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Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET

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Mobile ad hoc networks (MANETs) are qualified by multi-hop wireless links and resource restrained nodes. Generally, mobile ad hoc networks (MANETs) are susceptible to various attacks like gray hole attack,… Click to show full abstract

Mobile ad hoc networks (MANETs) are qualified by multi-hop wireless links and resource restrained nodes. Generally, mobile ad hoc networks (MANETs) are susceptible to various attacks like gray hole attack, black hole attack, selective packet dropping attack, Sybil attack, and flooding attack. Therefore, the wireless network should be protected using encryption, firewalls, detection schemes to identify the attackers and decreasing their impact on the network. So, it’s an essential task to design the intelligent intrusion detection system. This research work deals with designing the multilevel trust based intelligence intrusion detection system with cryptography schemes for detecting the attackers. In order to identify the attackers, we propose a novel trust management with elliptic curve cryptography (ECC) algorithm. At first, a trust manager is maintained, its functions is to classify the trust into three different sets of trust level based upon the elliptic curve cryptography and Schnorr’s signature in the MANET. Each trust level has identified a single attacker. Thus, the proposed method has detected three types of attackers such as black hole attack, flooding attack and selective packet dropping attack. Furthermore, it have provided countermeasure for these attackers in the MANET as well as improved performances. Hence, it obtains higher throughput, minimum delay, minimum packet loss and efficient end to end delivery in MANET. Thus, the proposed scheme is a secure and optimal solution to encounter attackers, which represents to be efficient and significant.

Keywords: attack; detection system; detection; trust; cryptography; intrusion detection

Journal Title: Cluster Computing
Year Published: 2017

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