dropping attacks in opportunistic networks

Opportunistic networks (OppNets) are an interesting topic that are seen to have a promising future. Many protocols have been developed to accommodate the features of OppNets such as frequent partitions, long delays, and no end-to-end path between the source and destination nodes. Embedding security into these protocols is challenging and has taken a lot of attention in research. One of the attacks that OppNets are exposed to is the packet dropping attack, where the malicious node attempts to drop some packets and forwards an incomplete number of packets which results in the distortion of the message. To increase the security levels in OppNets, this paper presents an algorithm developed to detect packet dropping attacks, and finds the malicious node that attempted the attack. The algorithm detects the attack by using an indicative field in the header section of each packet; the indicative field has 3 sub fields - the identification field, the flag field, and the offset field. These 3 fields are used to find if a node receives the complete original number of packets from the previous node. The algorithm will have the advantage of detecting packets dropped by each intermediate node, this helps solve the difficulties of finding malicious nodes by the destination node only.

Routing Protocols in Opportunistic Networks – A Survey

ABSTRACT Opportunistic networks (OppNets) refer to a number of wireless nodes opportunistically communicating with each other in a form of “Store–Carry–Forward”. This occurs when they come into contact with each other without a proper network infrastructure. OppNets are designed to operate in an environment characterized by high delay, high error rate, intermittent connectivity, and non-availability of end-to-end route between the source and destination. OppNets use wireless technologies, such as IEEE 802.11, WiMAX, Bluetooth, and other short-range radio communication, and grow from a single node (seed) to become large networks by inviting new nodes (helpers) to join the network. Nodes have the ability to store and carry data and also forward it to other nodes in order to achieve different tasks. In OppNets, there is no end-to-end connection between the source and the destination nodes. Further, due to their inherent features, OppNets suffer from frequent partitions and long delays, while also being subject to serious security challenges. This survey includes an overview of the available OppNets routing protocols, their classification, and an evaluation of six routing protocols (Epidemic routing, PRoPHET, MaxProp, Spray and Wait, Direct Delivery, and First Contact) in terms of complexity/robustness and scalability. Detailed simulation results show that as the load on the network increases, the performance of protocols decrease in terms of delivery delay and network overhead. As for scalability, simulation results show that Epidemic routing and PRoPHET achieved high delivery rates, but with a very high network overhead. MaxProp and Spray and Wait achieved lower delivery rates, but with a low network overhead. First Contact and Direct Delivery achieved low delivery rates with high delivery delays. Results vary depending on the buffer size, contact times, and speed. The results indicate that trams have the capacity to carry and exchange information faster, and improve connectivity in OppNets.

Defense against packet collusion attacks in opportunistic networks

Security is a major challenge in Opportunistic Networks (OppNets) because of its characteristics such as an open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nor destination nodes have control over the behaviour of intermediate nodes in the network. Consequently, the knowledge of where or when packets are/will be dropped is difficult to gather. In this paper, we present a novel attack and traceback mechanism against a special type of packet dropping attacks packet collusion attacks, where the malicious node(s) drops some or all packets and then injects new fake packets in their place to mask the packet dropping. Our novel detection and traceback mechanism is based on the concept of a Merkle (or hash) tree and simulation results show it to be highly effective and accurate in terms of detecting attack instances and tracing back to the malicious node(s) in the network that is the attack source.

Security and Trust in Opportunistic Networks – A Survey

ABSTRACT Opportunistic networks or OppNets refer to a number of wireless nodes opportunistically communicating with each other in a form of “Store–Carry–Forward”. This occurs when they come into contact with each other without proper network infrastructure. OppNets use wireless technologies, such as IEEE 802.11, WiMAX, Bluetooth, and other short-range radio communication. In OppNets, there is no end-to-end connection between the source and the destination nodes, and the nodes usually have high mobility, low density, limited power, short radio range, and often subject to different kinds of attacks by malicious nodes. Due to these characteristics and features, OppNets are subject to serious security challenges. OppNets strongly depend on human interaction; therefore, the success of securing such networks is based on trust between people. This survey includes the security approaches in OppNets and techniques used to increase their security levels.

Packet integrity defense mechanism in OppNets

In an Opportunistic network, as data gets transferred from node to node, with the existence of malicious nodes in the network, it is possible that the data gets modified. To make sure that data remains in its original format, we propose a technique that allows nodes to authenticate packets as they receive them by constructing hash trees, also referred to as Merkle trees. Merkle trees are used to check and authenticate all the packets. As a result of this, direct trust is formed. Direct trust is updated based on the authenticity of the packets and the encounter rate with the node. As nodes come into contact with each other during the packet transmission period, they share feedback on how much they trust other nodes. This feedback, in addition to the formed direct trust with a node is used to derive a reputation value. The reputation value allows nodes to make the correct packet transmission decisions when meeting with nodes. Using an OppNet simulator that embeds OppNet protocols, we have tested the proposed reputation-based system. The results show the effectiveness of the reputation system as malicious nodes and modified packets are detected. As a result, the performance of the network improves, and with time, the packet modification rates decrease as malicious nodes are caught.

Reputation based malicious node detection in OppNets

Routing and security management in Opportunistic Networks is challenging, where effective and secure forwarding of data delivery without any loss is not easy to guarantee. Packet dropping attacks are one of the most popular attacks that OppNets are exposed to. This paper presents an efficient malicious path, malicious node, and a packet dropping detection technique against selective packet dropping attacks. In our technique, we have developed a node by node packet dropping detection mechanism using the Merkle tree hashing technique. The result of detection is used to build trust and reputation for nodes, which are then used to detect malicious nodes. Simulation results show that the technique efficiently detects malicious paths, malicious nodes, and dropped packets. The results also show that with the increase of simulation time, node detection accuracy also increases as nodes have more time to establish reputation among nodes in the network. Results also show that the packet dropping rate drops over time, thus improving routing in OppNets.

Establishing trust relationships in OppNets using Merkle trees

Opportunistic Networks (OppNets) are exposed to a variety of attacks, among them are packet dropping attacks. The security challenges in OppNets is to effectively and securely forward data and guarantee their delivery without any loss. Security and trust in OppNets have gained popularity in research because of their inherent features, including frequent partitions, long delays and intermittent connectivity. This paper presents an efficient malicious path and malicious node detection technique against selective packet dropping attacks. In our algorithm we have developed a solid detection mechanism using the Merkle tree hashing technique. The result of malicious path detection is used to build trust by destination nodes for each path, the built trust value of nodes is then used to detect malicious nodes. Simulation results show that the technique accurately detects malicious paths. The results also show that with the increase of simulation time, node detection accuracy also increases as intermediate nodes have more time to establish trust with destination nodes.

Malicious node detection in OppNets using hash chain technique

Opportunistic Networks aim to set a reliable networks where the nodes has no end-to-end connection and the communication links often suffer from frequent disruption and long delays. The design of the OppNets routing protocols is facing a serious challenges such as the protection of the data confidentiality and integrity. OppNets exploit the characteristics of the human social, such as similarities, daily routines, mobility patterns and interests to perform the message routing and data sharing. Packet dropping attack is one of the hardest attacks in Opportunistic Networks as both the source nodes and the destination nodes have no knowledge of where or when the packet will be dropped. In this paper, we present a new malicious nodes detection technique against packet faking attack where the malicious node drops one or more packets and instead of them injects new fake packets. We have called this novel attack in our previous works a packet faking attack. Each node in Opportunistic Networks can detect and then traceback the malicious nodes based on a solid and powerful idea that is, hash chain techniques. In our hash chain based defense techniques we have two phases. The first phases is to detect the attack, and the second phases is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. In our simulation, we have achieved a very high node detection accuracy and low false negative rate.

Malicious Node Traceback in Opportunistic Networks Using Merkle Trees

Security is a major challenge in Opportunistic Networks because of its characteristics, such as open medium, dynamic topology, no centralized management and absent clear lines of defense. A packet dropping attack is one of the major security threats in OppNets since neither source nodes nor destination nodes have the knowledge of where or when the packet will be dropped. In this paper, we present a malicious nodes detection mechanism against a special type of packet dropping attack where the malicious node drops one or more packets and then injects new fake packets instead. Our novel detection and traceback mechanism is very powerful and has very high accuracy. Each node can detect and then traceback the malicious nodes based on a solid and powerful idea that is, Merkle tree hashing technique. In our defense techniques we have two stages. The first stage is to detect the attack, and the second stage is to find the malicious nodes. We have compared our approach with the acknowledgement based mechanisms and the networks coding based mechanism which are well known approaches in the literature. Simulation results show this robust mechanism achieves a very high accuracy and detection rate.