Towards Generation of Attack Trees using Machine Learning

Kacper Jakub Sowka; Madeline Cheah; Trang Doan; Hoang Nga Nguyen; Siraj Shaikh

Towards Generation of Attack Trees using Machine Learning

Kacper Jakub Sowka, Madeline Cheah, Trang Doan, Hoang Nga Nguyen, Siraj Shaikh

HORIBA MIRA Ltd.

Research output: Contribution to conference › Paper › peer-review

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Abstract

A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing are limited due to the black-box nature and complexity of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of testing vehicular systems, but these still require a significant amount of expert input to build test cases. Accordingly, this work aims to further automate the process by introducing a machine learning based generation scheme for data which can then be used to facilitate the creation of further tests.

Original language	English
Number of pages	4
Publication status	Accepted/In press - 24 Apr 2021
Event	15th EuroSys Doctoral Workshop - Virtual Duration: 26 Apr 2021 → 28 Apr 2021

Workshop

Workshop	15th EuroSys Doctoral Workshop
Abbreviated title	EuroSys 2021
Period	26/04/21 → 28/04/21

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Towards Generation of Attack TreesAccepted author manuscript, 296 KB

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title = "Towards Generation of Attack Trees using Machine Learning",

abstract = "A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing are limited due to the black-box nature and complexity of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of testing vehicular systems, but these still require a significant amount of expert input to build test cases. Accordingly, this work aims to further automate the process by introducing a machine learning based generation scheme for data which can then be used to facilitate the creation of further tests.",

author = "Sowka, {Kacper Jakub} and Madeline Cheah and Trang Doan and Nguyen, {Hoang Nga} and Siraj Shaikh",

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note = "15th EuroSys Doctoral Workshop, EuroSys 2021 ; Conference date: 26-04-2021 Through 28-04-2021",

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T1 - Towards Generation of Attack Trees using Machine Learning

AU - Sowka, Kacper Jakub

AU - Cheah, Madeline

AU - Doan, Trang

AU - Nguyen, Hoang Nga

AU - Shaikh, Siraj

PY - 2021/4/24

Y1 - 2021/4/24

N2 - A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing are limited due to the black-box nature and complexity of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of testing vehicular systems, but these still require a significant amount of expert input to build test cases. Accordingly, this work aims to further automate the process by introducing a machine learning based generation scheme for data which can then be used to facilitate the creation of further tests.

AB - A comprehensive cybersecurity evaluation of automotive on-board networks has become a crucial antecedent to the commercial distribution of vehicles. However, the means to perform the required testing are limited due to the black-box nature and complexity of automotive systems. To rectify this, several approaches have been put forward to systematise and automate the process of testing vehicular systems, but these still require a significant amount of expert input to build test cases. Accordingly, this work aims to further automate the process by introducing a machine learning based generation scheme for data which can then be used to facilitate the creation of further tests.

M3 - Paper

T2 - 15th EuroSys Doctoral Workshop

Y2 - 26 April 2021 through 28 April 2021

ER -

Towards Generation of Attack Trees using Machine Learning

Abstract

Workshop

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