Detection and Minimization of Malware by Implementing AI in SMEs

Nisha Rawindaran, Liqaa Nawaf, Vibhushinie Bentotahewa, Edmond Prakash, Ambikesh Jayal, Chaminda Hewage, Daniyal Mohammed N. Alghazzawi

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 defence 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
PublisherIntechOpen
ISBN (Electronic)978-1-83768-446-5
DOIs
Publication statusPublished - 23 Dec 2022

Keywords

  • machine learning
  • artificial intelligence
  • cyber defence
  • big data
  • privacy

Cite this