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- W4285280646 abstract "In the last decade, protection of sensitive information and online resources from network intrusion is the critical challenge in the field of cybersecurity. Intrusion detection system provides an essential base for network defence. It requires the adaptive techniques that can handle the zero-day attacks. A zero-day attack imposes severe damage to the network or system. Attackers take hold of the vulnerabilities in the network and perform the zero-day exploit on the Web. This paper presents the machine learning techniques to detect the zero-day exploits with higher accuracy in less amount of time with reduced false alarm rate. The CICIDS dataset is streamed which includes zero-day attacks. The system is implemented using random forest, random tree, Naïve Bayes and Hoeffding tree techniques and evaluated against the performance measures such as accuracy, detection time and memory usage. It found that Hoeffding tree gives the accuracy of 99.97% in 5.94 s with the memory usage of 0.08 MB." @default.
- W4285280646 created "2022-07-14" @default.
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- W4285280646 date "2022-01-01" @default.
- W4285280646 modified "2023-09-26" @default.
- W4285280646 title "Zero-Day Attack Detection Analysis in Streaming Data Using Supervised Learning Techniques" @default.
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- W4285280646 doi "https://doi.org/10.1007/978-981-19-0011-2_46" @default.
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