Computer virus and protection methods using lab analysis

Research output: Book chapter/Published conference paperConference paperpeer-review


The aim of this paper is to explore the hypothesis of a computer virus threat, and how destructive it can be if executed on a targeted machine. What are the possible counter measures to protect computers from these threats? In this study, we performed an analysis from the data extracted from different test of scenarios and labs conducted in a test environment. Information security risks associated with computer viruses can infect computers and other storage devices by copying themselves into a file and other executable programs. These file get infection and allow attackers to connect to target systems by using backdoors. The results of this study show that, the proper security implementations and the use of up to date operating systems patches and anti-virus programs helps users to prevent the loss of data and any viral attack on the system. Nevertheless, this observation could be used for further research in the network security and related fields; this study will also help computer users to use the possible steps and techniques to protect their systems and information from any possible attacks on their network systems.
Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA 2017)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781509036196
ISBN (Print)9781509036189, 9781509036202
Publication statusPublished - 23 Oct 2017
Event2nd IEEE International Conference on Big Data Analysis: ICBDA 2017 - Beijing Post Hotel, Beijing, China
Duration: 10 Mar 201712 Mar 2017 (Conference website) (Conference proceedings)


Conference2nd IEEE International Conference on Big Data Analysis
OtherIn recent years, "Big Data" has become a new ubiquitous term. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and ultimately society itself. The 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA 2017) provides a leading forum for disseminating the latest research in Big Data Research, Development, and Application. ICBDA1017 is co-organized by IEEE and Research Institute of Big Data Analytics, Xi'an Jiaotong-Liverpool University, China. Assisted by University of Texas at Dallas, USA. Prof. Steven Guan (Xi'an Jiaotong-Liverpool University, China) and Prof. Kang Zhang (University of Texas at Dallas, USA) take charge of the Conference Co-chair.
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