A cryptographic paradigm to detect and mitigate blackhole attack in VANET environments

Reliable communication in ad hoc networks necessitates mobile nodes to synchronize among themselves for cooperation. The cooperation in ad hoc networks is enforced through the implementation of encryption algorithms and intrusion detection systems. Most of the intrusion detection and encryption algorithms are not effective enough to address the extent to which a participative mobile node may trust other interacting mobile nodes for data forwarding. The existing mitigation frameworks consider packet forwarding capability as the single parameter for calculating trust values which cannot be appropriate for computing the accurate trust factor as reliable nodes are highly impacted by data rate, throughput, delay and energy consumption. An Integrated Context-based Mitigation Framework (ICMF) is proposed for facilitating effective mitigation of rendezvous point misbehavior and for enhancing the performance of the network. This proposed ICMF framework facilitates rendezvous point misbehavior detection by determining Grey theory Inspired Discriminating Factor (GTIDF) that is contextually derived from multiple factors including data rate, throughput, delay and energy consumptions using the benefits of the Grey theory. From the simulation results of the proposed ICMF framework, improved root node attackers survivability rate and root node attackers categorization rate are inferred when compared to the benchmarked baseline mitigation frameworks.

Integrated Context-Based Mitigation Framework for Enforcing Security against Rendezvous Point Attack in MANETs

When it comes to selecting an optimal defense strategy for an intrusion detection system of a wireless sensor network, such challenges as those brought about by the diversification of the attack methods and the expanded database of the attack patterns have to be dealt with. To overcome those challenges, this paper combines realistic bounded rationality with the incomplete information of the attack-defense players by employing evolutionary game theory as a tool. Firstly, an attack-defense evolutionary game model considering three types of population, in which attackers are subdivided by the source of the threat into external attackers and selfish nodes, is proposed. The sets of player types and the game strategies in our model can be extended from $2\times2$ to $\text{n}\times $ m $\times $ l. The sensitivity of the evolutionary population to similar strategies, which reflects the efficiency change in the multi-agent learning process, is depicted by a replicator dynamic equation especially optimized for this purpose by the introduction of an enhanced cooperation mechanism. In essence, an optimal defense strategy selection algorithm is provided by calculating the evolutionary stable equilibrium and a description of the evolutionary trajectory of the players over time is obtained. Moderate security and proactive defense in the form of support decisions have been provided by our method for wireless sensor networks. Experimental results have verified the validity of our method. Moreover, the optimized algorithm has solved the problem that an excessively large database of attack patterns affects the speed of switching to the optimal defense decision strategy and the learning efficiency of the evolutionary game replicator dynamic mechanism is not fast enough.

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An Optimal Decision Method for Intrusion Detection System in Wireless Sensor Networks With Enhanced Cooperation Mechanism

A vehicle ad‐hoc network (VANET) is an essential component of the intelligent transportation system. In VANET, vehicle‐to‐vehicle communication plays a significant role in providing a secure network and safe exchange of information between nodes. However, VANET is prone to critical risks, threats, and attacks due to its unique characteristics and dynamic topology. Trust is one of the most critical aspects of security. So, this concept has already proved its accuracy and efficiency in various wireless applications. To ensure trust in vehicular communications, we propose a decentralized prediction and reputation approach that includes two models. First, we introduce a reputation model based on the Bayesian theorem and watchdog concept. We investigate the vehicle's behavior in the network to measure the vehicle's trust rate. Then, we present a prediction model to anticipate the vehicle's state and detect malicious vehicles. Based on this approach, vehicles can avoid interacting with unreliable vehicles and ignore incoming messages from them to reduce the effect of malicious nodes in the network. Our extensive simulations show the performance and accuracy of our approach. Also, the certainty rate remains between 91% and 99% regardless of the simulation scenario's duration. This simulation also demonstrates our classification's certainty, even under different network situations.

Decentralized prediction and reputation approach in vehicular networks

A selfish node trust aware with Optimized Clustering for reliable routing protocol in Manet

Mobile Ad hoc Network is a collection of autonomous mobile nodes connected with each other without any central coordinator. There is several security issues arise in MANET due to dynamically changing network topology. The reliable communication is achieved only when there exists cooperation among the mobile nodes in MANET. Due to the lack of resources some mobile nodes refuse to forward the packet to other nodes. This type of nodes is called the selfish nodes. These kind of selfish nodes in the route degrade the overall performance of the whole network. In this paper we discussed about the various selfish node detection methods and propose a selfish node detection algorithm (SNDA) for identifying and isolation of selfish node from the network.

An Efficient Approach to Identify Selfish Node in MANET

Cloud based computing is a skill which provide communications for large scale storage of data, along with its refine and assigning. The security and retreat become main concern for uploading sensitive data without any information leak. This can be achieved by the cloud servers by integrating the encryption techniques into it, which becomes a major objection to guide the efficient keyword based questions and the encrypted data of the matching results. The searching can be an interrogation technique which is either based on a single keyword or multi-keyword as input. In this paper, a fresh algorithm known as Multi-Keyword Interrogation Scheme (MKIS) algorithm is recommended. It lessens the maintenance overhead to a greater scope when the keyword dictionary expansion occurs dynamically. The experimental results show that MKIS presents excellent performance when compared with the existing schemes. The proposed technique is presented in 2 aspects: many-keyword graded the look to attain exact look for output and meaning-based explore to maintain synonym questions. Search Terms— Cloud based computing, Multi-keyword interrogation, Data encryption, Security preserving, semantic grilling.

M. Mohamed Musthafa

Papers

An Efficient Approach to Identify Selfish Node in MANET

Efficient Semantic Interrogation Scheme over Cryptographic Data in Cloud