IoT are prone to vulnerabilities as a result of a lack of centralized management, dynamic topologies, and predefined boundary. There are diverse attacks that affect the performance of IoT network. Flooding attack is a DoS attack that brings down the network by flooding with a huge count of HELLO packets, routed to a destination that does not exist. The main intent of this paper is to develop a novel robust model for detecting and preventing HELLO flooding attacks using optimized deep learning approach. In this proposed research model, the steps like Cluster head selection, k-paths generation, HELLO flooding attack detection and prevention, and optimal shortest path selection are employed. Once after the random cluster head selection and k-paths generation, few Route Discovery Frequency Vectors like Route Discover Time and Inter Route Discovery Time of each node is determined for detecting the HELLO flooding attack. Initially, a threshold function is used to match with the computed Received Signal Strength (RSS) of each node to detect the stranger node. Further, the HELLO flood attack is confirmed by the optimized Deep Belief Network (DBN), which is removed from the network subsequently. Once after securing the network, the shortest route path selection is done optimally by the improved meta-heuristic algorithm. Here, improved Rider Optimization Algorithm (ROA) termed as Bypass-Linked Attacker Update-based ROA (BAU-ROA) is used for performing the optimal DBN as well as optimal shortest path selection. The objective constraints like node trust, distance between the nodes, delay of transmission, and packet loss ratio are considered for performing the optimal shortest path selection. Finally, the experimental evaluation of various performance measures validates the fruitful performance of the proposed model. Based on the analysis, the const function of proposed BAU-ROA is 28.1% superior to D-DHOA, 34.1% superior to DHOA, and 39.2% superior to WOA at 5th iteration. When considering the 10th iteration, the developed BAU-ROA is 12.2% superior to DHOA, 19.3% superior to D-DHOA, 28.1% superior to ROA, and 39.2% superior to WOA.

物联网由于缺乏集中管理，动态拓扑和预定义的边界而容易受到漏洞攻击。影响物联网网络性能的攻击多种多样。泛洪攻击是一种DoS攻击，它通过泛洪大量HELLO数据包来淹没网络，这些HELLO数据包被路由到不存在的目的地。本文的主要目的是使用优化的深度学习方法开发一种新颖的鲁棒模型，用于检测和预防HELLO泛洪攻击。在提出的研究模型中，采用了簇头选择，k路径生成，HELLO泛洪攻击检测和预防以及最佳最短路径选择等步骤。在随机簇头选择和k路径生成之后，确定每个节点的少数路由发现频率向量（例如路由发现时间和路由间发现时间）以检测HELLO泛洪攻击。最初，阈值函数用于与每个节点的计算接收信号强度（RSS）匹配，以检测陌生节点。此外，已通过优化的深度信任网络（DBN）确认了HELLO泛洪攻击，该网络随后被从网络中删除。一旦保护了网络，最短的路由路径选择将通过改进的元启发式算法进行最佳选择。这里，称为基于旁路链接的攻击者更新的ROA（BAU-ROA）的改进的骑手优化算法（ROA）用于执行最佳DBN以及最佳最短路径选择。客观约束，例如节点信任度，节点之间的距离，传输延迟，为了进行最佳的最短路径选择，考虑了丢包率和丢包率。最后，各种性能指标的实验评估验证了所提出模型的卓有成效。根据分析，在第5次迭代中，拟议的BAU-ROA的const函数比D-DHOA高28.1％，比DHOA高34.1％，比WOA高39.2％。考虑到第10次迭代，开发的BAU-ROA比DHOA高12.2％，比D-DHOA高19.3％，比ROA高28.1％，比WOA高39.2％。第5次迭代比DHOA高1％，比WOA高39.2％。考虑到第10次迭代，开发的BAU-ROA比DHOA高12.2％，比D-DHOA高19.3％，比ROA高28.1％，比WOA高39.2％。第5次迭代比DHOA高1％，比WOA高39.2％。考虑到第10次迭代，开发的BAU-ROA比DHOA高12.2％，比D-DHOA高19.3％，比ROA高28.1％，比WOA高39.2％。