Title: Analysing malware log files for internet investigation using Hadoop platform

Authors: Mohd Sharudin Mat Deli; Saiful Adli Ismail; Mohd Nazri Kama; Othman Mohd Yusop; Azri Azmi

Addresses: Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Malaysia ' Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Malaysia ' Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Malaysia ' Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Malaysia ' Advanced Informatics School, Level 5, Menara Razak, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Malaysia

Abstract: To protect the computer and internet users from exposing themselves towards malware attacks, identifying the attacks through investigating malware log file is an essential step to curb this threat. The log file exposes crucial information in identifying the malware, such as algorithm and functional characteristic, the network interaction between the source and the destination, and type of malware. By nature, the log file size is humongous and requires the investigation process to be executed on faster and stable platform such as big data environment. In this study, Hadoop technology used to process and extract the information from the malware log files that obtains from university's security equipment. The Python program was used for data transformation then analysis it in Hadoop simulation environment. The results of log processing have reduced 50% of the original log file size, while the total execution time would not increase linearly with the size of the data.

Keywords: internet security; types of malware; malware log files; big data environment; Hadoop environment; log file processing; log files in Hadoop.

DOI: 10.1504/IJDET.2019.103357

International Journal of Digital Enterprise Technology, 2019 Vol.1 No.4, pp.317 - 332

Received: 22 Jan 2018
Accepted: 25 Jun 2018

Published online: 05 Nov 2019 *

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