Advanced Analytics for Connected Car Cybersecurity

Levi, Matan; Allouche, Yair; Kontorovich, Aryeh

doi:10.1109/vtcspring.2018.8417690

Cited by 49 publications

(25 citation statements)

References 17 publications

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“…Regarding time-series anomaly detection on automotive networks, Narayanan et al [45], and Levi et al [46], propose an ADS by training a Hidden Markov model (HMM) to learn the vehicle's normal behavior and classify anomalies. The intuition behind this approach is that a vehicle's behavior is considered as a sequence of finite events that are dependent on the previous state, similar to a Markov process.…”

Section: Related Workmentioning

confidence: 99%

CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

Longari

Valcarcel

Zago

et al. 2021

IEEE Trans. Netw. Serv. Manage.

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Automotive security has gained significant traction in the last decade thanks to the development of new connectivity features that have brought the vehicle from an isolated environment to an externally facing domain. Researchers have shown that modern vehicles are vulnerable to multiple types of attacks leveraging remote, direct and indirect physical access, which allow attackers to gain control and affect safety-critical systems. Conversely, Intrusion Detection Systems (IDSs) have been proposed by both industry and academia to identify attacks and anomalous behaviours. In this paper, we propose CANnolo, an IDS based on Long Short-Term Memory (LSTM)-autoencoders to identify anomalies in Controller Area Networks (CANs). During a training phase, CANnolo automatically analyzes the CAN streams and builds a model of the legitimate data sequences. Then, it detects anomalies by computing the difference between the reconstructed and the respective real sequences. We experimentally evaluated CANnolo on a set of simulated attacks applied over a real-world dataset. We show that our approach outperforms the state-of-the-art model by improving the detection rate and precision.

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Section: Related Workmentioning

confidence: 99%

CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

Longari

Valcarcel

Zago

et al. 2021

IEEE Trans. Netw. Serv. Manage.

View full text Add to dashboard Cite

show abstract

“…Levi et al [92] propose a security system that is located in a backend and uses a Hidden Markov Model (HMM), which is a machine learning approach to predict event sequences (see Figure 7). Instead of transmitting raw vehicle data (e.g.…”

Section: ) ''Advanced Analytics For Connected Cars Cyber Security'' [92]mentioning

confidence: 99%

Context-Aware Security for Vehicles and Fleets: A Survey

2021

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Vehicles are becoming increasingly intelligent and connected. Interfaces for communication with the vehicle, such as WiFi and 5G, enable seamless integration into the user's life, but also cyber attacks on the vehicle. Therefore, research is working on in-vehicle countermeasures such as authentication, access controls, or intrusion detection. Recently, legal regulations have also become effective that require automobile manufacturers to set up a monitoring system for fleet-wide security analysis. The growing amount of software, networking, and the automation of driving create new challenges for security. Context-awareness, situational understanding, adaptive security, and threat intelligence are necessary to cope with these everincreasing risks. In-vehicle security should be adaptive to secure the car in an infinite number of (driving) situations. For fleet-wide analysis and alert triage, knowledge and understanding of the circumstances are required. Context-awareness, nonetheless, has been sparsely considered in the field of vehicle security. This work aims to be a precursor to context-aware, adaptive and intelligent security for vehicles and fleets. To this end, we provide a comprehensive literature review that analyzes the vehicular as well as related domains. Our survey is mainly characterized by the detailed analysis of the context information that is relevant for vehicle security in the future.

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“…Moreover, this approach proves to be ineffective for a small subset of IDs whose entropy exhibits large variations even in normal conditions. Levi et al [6] proposed a new temporal based detection technique using Hidden Markov Model (HMM) and regression model for vehicle fleet. Important data are collected and then tested against the HMM trained on vehicles' normal behavior.…”

Section: ) Flow-based Idssmentioning

confidence: 99%

“…We categorize the datasets used in the works reviewed in this paper into: real data that are extracted from test vehicles, simulated data refer to the data generated by simulation or prototyping, and data that are not comprehensively described in the source work. As can be seen in Figure 6, 21 works out of 42 works reviewed in this, such as [18], [31], [34], [38], and [50], have used real data, whereas 11 works, such as [5], [6], and [42], have used simulated data, and 10 works did not provide comprehensive description of the used data.…”

Section: Datasetsmentioning

confidence: 99%

“…Protecting vehicles against cyberattacks is a challenging task since historically vehicles have been designed without comprehensive security requirements in mind, having relied on the assumption that vehicles operate independently with no communication capabilities. In this respect, conventional proactive security countermeasures (such as encryption algorithms, access control and the like) might not be applicable to modern vehicles due to the high connectivity (giving a large attack surface), time-sensitivity, resource constraints, and complexity of modern vehicles [5], [6]. Recently, a considerable attention has been paid to reactive systems, such as Intrusion Detection Systems (IDSs), as complementary solutions to proactive security countermeasures, since they can detect potential cyberattacks in intra-vehicle networks as well as misbehaviors in connected vehicular networks [7].…”

Section: Introductionmentioning

confidence: 99%

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Intrusion Detection Systems for Intra-Vehicle Networks: A Review

et al. 2019

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A modern vehicle is a complex system of sensors, electronic control units, and actuators connected through different types of intra-vehicle networks to control and monitor the state of the vehicle. In addition, modern vehicles are becoming increasingly connected to the outside world through V2X technologies. However, these provide new attack surfaces that increase the cybersecurity risk to modern vehicles. To this end, there are two distinct and key challenges that need to be addressed to ensure safety and consumer trust. While modern vehicles must be equipped with the best countermeasures against cybersecurity threats, a reliable mechanism shall be also in place to detect the potential intrusions of the system while in operation, which is termed as intrusion detection. This paper provides a structured and comprehensive review of the state of the art of the intra-vehicle intrusion detection systems (IDSs) for passenger vehicles. We first provide an overview of intra-vehicle networks before reviewing contemporary research in intra-vehicle IDSs. The approach employed is to categorize the reviewed works based on their detection technique and to examine the used feature and feature selection methods, evaluation dataset, attack type, performance metrics, and benchmark models. This paper also presents outstanding research challenges and gaps in intra-vehicle IDS research.

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Advanced Analytics for Connected Car Cybersecurity

Cited by 49 publications

References 17 publications

CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

Context-Aware Security for Vehicles and Fleets: A Survey

Intrusion Detection Systems for Intra-Vehicle Networks: A Review

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