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 Paper published in ProceedingsResearchpeer-review

Abstract

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
PublisherSpringer-Verlag London Ltd.
Pages304-313
Number of pages10
Volume11342
ISBN (Print)9783030053444
DOIs
Publication statusPublished - Dec 2018
Event11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018 - Melbourne, Australia
Duration: 11 Dec 201813 Dec 2018

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

Conference

Conference11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018
CountryAustralia
CityMelbourne
Period11/12/1813/12/18

Fingerprint

Learning systems
Machine Learning
Relay
Attack
Supervised learning
Decision trees
Probability of Detection
False Alarm Rate
Locking
Supervised Learning
Countermeasures
Adaptability
Comparative Analysis
Cross-validation
Decision tree
Leverage
Transactions
Fold
Communication

Cite this

Ahmad, U., Song, H., Bilal, A., Alazab, M., & Jolfaei, A. (2018). Secure passive keyless entry and start system using machine learning. In L. T. Yang, G. Wang, & J. Chen (Eds.), Security, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings (Vol. 11342, pp. 304-313). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11342 LNCS). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-030-05345-1_26
Ahmad, Usman ; Song, Hong ; Bilal, Awais ; Alazab, Mamoun ; Jolfaei, Alireza. / Secure passive keyless entry and start system using machine learning. Security, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings. editor / Laurence T. Yang ; Guojun Wang ; Jinjun Chen. Vol. 11342 Springer-Verlag London Ltd., 2018. pp. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Ahmad, U, Song, H, Bilal, A, Alazab, M & Jolfaei, A 2018, Secure passive keyless entry and start system using machine learning. in LT Yang, G Wang & J Chen (eds), Security, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings. vol. 11342, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11342 LNCS, Springer-Verlag London Ltd., pp. 304-313, 11th International Conference on Security, Privacy and Anonymity in Computation, Communication, and Storage, SpaCCS 2018, Melbourne, Australia, 11/12/18. https://doi.org/10.1007/978-3-030-05345-1_26

Secure passive keyless entry and start system using machine learning. / Ahmad, Usman; Song, Hong; Bilal, Awais; Alazab, Mamoun; Jolfaei, Alireza.

Security, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings. ed. / Laurence T. Yang; Guojun Wang; Jinjun Chen. Vol. 11342 Springer-Verlag London Ltd., 2018. p. 304-313 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11342 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in ProceedingsResearchpeer-review

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AU - Song, Hong

AU - Bilal, Awais

AU - Alazab, Mamoun

AU - Jolfaei, Alireza

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AB - 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.

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Ahmad U, Song H, Bilal A, Alazab M, Jolfaei A. Secure passive keyless entry and start system using machine learning. In Yang LT, Wang G, Chen J, editors, Security, Privacy, and Anonymity in Computation, Communication, and Storage - 11th International Conference and Satellite Workshops, SpaCCS 2018, Proceedings. Vol. 11342. Springer-Verlag London Ltd. 2018. p. 304-313. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-05345-1_26