Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : S. Senthil Kumar, S. Mythili
DOI :  10.14445/22315381/IJETT-V70I5P238

Citation 

MLA Style: Senthil Kumar, S., and Mythili, S. "Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach." International Journal of Engineering Trends and Technology, vol. 70, no. 5, May. 2022, pp. 346-356. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P238

APA Style:Senthil Kumar, S., & Mythili, S. (2022). Fuzzy Anomalous Rules-based Car Hacking Detection using Lasso Regression Approach. International Journal of Engineering Trends and Technology, 70(5), 346-356. https://doi.org/10.14445/22315381/IJETT-V70I5P238

Abstract
Car hacking is the exploitation of vulnerabilities within cars` software, hardware, and communication systems. Various kinds of attacks can be injected to perform car hacking, affecting the electronic control unit to exploit the vulnerability. Predicting whether car hacking is present or not is the most difficult task. In the previous research, we introduced the Lasso Regression-based Improved Anomalous Detection Algorithm (LR-IADS).The main aim of the research work is to implementa credit card dataset to predict whether the fraudulent transaction is happening in the environment. In this research work, anomalous fuzzy rules were created initially with the help of attributes chosen from the database. Based on the Gini index, information gain, and gain ratio, we choose the attributes here. The lasso regression analysis method helps to do the rule pruning on the generated anomalous rules.
At last, unexpected suspicious detection is done according to these anomalous rules by commencing the classification process. IRVMs (Improved Relevant Vector Machines) perform it based on Association Classifiers. This research work is implemented on the car hacking database for intrusion detection gathered from the controller area network. The complete analysis of the study work is performed in a Matlab simulation platform, demonstrating that the suggested LR-IADS approach may provide accurate car hacking detection results.

Keywords
Anomalous Rules, Car Hacking Detection, Gini Index, Information Gain, Relevance Vector Machine.

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