Detection and Minimization of Malware by Implementing AI in SMEs

Nisha Rawindaran, Liqaa Niwaf*, Vibhushinie Bentotahewa, Edmond Prakash, Ambikesh Jayal, Chaminda Hewage, Daniyal Alghazzawi

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

The malware can threaten personal privacy by opening backdoors for attackers to access user passwords, IP addresses, banking information, and other personal data, whilst some malware extracts personal data and sends them to people unknown to the users. In this chapter, the authors will present recent case studies and discuss the privacy and security threats associated with different types of malwares. The small medium enterprises (SMEs) have a unique working model forming the backbone of the UK economy and malware affects SMEs’ organizations. Also, the use of Artificial Intelligence (AI) as both an offense and defense mechanism, for the hacker, and the end user will be investigated further. In conclusion, finding a balance between IT expertise and the costs of products that are able to help SMEs protect and secure their data will benefit the SMEs by using a more intelligent controlled environment with applied machine learning techniques and not compromising on costs will be discussed.

Original languageEnglish
Title of host publicationMalware - Detection and Defense
EditorsEduard Babulak
Place of PublicationLondon, United Kingdom
PublisherInTech
Chapter3
ISBN (Electronic)978-1-83768-446-5
ISBN (Print)978-1-83768-445-8 , 978-1-83768-444-1
DOIs
Publication statusPublished - 2022
Externally publishedYes

Keywords

  • Malware
  • privacy
  • cyber defense
  • ransomware
  • SME
  • artificial intelligence
  • machine learning
  • big data
  • GDPR

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