Biblio
Mobile Ad-hoc Networks (MANETs) have attracted lots of concerns with its widespread use. In MANETs, wireless nodes usually self-organize into groups to complete collaborative tasks and communicate with one another via public channels which are vulnerable to attacks. Group key management is generally employed to guarantee secure group communication in MANETs. However, most existing group key management schemes for MANETs still suffer from some issues, e.g., receiver restriction, relying on a trusted dealer and heavy certificates overheads. To address these issues, we propose a group key management scheme for MANETs based on an identity-based authenticated dynamic contributory broadcast encryption (IBADConBE) protocol which builds on an earlier work. Our scheme abandons the certificate management and does not need a trusted dealer to distribute a secret key to each node. A set of wireless nodes are allowed to negotiate the secret keys in one round while forming a group. Besides, our scheme is receiver-unrestricted which means any sender can flexibly opt for any favorable nodes of a group as the receivers. Further, our scheme satisfies the authentication, confidentiality of messages, known-security, forward security and backward security concurrently. Performance evaluation shows our scheme is efficient.
The extensive increase in the number of IoT devices and the massive data generated and sent to the cloud hinder the cloud abilities to handle it. Further, some IoT devices are latency-sensitive. Such sensitivity makes it harder for far clouds to handle the IoT needs in a timely manner. A new technology named "Fog computing" has emerged as a solution to such problems. Fog computing relies on close by computational devices to handle the conventional cloud load. However, Fog computing introduced additional problems related to the trustworthiness and safety of such devices. Unfortunately, the suggested architectures did not consider such problem. In this paper we present a novel self-configuring fog architecture to support IoT networks with security and trust in mind. We realize the concept of Moving-target defense by mobilizing the applications inside the fog using live migrations. Performance evaluations using a benchmark for mobilized applications showed that the added overhead of live migrations is very small making it deployable in real scenarios. Finally, we presented a mathematical model to estimate the survival probabilities of both static and mobile applications within the fog. Moreover, this work can be extended to other systems such as mobile ad-hoc networks (MANETS) or in vehicular cloud computing (VCC).
Wireless sensor network operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Node misbehavior due to selfish or malicious reasons or faulty nodes can significantly degrade the performance of mobile ad-hoc networks. To cope with misbehavior in such self-organized networks, nodes need to be able to automatically adapt their strategy to changing levels of cooperation. The problem of identifying and isolating misbehaving nodes that refuses to forward packets in multi-hop ad hoc networks. a comprehensive system called Audit-based Misbehavior Detection (AMD) that effectively and efficiently isolates both continuous and selective packet droppers. The AMD system integrates reputation management, trustworthy route discovery, and identification of misbehaving nodes based on behavioral audits. AMD evaluates node behavior on a per-packet basis, without employing energy-expensive overhearing techniques or intensive acknowledgment schemes. AMD can detect selective dropping attacks even if end-to-end traffic is encrypted and can be applied to multi-channel networks.
Mobile ad-hoc networks (MANETs) are decentralized and self-organizing communication systems. They have become pervasive in the current technological framework. MANETs have become a vital solution to the services that need flexible establishments, dynamic and wireless connections such as military operations, healthcare systems, vehicular networks, mobile conferences, etc. Hence it is more important to estimate the trustworthiness of moving devices. In this research, we have proposed a model to improve a trusted routing in mobile ad-hoc networks by identifying malicious nodes. The proposed system uses Reinforcement Learning (RL) agent that learns to detect malicious nodes. The work focuses on a MANET with Ad-hoc On-demand Distance Vector (AODV) Protocol. Most of the systems were developed with the assumption of a small network with limited number of neighbours. But with the introduction of reinforcement learning concepts this work tries to minimize those limitations. The main objective of the research is to introduce a new model which has the capability to detect malicious nodes that decrease the performance of a MANET significantly. The malicious behaviour is simulated with black holes that move randomly across the network. After identifying the technology stack and concepts of RL, system design was designed and the implementation was carried out. Then tests were performed and defects and further improvements were identified. The research deliverables concluded that the proposed model arranges for highly accurate and reliable trust improvement by detecting malicious nodes in a dynamic MANET environment.
Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.
Mobile Ad-Hoc Networks (MANET) consist of peer-to-peer infrastructure less communicating nodes that are highly dynamic. As a result, routing data becomes more challenging. Ultimately routing protocols for such networks face the challenges of random topology change, nature of the link (symmetric or asymmetric) and power requirement during data transmission. Under such circumstances both, proactive as well as reactive routing are usually inefficient. We consider, zone routing protocol (ZRP) that adds the qualities of the proactive (IARP) and reactive (IERP) protocols. In ZRP, an updated topological map of zone centered on each node, is maintained. Immediate routes are available inside each zone. In order to communicate outside a zone, a route discovery mechanism is employed. The local routing information of the zones helps in this route discovery procedure. In MANET security is always an issue. It is possible that a node can turn malicious and hamper the normal flow of packets in the MANET. In order to overcome such issue we have used a clustering technique to separate the nodes having intrusive behavior from normal behavior. We call this technique as effective k-means clustering which has been motivated from k-means. We propose to implement Intrusion Detection System on each node of the MANET which is using ZRP for packet flow. Then we will use effective k-means to separate the malicious nodes from the network. Thus, our Ad-Hoc network will be free from any malicious activity and normal flow of packets will be possible.
Mobile ad-hoc networks are a new field in networking because it works as an autonomous network. Application of mobile ad-hoc networks are increasing day by day in recent year now a days. So it important is increasing to provide suitable routing protocol and security from attacker. Mobile ad-hoc network now a days faces many problems such as small bandwidth, energy, security, limited computational and high mobility. The main problem in mobile ad-hoc networks is that wireless networks, Infrastructure wireless networks have larger bandwidth, larger memory, power backup and different routing protocol easily applies. But in case of mobile ad-hoc networks some of these application failed due to mobility and small power backup so it is required such type of routing protocol which is take small energy during the transfer of packet. So we see that still there are many challenging works in mobile ad-hoc networks remained and to research in this area related to routing protocol, security issues, solving energy problem and many more which is feasible to it. Our research most probably will be dedicated to Authentication in mobile ad-hoc network.