An Efficient Method to Decide the Malicious Traffic

IF 0.6 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Ajay Kumar, Jitendra Singh, Vikas Kumar, Saurabh Shrivastava
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引用次数: 0

Abstract

To address the high rate of false alarms, this article proposed a voting-based method to efficiently predict intrusions in real time. To carry out this study, an intrusion detection dataset from UNSW was downloaded and preprocessed before being used. Given the number of features at hand and the large size of the dataset, performance was poor while accuracy was low. This low prediction accuracy led to the generation of false alerts, consequently, legitimate alerts used to pass without an action assuming them as false. To deal with large size and false alarms, the proposed voting-based feature reduction approach proved to be highly beneficial in reducing the dataset size by selecting only the features secured majority votes. Outcome collected prior to and following the application of the proposed model were compared. The findings reveal that the proposed approach required less time to predict, at the same time predicted accuracy was higher. The proposed approach will be extremely effective at detecting intrusions in real-time environments and mitigating the cyber-attacks.
一种判定恶意流量的有效方法
为了解决误报率高的问题,本文提出了一种基于投票的方法来有效地实时预测入侵。为了进行这项研究,从新南威尔士大学下载了一个入侵检测数据集,并在使用前进行了预处理。考虑到手头的特征数量和数据集的大尺寸,性能较差,而准确性较低。这种低预测准确率导致了虚假警报的产生,因此,合法警报过去常常在没有采取行动的情况下通过,并认为它们是虚假的。为了处理大尺寸和误报,所提出的基于投票的特征约简方法被证明通过只选择获得多数投票的特征来降低数据集的大小是非常有益的。比较了在应用拟议模型之前和之后收集的结果。研究结果表明,所提出的方法需要较少的预测时间,同时预测精度更高。所提出的方法在实时环境中检测入侵和减轻网络攻击方面将非常有效。
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来源期刊
International Journal of Decision Support System Technology
International Journal of Decision Support System Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.20
自引率
18.20%
发文量
40
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