Title page for etd-0730116-223811


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URN etd-0730116-223811
Author Yu-han Chang
Author's Email Address No Public.
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Department Information Management
Year 2016
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Improving Detection Efficiency using Cloud Computing
Date of Defense 2016-07-25
Page Count 58
Keyword
  • Distributed File System
  • Cloud Computing
  • Intrusion Detection System
  • Abstract Recently, with the popularity and convenience features of Internet, Internet has become one of the attacker profitable way to enter the local area network. Most organizations, companies and government agencies will purchase the firewall, intrusion detection systems, intrusion prevention systems or other information security system to prevent and defense their network.
    With the increasing of the security infrastructure and system, these problems can have a significant impact on organizations. For example, All kinds of Raw Log Messages in different formats and big data storage are important issues. The traditional data analysis architecture by means of a powerful server has serious performance issues when processing big data.
    This study proposes a cloud computing architecture by deploy the settings of storage space, number of namenode and datanode, CPU, memory and network bandwidth to make cloud computing system more efficacy. This study proposes an open source cloud computing platform solution for storing and analyzing big data. Clustered and distributed storage provided by the open source cloud platform, Hadoop, improves the time and storage issue faced in traditional centralized architecture. To improve the bottleneck of the read/write access time during big data processing, in-memory processing technology, Spark, is adopted to reduce the number of disk accesses. The experimental results demonstrate that the proposed cloud platform provides a great performance improvement.
    Advisory Committee
  • Gu-Hsin Lai - chair
  • Hui-Tang Lin - co-chair
  • Chih-Hung Wang - co-chair
  • Chia-Mai Chen - advisor
  • Files
  • etd-0730116-223811.pdf
  • Indicate in-campus at 5 year and off-campus access at 5 year.
    Date of Submission 2016-09-05

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