Cognitive Analysis Based On Machine Learning For Web Logs

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Imani Hamid; Dadashtabar Kourosh

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Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2020 | Issue:6

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Title

Cognitive Analysis Based On Machine Learning For Web Logs

Author(s)

Imani Hamid | Dadashtabar Kourosh

Keywords

Log Analysis

Security

Machine Learning

Multilayer Perceptron Neural Network

Abstract

Logs contain valuable information about user actions on the web that are widely used in a variety of Security, industry and science areas. Logs are an excellent resource for determining the health of a system. An enormous amount of logs are generated every day that record users' activities, which is very difficult to analyze in traditional ways, so there is a need for intelligent and cognitive analytics. Machine Learning techniques are currently used as an efficient tool in the processing and analysis of web logs. In this paper, we used a 5-layer neural network with 2461 parameters and sigmoid, hyperbolic and relay activation functions, in addition to intelligently detecting cyber threats to optimal results in accuracy and minimizing error function. In analyzing web logs compared to other conventional Machine Learning methods such as clustering, decision tree, support vector machines, principal component analysis, isolated forest and logistic regression.

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APA: Copy

Imani, Hamid, & Dadashtabar, Kourosh. (2020). Cognitive Analysis Based On Machine Learning For Web Logs. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949286/en

Vancouver: Copy

Imani Hamid, Dadashtabar Kourosh. Cognitive Analysis Based On Machine Learning For Web Logs. 2020. Available from: https://sid.ir/paper/949286/en

IEEE: Copy

Hamid Imani, and Kourosh Dadashtabar, “Cognitive Analysis Based On Machine Learning For Web Logs,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2020, [Online]. Available: https://sid.ir/paper/949286/en

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