Assault Type Detection in WSN Based on Modified DBSCAN with Osprey Optimization Using Hybrid Classifier LSTM with XGBOOST for Military Sector

IF 1 Q4 OPTICS Optical Memory and Neural Networks Pub Date : 2024-03-25 DOI:10.3103/s1060992x24010089

R. Preethi

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Abstract

Military tasks constitute the most important and significant applications of Wireless sensor networks (WSNs). In military, Sensor node deployment increases activities, efficient operation, saves loss of life, and protects national sovereignty. Usually, the main difficulties in military missions are energy consumption and security in the network. Another major security issues are hacking or masquerade attack. To overcome the limitations, the proposed method modified DBSCAN with OSPREY optimization Algorithm (OOA) using hybrid classifier Long Short-Term Memory (LSTM) with Extreme Gradient Boosting (XGBOOST) to detect attack types in the WSN military sector for enhancing security. First, nodes are deployed and modified DBSCAN algorithm is used to cluster the nodes to reduce energy consumption. To select the cluster head optimally by using the OSPREY optimization Algorithm (OOA) based on small distance and high energy for transfer data between the base station and nodes. Hybrid LSTM-XGBOOST classifier utilized to learn the parameter and predict the four assault types such as scheduling, flooding, blackhole and grayhole assault. Classification and network metrics including Packet Delivery Ratio (PDR), Throughput, Average Residual Energy (ARE), Packet Loss Ratio (PLR), Accuracy and F1_score are used to evaluate the performance of the model. Performance results show that PDR of 94.12%, 3.2 Mbps throughput at 100 nodes, ARE of 8.94J, PLR of 5.88%, accuracy of 96.14%, and F1_score of 95.04% are achieved. Hence, the designed model for assault prediction types in WSN based on modified DBSCAN clustering with a hybrid classifier yields better results.

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基于改进的 DBSCAN 和 Osprey 优化的 WSN 攻击类型检测，使用混合分类器 LSTM 和 XGBOOST，用于军事领域

摘要军事任务是无线传感器网络（WSN）最重要和最显著的应用。在军事领域，部署传感器节点可以增加活动、提高运行效率、避免人员伤亡并保护国家主权。通常，军事任务中的主要困难是能源消耗和网络安全。另一个主要安全问题是黑客攻击或伪装攻击。为了克服这些局限性，本文提出的方法利用混合分类器长短期记忆（LSTM）与极端梯度提升（XGBOOST）对 DBSCAN 与 OSPREY 优化算法（OOA）进行了改进，以检测 WSN 军事领域的攻击类型，从而提高安全性。首先，部署节点并使用改进的 DBSCAN 算法对节点进行聚类，以降低能耗。根据基站和节点之间传输数据的小距离和高能量，使用 OSPREY 优化算法（OOA）优化选择簇头。利用 LSTM-XGBOOST 混合分类器学习参数并预测四种攻击类型，如调度攻击、洪水攻击、黑洞攻击和灰洞攻击。分类和网络指标包括数据包交付率（PDR）、吞吐量、平均剩余能量（ARE）、数据包丢失率（PLR）、准确率和 F1_score 用于评估模型的性能。性能结果显示，PDR 为 94.12%，100 个节点的吞吐量为 3.2 Mbps，ARE 为 8.94J，PLR 为 5.88%，准确率为 96.14%，F1_score 为 95.04%。因此，基于改进的 DBSCAN 聚类和混合分类器设计的 WSN 攻击预测类型模型取得了较好的效果。

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来源期刊

Optical Memory and Neural Networks OPTICS-

CiteScore

1.50

自引率

11.10%

发文量

期刊介绍： The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.