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Generalization of a Machine Learning...

Tricarico, Andrea,

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Generalization of a Machine Learning Classifier of CAN Bus Signals /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Generalization of a Machine Learning Classifier of CAN Bus Signals // Andrea Tricarico.
Author:	Tricarico, Andrea,
Description:	1 electronic resource (99 pages)
Notes:	Source: Masters Abstracts International, Volume: 82-10.
Contained By:	Masters Abstracts International82-10.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28334279
ISBN:	9798557098434

Generalization of a Machine Learning Classifier of CAN Bus Signals /
Tricarico, Andrea,

Generalization of a Machine Learning Classifier of CAN Bus Signals /Andrea Tricarico. - 1 electronic resource (99 pages)

Source: Masters Abstracts International, Volume: 82-10.

In the last decades, the automotive field has evolved considerably. Modern cars are composed of a complex network of Electronic Control Units (ECUs). These ECUs are small computing units that elaborate data captured from different sensors and then perform actions throw actuators. They can optimize a variety of different actions performed by the driver (e.g. the fuel injection during acceleration) and activate safety mechanisms. Moreover, in the latest years, manufacturers have introduced new functionalities, like autonomous driving features and the possibility to control the car remotely, which require a complex network of computing units inside the vehicle.All the ECUs are connected with an in-vehicle network which is usually designed following the CAN bus protocol developed by Bosch in the '80s (which is the de-facto standard for the internal networks of modern vehicles). This protocol is particularly suitable to design real-time networks that are simple and cheap. The security against external attacks was not a problem at that time because the vehicles were not accessible from the external world. However, in the last years, more and more cars have introduced features to connect the vehicle to external devices (like the smartphone of the driver) and networks (like the Internet). These changes have considerably increased the attack surface and the lack of security in the CAN bus protocol is becoming a serious problem.Many studies have demonstrated that it is possible, for an attacker, to gain control of some vehicle's functionalities through the injection of malicious packets in the CAN bus and that this can be done even remotely exploiting vulnerabilities of the computing units that are connected to external devices.Unfortunately, manufacturers are still not paying enough attention to the security of the in-vehicles networks and they rely on a security-by-obscurity paradigm (keeping secret the detailed specification about the logic inside the ECUs and the syntax of the packets).Researchers that study the security of modern vehicles and their vulnerabilities have to manually reverse engineer the packets sent from the ECUs to understand their internal logic and find their vulnerabilities.In this work, we propose a new approach to the reverse engineering of CAN bus packets to reduce the effort and time needed for this process.We analyze directly the raw logs that the researchers collect from their vehicles during a driving session. We extract the different signals that are encoded in the recorded sequence of CAN packets and process them.We then use a machine learning classifier based on LSTM networks (a type of recurrent neural network particularly suited for the analysis of time-series) to find some specific physical signals without any prior knowledge on the vehicle architecture and without performing long experiments on the vehicle.In our evaluation phase, we demonstrate that this model can drastically reduce the time needed to reverse engineer messages and signals of a vehicle without its proprietary specification. We then explain how to collect a suitable dataset to train the model and make it able to generalize the knowledge to classify signals of unknown vehicles.

English

ISBN: 9798557098434Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Automotive

Generalization of a Machine Learning Classifier of CAN Bus Signals /
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500 $a Source: Masters Abstracts International, Volume: 82-10.
500 $a Advisors: Gjomemo, Rigel Committee members: Buy, Ugo A.; Zanero, Stefano.
502 $b M.S. $c University of Illinois at Chicago $d 2020.
520 $a In the last decades, the automotive field has evolved considerably. Modern cars are composed of a complex network of Electronic Control Units (ECUs). These ECUs are small computing units that elaborate data captured from different sensors and then perform actions throw actuators. They can optimize a variety of different actions performed by the driver (e.g. the fuel injection during acceleration) and activate safety mechanisms. Moreover, in the latest years, manufacturers have introduced new functionalities, like autonomous driving features and the possibility to control the car remotely, which require a complex network of computing units inside the vehicle.All the ECUs are connected with an in-vehicle network which is usually designed following the CAN bus protocol developed by Bosch in the '80s (which is the de-facto standard for the internal networks of modern vehicles). This protocol is particularly suitable to design real-time networks that are simple and cheap. The security against external attacks was not a problem at that time because the vehicles were not accessible from the external world. However, in the last years, more and more cars have introduced features to connect the vehicle to external devices (like the smartphone of the driver) and networks (like the Internet). These changes have considerably increased the attack surface and the lack of security in the CAN bus protocol is becoming a serious problem.Many studies have demonstrated that it is possible, for an attacker, to gain control of some vehicle's functionalities through the injection of malicious packets in the CAN bus and that this can be done even remotely exploiting vulnerabilities of the computing units that are connected to external devices.Unfortunately, manufacturers are still not paying enough attention to the security of the in-vehicles networks and they rely on a security-by-obscurity paradigm (keeping secret the detailed specification about the logic inside the ECUs and the syntax of the packets).Researchers that study the security of modern vehicles and their vulnerabilities have to manually reverse engineer the packets sent from the ECUs to understand their internal logic and find their vulnerabilities.In this work, we propose a new approach to the reverse engineering of CAN bus packets to reduce the effort and time needed for this process.We analyze directly the raw logs that the researchers collect from their vehicles during a driving session. We extract the different signals that are encoded in the recorded sequence of CAN packets and process them.We then use a machine learning classifier based on LSTM networks (a type of recurrent neural network particularly suited for the analysis of time-series) to find some specific physical signals without any prior knowledge on the vehicle architecture and without performing long experiments on the vehicle.In our evaluation phase, we demonstrate that this model can drastically reduce the time needed to reverse engineer messages and signals of a vehicle without its proprietary specification. We then explain how to collect a suitable dataset to train the model and make it able to generalize the knowledge to classify signals of unknown vehicles.
546 $a English
590 $a School code: 0799
650 4 $a Computer science. $3 523869
650 4 $a Automotive engineering. $3 2181195
650 4 $a Artificial intelligence. $3 516317
653 $a Automotive
653 $a Controller Area Network
653 $a Reverse Engineering
653 $a LSTM
653 $a Machine Learning
653 $a Security
690 $a 0984
690 $a 0540
690 $a 0800
710 2 $a University of Illinois at Chicago. $b Computer Science. $e degree granting institution. $3 3815559
720 1 $a Gjomemo, Rigel $e degree supervisor.
773 0 $t Masters Abstracts International $g 82-10.
790 $a 0799
791 $a M.S.
792 $a 2020
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28334279