Sharma, Priynka and Chaudhary, Kaylash C. (2024) Bio-Inspired Negative Selection Tested for Ransomware Anomaly Detection. In: Computing and Machine Learning. Lecture Notes in Networks and Systems, 856 . Springer Nature, Singapore, pp. 253-265. ISBN 978-981-97-7570-5
Full text not available from this repository.Abstract
Ransomware attacks represent a danger to the security and integrity of computer systems, resulting in considerable monetary losses and operational disruptions across various industries. Alternative methods must be developed since traditional signature-based techniques have trouble identifying new and polymorphic ransomware strains. In this paper, we examine the performance of a Negative Selection (NS) method for ransomware anomaly detection that is bio-inspired. The suggested approach is inspired by the ability of the human immune system to identify and destroy foreign substances. It uses the NS mechanism to recognize self and non-self-patterns and identify ransomware abnormalities. The system uses a population of false cells represented as binary strings and a self/non-self-conditional model to detect the existence of ransomware. We experimented with a comprehensive dataset of ransomware samples using MATLAB and WEKA to assess the algorithm’s efficacy. The findings showed that the bio-inspired NS algorithm outperformed (98.9%) compared to conventional machine learning techniques. The trials also revealed that the suggested method had a low rate of false positives and detection abilities, making it appropriate for real-time ransomware detection in various security application settings.
| Item Type: | Book Chapter |
|---|---|
| Subjects: | T Technology > T Technology (General) |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Priynka Sharma |
| Date Deposited: | 08 May 2025 03:19 |
| Last Modified: | 08 May 2025 03:19 |
| URI: | https://repository.usp.ac.fj/id/eprint/14898 |
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