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Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing
Cluster Computing ( IF 3.6 ) Pub Date : 2021-01-02 , DOI: 10.1007/s10586-020-03222-y
S. Krishnaveni , S. Sivamohan , S. S. Sridhar , S. Prabakaran

Cloud computing is a preferred option for organizations around the globe, it offers scalable and internet-based computing resources as a flexible service. Security is a key concern factor in any cloud solution due to its distributed nature. Security and privacy are huge obstacles faced in its success of the on-demand service as it is easily vulnerable to intruders for any kind of attack. A huge upsurge in network traffic has paved the way to security breaches which are more complicated and widespread. Tackling these attacks has become an inefficient application of traditional intrusion detection systems (IDS) environment. In this research, we developed an efficient Intrusion Detection System (IDS) for the cloud environment using ensemble feature selection and classification techniques. This proposed method was relying on the univariate ensemble feature selection technique, which is used for the selection of valuable reduced feature sets from the given intrusion datasets. While the ensemble classifiers that can competently fuse the single classifiers to produce a robust classifier using the voting technique. An ensemble based proposed method effectively classifies whether the network traffic behavior is normal or attack. The implementation of the proposed method was measured by applying various performance evaluation metrics and ROC-AUC (“area under the receiver operating characteristic curves”) across various classifiers. The results of the proposed methodology achieved a strong considerable amount of performance enhancement compared with other existing methods. Moreover, we performed a pairwise t test and proved that the performance of the proposed method was statistically significantly different from other existing approaches. Finally, the outcome of this investigation was obtained with the best accuracy and lowest false alarm rate (FAR).



中文翻译:

基于集成的高效特征选择和分类算法在云计算中的网络入侵检测

云计算是全球组织的首选选项,它以灵活的服务提供可扩展的基于Internet的计算资源。由于其分布式特性,安全性是任何云解决方案中的关键关注因素。安全和隐私是按需服务成功所面临的巨大障碍,因为按需服务很容易受到入侵者的攻击。网络流量的巨大增长为更复杂,更广泛的安全漏洞铺平了道路。应对这些攻击已成为传统入侵检测系统(IDS)环境的低效应用。在这项研究中,我们使用集成特征选择和分类技术为云环境开发了有效的入侵检测系统(IDS)。该提出的方法依赖于单变量集成特征选择技术,该技术用于从给定的入侵数据集中选择有价值的简化特征集。而能够有效融合单个分类器以使用投票技术生成鲁棒分类器的集成分类器。基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 用于从给定的入侵数据集中选择有价值的精简特征集。而能够有效融合单个分类器以使用投票技术生成鲁棒分类器的集成分类器。基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 用于从给定的入侵数据集中选择有价值的精简特征集。而能够有效融合单个分类器以使用投票技术生成鲁棒分类器的集成分类器。基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 而能够有效融合单个分类器以使用投票技术生成鲁棒分类器的集成分类器。基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来衡量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 而能够有效融合单个分类器以使用投票技术生成鲁棒分类器的集成分类器。基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的 基于整体的提议方法有效地对网络流量行为是正常还是攻击进行了分类。通过在各种分类器上应用各种性能评估指标和ROC-AUC(“接收器工作特性曲线下的区域”)来测量所提出方法的实施情况。与其他现有方法相比,所提出方法的结果获得了相当大的性能增强。而且,我们进行了成对的t检验,证明该方法的性能与其他现有方法在统计学上有显着差异。最后,以最高的准确性和最低的误报率(FAR)获得了该调查的结果。

更新日期:2021-01-03
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