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Securing information systems and the networks that connect them is paramount in the age of growing incidents of cyber attacks. Invasive and continuous cyber attacks on information systems continue to create a potentially shocking blow to the transmission of information from one end to the other. Wireless networks are often vulnerable to intentional interference physical layer reactive jamming attacks, in which the adversaries effectively and stealthily corrupt the packet by injecting high level of noise thereby keeping the channel busy. Consequently, legitimate traffic gets completely blocked; resulting in packet loss at the receiver side. These jammers cause serious threat to wireless communication and they are difficult to identify. A novel method known as 'Neuro Fuzzy Detachment Scheme' has been proposed to prevent such sophisticated and powerful jamming attack.The key idea is to identify and detach jammers from the network to ensure perspicuous transmission. The proposed detachment scheme operates in three modules. In module one, the past record log files are analyzed to extract the trust values for different traces. Using the extracted trust values, the decision factors are monitored for different runs with the attacker in between the communication range of source and the sink and then the suspicious traces are identified and classified using ANFIS classifier. In module two, to trace abnormality, three reorganizing algorithms have been developed to reorganize the categorized traces. In module three, misbehaving nodes are identified and detached from the network. It is observed through simulation studies that the proposed scheme attains higher throughput and packet delivery ratio while attaining lower delay.

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Wireless multi-hop networks are often exposed to serious physical layer jamming attack. In this attack, the jammer node corrupts the packet by injecting high level of noise and keeps the channel busy and thus blocks the legitimate communication. If multiple jammers collude together, this attack will become very severe. To prevent this attack, a simple yet effective Reliability Behavior Neuro-Fuzzy system has been proposed and it operates in three modules. In module one, each route node obtains its behavior value from the route path and neighboring paths using direct and indirect behavior observations. In module two, based on the behavior value, three factor identification methods have been presented to identify the reliability value of nodes. In module three, using the reliability value the route nodes are level positioned and classified into groups by a neuro-fuzzy classifier. By simulation studies, it is observed that the proposed scheme significantly not only identifies misbehaving nodes with higher detection rate and lower false positive and but also achieves higher network throughput and lower jamming throughput.