Design of intrusion detection, and MANET prevention mechanism, with scrutinized detection rate, memory consumption with minimal overhead are crucial research concerns. Node mobility and energy of the node are dual essential optimization issues in mobile ad hoc networks (MANETs) where nodes traverse uncertainly in any direction, evolving in topology's continuing modification. A Centrality Epilson Greedy Swarm and Gradient Deep Belief Classifier (CEGS-GDBC) for multi-attack intrusion detection are designed with the proposed method. The paper concentrates on the issues of node mobility and energy to emerge a clustering algorithm inspired by Dual Network Centrality for cluster head election in MANET. Compact cluster formation is done with the help of Epilson Greedy Swarm Optimization. Finally, with a hybrid type of IDS, Gradient using the Deep Belief Network Classifier identifies multi-attack, i.e., DoS and Zero-Day attack. The proposed work is experimented extensively in the NS-2 network simulator and compared with the other existing algorithms. The proposed method's performance is studied in terms of different parameters such as attack detection rate, memory consumption, and computational time for identifying and isolating the intruder. Simulation results show that the proposed method extensively minimizes the IDS traffic and overall memory consumption and maintains a high attack detection rate with minimal computational time. From the results, CEGS-GDBC method increases the attack detection rate by 31% and reduces the memory consumption and computational time by 39% and 41% as compared to Fuzzy elephant—Herd optimization and Cross centric intrusion detection system.

具有严格的检测率，最小的内存消耗和最小开销的入侵检测和MANET预防机制的设计是至关重要的研究重点。节点的移动性和节点的能量是移动自组织网络（MANET）中的双重基本优化问题，在该网络中，节点在任何方向上都不确定地穿越，从而不断发展着拓扑结构的不断变化。提出了一种用于多攻击入侵检测的集中式埃皮尔森贪婪群和梯度深度信念分类器（CEGS-GDBC）。本文着眼于节点移动性和能量的问题，提出了一种基于双网中心性的集群算法，用于MANET中的簇头选举。借助Epilson Greedy Swarm Optimization完成紧凑的集群形成。最后，使用IDS的混合类型，使用“深信度网络分类器”的渐变可识别多重攻击，即DoS和零日攻击。拟议的工作在NS-2网络模拟器中进行了广泛的实验，并与其他现有算法进行了比较。从攻击检测率，内存消耗以及识别和隔离入侵者的计算时间等不同参数出发，研究了该方法的性能。仿真结果表明，该方法可以最大程度地减少IDS流量和总体内存消耗，并以最少的计算时间保持较高的攻击检测率。从结果来看