A Novel Composite Intrusion Detection System (CIDS) for Wireless Sensor Network

Swaminathan K, V. Ravindran, R. Ponraj, S. Venkatasubramanian, K. Chandrasekaran, S. Ragunathan
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引用次数: 1

Abstract

Modern wireless technology demands the implementation of preset Sensor nodes for a structured wireless network. The network has sensor nodes for surveillance or environmental sensing, which wirelessly transmit data to a collection point. Therefore, data transfer must be protected by preventing external intrusion attacks. This will be handled by designing an effective intrusion detection system proposed as a Composite Intrusion detection system (CIDS). It is suitable for a network in heterogeneous network structure with a capable of identifying externals attacks like flooding of data's, sending unwanted data packets and changing the destination node. For routing of data packets between the nodes, minimum power utilization with changeable cluster heading method is used. The activities of sensor nodes will be monitored and a dataset is formed on the basis of the node’s activity. It is known as Network Databases (NDB). Using this dataset, the intrusion attacks will be identified by using Artificial Neural Network (ANN). ANN will be trained with a predefined dataset for the effective identification of external attacks. The proposed CIDS methodology shows the high accuracy of identifying the external attacks on the sensor networks when comparing to the previous designed system in all the types of attacks.
一种新的无线传感器网络复合入侵检测系统(CIDS)
现代无线技术要求在结构化无线网络中实现预设的传感器节点。该网络具有用于监视或环境传感的传感器节点,它们将数据无线传输到一个收集点。因此,必须通过防止外部入侵攻击来保护数据传输。这将通过设计一种有效的入侵检测系统来解决,该系统被称为复合入侵检测系统(CIDS)。它适用于异构网络结构的网络,具有识别外部攻击的能力,如数据泛滥、发送不需要的数据包和改变目的节点。对于节点间的数据包路由,采用可变簇头最小功耗方法。传感器节点的活动将被监控,并在节点活动的基础上形成数据集。它被称为网络数据库(NDB)。利用该数据集,利用人工神经网络(ANN)识别入侵攻击。人工神经网络将使用预定义的数据集进行训练,以有效识别外部攻击。与之前设计的系统相比,所提出的CIDS方法在所有类型的攻击中都具有较高的识别传感器网络外部攻击的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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