Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio.

中文翻译：

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基于动态特征选择和模糊时间决策树分类的无线传感器网络入侵检测

入侵检测系统在提供无线传感器网络的安全性方面承担着值得注意的工作。现有的入侵检测系统仅专注于检测已知类型的攻击。但是，它忽略了识别由恶意用户引入的新型攻击，从而导致网络中的漏洞和信息丢失。为了解决这一挑战，已经提出了一种新的入侵检测系统，该系统使用智能决策树分类算法检测已知和未知类型的攻击。为此，提出了一种新颖的特征选择算法，称为动态递归特征选择算法，该算法从数据集中选择了最优数量的特征。此外，通过扩展决策树算法并结合卷积神经网络，提出了一种智能的模糊时间决策树算法，可以有效地检测入侵者。使用KDD cup数据集和网络跟踪数据集进行的实验分析证明了该方法的有效性。事实证明，所提出的工作减少了误报率，能耗和延迟。另外，所提出的系统通过提高分组传送率来提高网络性能。在拟议的工作中减少了能源消耗和延迟。另外，所提出的系统通过提高分组传送率来提高网络性能。在拟议的工作中减少了能源消耗和延迟。另外，所提出的系统通过提高分组传送率来提高网络性能。