Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles
Abstract
:1. Introduction
- Strengthen interest in automated vehicles, demonstrating that security risks have been mitigated, the concept of cyber security has been validated and verified, and systems have been systematically tested.
- Protect motorists and manufacturers by ensuring that cybersecurity threats are handled following state-of-the-art standards and best practices.
- Develop safe and state-of-the-art AV technologies by ensuring that the automated guidance systems adopted are developed with security-by-design and defense-in-depth in mind.
- Gain a competitive advantage by collaborating with international experts who have up-to-date knowledge on information security, vulnerabilities, and applicable standards.
- Continuous vulnerability management: defining authorized channels for firmware and application updates that restrict the perimeter of attack.
- Security maintainability: if we want to refer, for example, to the cryptographic protection of data, it is unlikely that the keys and algorithms adopted in the initial phase will guarantee the same level of protection over time. For this reason, Security-by-design must be associated with a modular development approach that allows the creation of products capable of adapting to emerging threats.
- Cybersecurity evolution: from this point of view, it is useful to refer to the experience gained by the aeronautical industry, where the use of partitioned embedded systems and domain segregation have made it possible to achieve particularly high security standards.
- The definition of a chain-of-trust, from the prototyping of the individual components of a vehicle, and the system that drives it, to the cloud infrastructure used for data exchange and communications. Solutions based on distributed technologies and blockchain can provide a fundamental contribution in the certification of the phases that participate in the production chain and in the dynamics of the supply chain.
- The implementation of interfaces dedicated to the sector that refer to specialized security policies. The need to develop such countermeasures is accentuated by the frequent use of technologies borrowed from other sectors, such as OTA and bluetooth connections.
2. Related Works
3. Backgrounds on Cybersecurity in IoT, Bayesian Networks and Information Security in Automotive
3.1. Cybersecurity in IoT
- Theft or damage of device: (perception layer) physical damage to the device.
- Side-channel attacks: (perception layer) collect information on the running time, power consumption, electromagnetic radiation, or sounds produced by a device during the execution of a particular task to deduce information contained in the device memory;
- Fake Node attacks: (perception layer) inserting into the network nodes created by the attacker in order to transmit bogus information or consume the resources, in terms of energy, of the legitimate nodes;
- Replay attacks: (perception layer) after having intercepted authentic credentials of a node; an attacker then sends them back to the recipient simulating the identity of the issuer;
- Node Tampering attacks: (perception layer) replace part of the node hardware or firmware with components created by the attacker and equipped with malicious functions;
- Jamming attacks: (perception layer) consists, if the nodes communicate via wireless protocols, of disturbing the frequencies used by the protocol;
- Denial-of-Service (DoS): (network layer) its purpose is to prevent reaching the nodes via the network. To achieve this goal, it is possible to use many techniques, such as sending a large number of bogus packets on the network to make sure that the various nodes have more information in input than they can process (flooding), compromising a node in the network in order to modify its topology and degrade its performance (sinkhole attack);
- Man-in-the-middle: (network layer) consists of intercepting the data transmitted by the various nodes before they arrive at the recipient to steal them or retransmit a modified version;
- Storage attacks: (network layer) consist of modifying user information in the device memory or in the cloud;
- Routing attacks: (network layer) a class of attacks (the sinkhole attack is an example) in which an attacker tries to alter the information that the devices use to route packets to create loops, to send error messages, or to lose packets;
- Cross-Site-Scripting (XSS): (application layer), which uses client-side scripting languages (for example, JavaScript) to execute malicious code through a browser that shows a specific web page. This type of attack is also exploited in the IoT field because embedded devices often use web interfaces for configuration, more particularly in this case, we speak of Cross-Channel-Scripting (XCS);
- Malicious Code: (application layer) inject malicious code (malware) into the application for it to execute it;
- Credential theft: (application layer) in order to impersonate legitimate users. This layer can be accomplished through eavesdropping, man-in-the-middle attacks, brute force or dictionary attacks (to try to guess credentials), etc.
3.2. Bayesian Network
3.3. Information Security in Automotive
- Physical access points: allow direct or indirect physical access to the car’s internal network (USB, OBD, etc.).
- Short range access points: allow communication with the vehicle at a distance that generally varies from 5 to 300 m. Interfaces such as Wi-Fi, bluetooth, remote keyless entry (RKE), tire pressure monitoring system (TPMS), etc., are part of this class.
- Long range access points: allow communication with the vehicle at a distance greater than 1 km. Its groups interfaces include cellular networks (4G, 5G), global positioning system (GPS), etc.
3.3.1. Encryption, Access Control and Authentication Systems
3.3.2. Intrusion Detection System
- Signature detection based in which the collected data are compared with traces of already known attacks looking for a correspondence that confirms the fact that there is an attack in progress;
- Anomaly detection based, by which the system’s behavior is monitored by checking that its methods of use do not deviate from the regular use.
4. The Proposed Approach and Methodology
4.1. Case of Study
4.2. Two-Steps Algorithm
- The first step, called pre-processing, analyzes ten state frames (containing each frame the exact values of each car parameter considered for our case study, tab). Moreover, it verifies through spatial and temporal analysis obtained from an analysis of the problem whether it may or may not be a possible attack in that sequence of values. Each status frame is recorded with a unique timestamp, and its recording takes place every 4 ms.
- In the second step, through the use of a Bayesian network, previously trained through a pre-established data set during the simulation phase, it can decide whether we are in the presence or not of an attack, keeping in mind both the parameters that make up the frame values status, and the parameters obtained as information from these parameters.
- Steer: CAN message related to steering, 7 classes (−1:1 norm., step variable, very left, middle left, left, center, right, middle right, very right);
- Throttle: CAN message related to acceleration, 4 classes (0:1 norm., step variable, pedal not pressed, low, medium, high);
- Brake: CAN message related to braking, 4 classes (0:1 norm., pedal not pressed-low, medium, high);
- RPM: CAN message related to rotations per minute, 5 classes (0:1, step variable, stop, slow, normal, medium, high);
- Gear: CAN message related to gear of car, 5 classes (0, 1, 2, 3, 4, 5);
- Radiator: State of ignition of the cooling system, 2 classes (on, off);
- Lidar: Presence or absence of obstacles, 2 classes (0, 1);
- Lines: Crossing a road line or not, 2 classes (0, 1);
- Speedometer: Speed in absolute value, 6 classes (0:1 norm., stop, very slowly, slowly, medium, fast, very fast);
- Acceleration: Car acceleration, 5 classes (−1:1 norm., step variable, deceleration high, deceleration low, no acceleration, acceleration low, acceleration high);
- Speed: Car current speed, 6 classes (0:1 norm., step variable, stop [0 km/h], very slowly [0–30 km/h], slowly [30–50 km/h], medium [50–90 km/h], fast [90–130 km/h], very fast [130–150 km/h]);
- Engine Temperature: Car engine temperature, 4 classes (0:150, step variable, normal operation, low overheating, medium overheating, high overheating);
- Swerve: Car swerve, 7 classes (−1:1 norm., step 0.285, very left [−60° to −45°], middle left [−45° to −30°], left [−30° to −5°], center [−5° to 5°], right [5° to 30°], middle right [30° to 45°], very right [45° to 60°]);
- Obstacle: Presence or absence of generic obstacle within a radius of 20 m, 2 classes (true, false);
- Attack: Presence or absence of attack, 2 classes (true, false).
5. Experimental Results
- DoS attack: injecting messages of ‘0 × 000’ CAN ID in a short cycle.
- Fuzzy attack: injecting messages of spoofed random CAN ID and DATA values.
- Impersonation attack: injecting messages of Impersonating node, arbitration ID = ‘0 × 164’.
- Attack Free State: normal CAN messages.
- True Positives (TP): attack present and correct classification;
- True Negatives (TN): attack not present and correct classification;
- False Positives (FP): attack not present and incorrect classification;
- False Negatives (FN): attack present and incorrect classification.
6. Conclusions
7. Patents
Author Contributions
Funding
Conflicts of Interest
References
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Detected Attack | YES | NOT |
---|---|---|
YES | True Positives (TP) | False Negatives (FN) |
NOT | False Positives (FP) | True Negatives (TN) |
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Pascale, F.; Adinolfi, E.A.; Coppola, S.; Santonicola, E. Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles. Electronics 2021, 10, 1765. https://doi.org/10.3390/electronics10151765
Pascale F, Adinolfi EA, Coppola S, Santonicola E. Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles. Electronics. 2021; 10(15):1765. https://doi.org/10.3390/electronics10151765
Chicago/Turabian StylePascale, Francesco, Ennio Andrea Adinolfi, Simone Coppola, and Emanuele Santonicola. 2021. "Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles" Electronics 10, no. 15: 1765. https://doi.org/10.3390/electronics10151765
APA StylePascale, F., Adinolfi, E. A., Coppola, S., & Santonicola, E. (2021). Cybersecurity in Automotive: An Intrusion Detection System in Connected Vehicles. Electronics, 10(15), 1765. https://doi.org/10.3390/electronics10151765