利用Hadoop平台分析恶意软件日志文件进行网络调查

Mohd Sharudin Mat Deli, Saiful Adli Ismail, M. Kama, O. Yusop, Azri Azmi
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引用次数: 0

摘要

为了保护计算机和互联网用户免受恶意软件攻击,通过调查恶意软件日志文件来识别攻击是遏制这种威胁的必要步骤。日志文件暴露了识别恶意软件的关键信息,例如算法和功能特征、源和目标之间的网络交互以及恶意软件的类型。从本质上讲,日志文件的大小是巨大的,需要在大数据环境等更快、更稳定的平台上执行调查过程。本研究采用Hadoop技术对从高校安全设备中获取的恶意软件日志文件进行信息处理和提取。使用Python程序进行数据转换,然后在Hadoop模拟环境下进行分析。日志处理的结果减少了原始日志文件大小的50%,而总执行时间不会随着数据大小线性增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysing malware log files for internet investigation using Hadoop platform
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.
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