Secure Passive Keyless Entry and Start System Using Machine Learning

Usman Ahmad, Hong Song, Awais Bilal, Mamoun Alazab, Alireza Jolfaei

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Citations (Scopus)


Despite the benefits of the passive keyless entry and start (PKES) system in improving the locking and starting capabilities, it is vulnerable to relay attacks even though the communication is protected using strong cryptographic techniques. In this paper, we propose a data-intensive solution based on machine learning to mitigate relay attacks on PKES Systems. The main contribution of the paper, beyond the novelty of the solution in using machine learning, is in (1) the use of a set of security features that accurately profiles the PKES system, (2) identifying abnormalities in PKES regular behavior, and (3) proposing a countermeasure that guarantees a desired probability of detection with a fixed false alarm rate by trading off the training time and accuracy. We evaluated our method using the last three months log of a PKES system using the Decision Tree, SVM, KNN and ANN and provide the comparative analysis of the relay attack detection results. Our proposed framework leverages the accuracy of supervised learning on known classes with the adaptability of k-fold cross-validation technique for identifying malicious and suspicious activities. Our test results confirm the effectiveness of the proposed solution in distinguishing relayed messages from legitimate transactions.

Original languageEnglish
Title of host publicationSecurity, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings
EditorsLaurence T. Yang, Guojun Wang, Jinjun Chen
Number of pages10
ISBN (Electronic)978-3-030-05345-1
ISBN (Print)978-3-030-05344-4
Publication statusPublished - 7 Dec 2018
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11342 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Internet of Things
  • Machine learning
  • Passive keyless entry and start
  • Relay attack
  • Vehicle security


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