Lightweight Diagnostic-based Secure Framework for Electronic Control Units in Vehicles

Awaad, Tasneem A.; El-Kharashi, M. Watheq; Taher, Mohamed

doi:10.1109/isncc52172.2021.9615864

Cited by 5 publications

(6 citation statements)

References 18 publications

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“…Our work is inspired by different published research papers [ 16 , 17 , 18 ] that use various deep learning and machine learning models. We extended our previous works [ 19 , 20 ], where the first research introduced a framework (malicious diagnostic detection framework V1) of one layer that uses one machine learning model to detect simple point anomaly attacks in vehicle diagnostics and developed later in the second work, whereas the framework (malicious diagnostic detection framework V2) comprises two layers: the first one is the specification-based detection layer and the second one is the anomaly detection layer that uses Extreme Gradient Boosting (XGBoost) only, which aims to detect complex point anomaly attacks.…”

Section: Introductionmentioning

confidence: 99%

Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework

Awaad,

El-Kharashi,

Taher

et al. 2023

Sensors

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The advanced technology of vehicles makes them vulnerable to external exploitation. The current trend of research is to impose security measures to protect vehicles from different aspects. One of the main problems that counter Intrusion Detection Systems (IDSs) is the necessity to have a low false acceptance rate (FA) with high detection accuracy without major changes in the vehicle network infrastructure. Furthermore, the location of IDSs can be controversial due to the limitations and concerns of Electronic Control Units (ECUs). Thus, we propose a novel framework of multistage to detect abnormality in vehicle diagnostic data based on specifications of diagnostics and stacking ensemble for various machine learning models. The proposed framework is verified against the KIA SOUL and Seat Leon 2018 datasets. Our IDS is evaluated against point anomaly attacks and period anomaly attacks that have not been used in its training. The results show the superiority of the framework and its robustness with high accuracy of 99.21%, a low false acceptance rate of 0.003%, and a good detection rate (DR) of 99.63% for Seat Leon 2018, and an accuracy of 99.22%, a low false acceptance rate of 0.005%, and good detection rate of 98.59% for KIA SOUL.

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Section: Introductionmentioning

confidence: 99%

Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework

Awaad,

El-Kharashi,

Taher

et al. 2023

Sensors

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“…The goal of this study is to detect malicious behaviors in vehicle diagnostics with acceptable accuracy. Thus, we expanded our previous proposed IDS [13] to introduce a framework of two stages: the first stage is responsible for detecting the unreasonable values of diagnostics based on the specification rules for each PID, while the second stage is in charge of detecting malicious semantic values of PIDs using machine learning. Since the current stream of research is interested in the utilization of machine learning and deep learning approaches to detect complex attacks, the XGBoost algorithm is employed in the second stage of our framework to detect suspicious data as it is a widespread and efficient open-source framework for this aim [14].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent, Two-Stage, In-Vehicle Diagnostic-Based Secured Framework

et al. 2022

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Recent research interests have been directed to study the security of vehicles due to the advancement of their technologies. Due to the rapid growth and accelerated development of electronic control units (ECUs), they are countered to be exploited by external attacks. As a result, recent research efforts have been focused on investigating alternative countermeasures that might be implemented by introducing different intrusion detection systems (IDSs). The problem with some of IDSs is the location of their deployment because of the ECU limitations and constraints. Other introduced IDSs require severe changes in the in-vehicle network, which is not preferred by vehicle manufacturers. In this research, we introduce a novel design of a framework to check the state of the vehicle and capture possible attacks by detecting any malicious data in the diagnostic parameters of the vehicle. The framework is divided into two phases: the specific-based detection phase and the anomaly-based detection phase. The proposed system employs the extreme gradient boosting (XGBoost) algorithm to detect anomalies in diagnostic data and it is optimized by a non-dominated sorting genetic algorithm II (NSGA-II). The model is verified against two datasets collected from real vehicles. To generate anomalies in datasets, an attack generation algorithm is introduced. The model is trained on a dataset that contains different attack types and verified blindly against various attacks that have not been seen before. The framework's experimental results show that it can detect abnormalities with accuracy 97.00% for the Seat Leon 2018 dataset and 97.49% for the KIA SOUL dataset.

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“…After that, this ISA is realized on an ASIP. The automotive cybersecurity aspect has been addressed by other recent papers [15][16][17] as well, in different ways.…”

Section: Introductionmentioning

confidence: 99%

Two-Layer Bus-Independent Instruction Set Architecture for Securing Long Protocol Data Units in Automotive Open System Architecture-Based Automotive Electronic Control Units

et al. 2022

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In this paper, we propose a bus-independent hardware (HW)-based approach to secure long protocol data units (PDUs) in Automotive Open System Architecture (AUTOSAR)-based automotive electronic control units (ECUs). Our approach is based on extending previous works that implemented two AUTOSAR communication (COM) application-specific instruction set processors (ASIPs). COM ASIP V1 introduced two instructions to handle the transmission and reception of PDUs no larger than 8 bytes and signals no larger than 32 bits individually through send signal and receive signal instructions. COM ASIP V2 introduced two extra instructions to handle long signals and PDUs of arbitrary lengths. We extended the instruction set architecture (ISA) of our previous ASIPs by introducing six new instructions, in COM ASIP V3, to hash PDUs that contain these signals to authenticate transmission and reception of such PDUs. The experimental results show that COM ASIP V3 can handle (i.e., transmit, receive, calculate hash, or verify hash) a 64-byte controller area network flexible data-rate (CAN FD) frame in 1.575 μs and a 254-byte FlexRay frame in 6.301 μs. These measurements indicate that the throughput of our new COM ASIP is much higher, 42× to 75×, than the throughput required by these communication buses.

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Lightweight Diagnostic-based Secure Framework for Electronic Control Units in Vehicles

Cited by 5 publications

References 18 publications

Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework

Detecting Cyber Attacks In-Vehicle Diagnostics Using an Intelligent Multistage Framework

An Intelligent, Two-Stage, In-Vehicle Diagnostic-Based Secured Framework

Two-Layer Bus-Independent Instruction Set Architecture for Securing Long Protocol Data Units in Automotive Open System Architecture-Based Automotive Electronic Control Units

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