A NOVEL NETWORK INTRUSION DETECTION SYSTEM FOR PREVENTING FLOODING ATTACKS PACKET DROPPING ATTACKS IN MANETS USING DEEP LEARNING ALGORITHM (Conference Paper)

Abstract

The network intrusion detection System (NIDS) is more required for maintaining security in the networks. On the other hand it faces more challenges in wireless network than compared to wired network. A wireless network with several nodes connected to one another via wireless components like transmitters and receivers is termed as Mobile Ad Hoc Network (MANET). The characteristics of MANETs are unique pattern, bandwidth, energy, unstable topology, and security. Because of this, MANETs are susceptible to an extensive collection of extortions and assaults, including denial of service (DoS), flooding, impersonation, black holes, and gray holes attacks. This research concentrates on attacks in MANET. This study introduces a novel Enhanced Generative Adversarial Network with Bidirectional Long Short-Term Memory and Cross-correlated Convolutional Neural Network (EGAN-BiLSTM-CCNN) model in MANET. This model was deployed in cluster heads (CHs) for IDS based on the local information of nodes. First this work uses network simulator (NS2) to simulate the flooding, packet dropping in MANET environment. The parameters derived from the nodes must be capable to accurately depict network behavior and distinguish the typical and anomalous network activity. Every sampling interval time results in the creation of a training dataset that includes every training instance, network activity during the designated interval, and an indication of the types of attacks that occurred during this interval. The EGAN-BiLSTM-CCNN IDS model deployed within each CH for intrusion detection, achieving a balance between security and performance in MANETs. Next, the developed model is utilized in the cluster header to identify malicious nodes, hence preventing MANET attacks and improving network speed.