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Web Access Log Anomaly Detection Based on Deep Learning

Published: 13 July 2021 Publication History

Abstract

Network information security is becoming a vital topic for many companies and research institutes with big data. Information leakage via cyber is rising rapidly. Many companies and research institutes managers have not realized the significance of information security. This paper describes an investigation of anomaly detection. We measure the format and content of the original web access log. The intent is to analyze types of access and distribution of access types of each user and to transform them into the type that can be learned by the machine. With the transformed log, a neural network model can be put in place to learn how to detect an abnormal access log and alert the relative computer managers. The main goal is to design a model that can assist computer managers to do the anomaly detection job.

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          cover image ACM Other conferences
          ICSIM '21: Proceedings of the 2021 4th International Conference on Software Engineering and Information Management
          January 2021
          251 pages
          ISBN:9781450388955
          DOI:10.1145/3451471
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          Published: 13 July 2021

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          Author Tags

          1. Anomaly detection
          2. Information security
          3. Neural network

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