Abstract
An important problem in wireless communication networks (WCNs) is that they have a minimum number of resources, which leads to high-security threats. An approach to find and detect the attacks is the intrusion detection system (IDS). In this paper, the fuzzy lion Bayes system (FLBS) is proposed for intrusion detection mechanism. Initially, the data set is grouped into a number of clusters by the fuzzy clustering algorithm. Here, the Naive Bayes classifier is integrated with the lion optimization algorithm and the new lion naive Bayes (LNB) is created for optimally generating the probability measures. Then, the LNB model is applied to each data group, and the aggregated data is generated. After generating the aggregated data, the LNB model is applied to the aggregated data, and the abnormal nodes are identified based on the posterior probability function. The performance of the proposed FLBS system is evaluated using the KDD Cup 99 data and the comparative analysis is performed by the existing methods for the evaluation metrics accuracy and false acceptance rate (FAR). From the experimental results, it can be shown that the proposed system has the maximum performance, which shows the effectiveness of the proposed system in the intrusion detection.
摘要:
无线通信网络(WCNs)的一个重要问题是它们拥有最少的资源, 这就导致了高安全性的威胁. 入侵检测系统(IDS)是一种发现和检测攻击的方法. 提出了一种用于入侵检测的模糊狮子贝叶斯系统 (FLBS). 首先, 采用模糊聚类算法对数据集进行聚类. 将朴素贝叶斯分类器与狮子优化算法相结合, 建立新的狮子朴素贝叶斯(LNB), 实现概率测度的最优生成. 然后, 将LNB 模型应用于每个数据组, 生成聚合数据. 在生成聚集数据后, 将LNB 模型应用于聚合数据, 并基于后验概率函数对异常节点 进行识别. 利用KDD CUP 99 数据对所提出的FLBS 系统的性能进行评价, 并对现有的评价指标、准 确性和错误接受率(FAR)进行比较分析. 实验结果表明, 该系统具有最大的性能, 说明了该系统在入 侵检测中的有效性.
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Narendrasinh, B.G., Vdevyas, D. FLBS: Fuzzy lion Bayes system for intrusion detection in wireless communication network. J. Cent. South Univ. 26, 3017–3033 (2019). https://doi.org/10.1007/s11771-019-4233-1
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DOI: https://doi.org/10.1007/s11771-019-4233-1
Key words
- intrusion detection
- wireless communication network
- fuzzy clustering
- naive Bayes classifier
- lion naive Bayes system