A Kohonen SOM architecture for intrusion detection on in-vehicle communication networks

The diffusion of connected devices in modern vehicles involves a lack in security of the in-vehicle communication networks such as the controller area network (CAN) bus. The CAN bus protocol does not provide security systems to counter cyber and physical attacks. Thus, an intrusion-detection system to identify attacks and anomalies on the CAN bus is desirable. In the present work, we propose a distance-based intrusion-detection network aimed at identifying attack messages injected on a CAN bus using a Kohonen self-organizing map (SOM) network. It is a power classifier that can be trained both as supervised and unsupervised learning. SOM found broad application in security issues, but was never performed on in-vehicle communication networks. We performed two approaches, first using a supervised X-Y fused Kohonen network (XYF) and then combining the XYF network with a K-means clustering algorithm (XYF-K) in order to improve the efficiency of the network. The models were tested on an open source dataset concerning data messages sent on a CAN bus 2.0B and containing large traffic volume with a low number of features and more than 2000 different attack types, sent totally at random. Despite the complex structure of the CAN bus dataset, the proposed architectures showed a high performance in the accuracy of the detection of attack messages

AI-based intrusion detection systems for in-vehicle networks: a survey.

The Controller Area Network (CAN) is the most widely used in-vehicle communication protocol, which still lacks the implementation of suitable security mechanisms such as message authentication and encryption. This makes the CAN bus vulnerable to numerous cyber attacks. Various Intrusion Detection Systems (IDSs) have been developed to detect these attacks. However, the high generalization capabilities of Artificial Intelligence (AI) make AI-based IDS an excellent countermeasure against automotive cyber attacks. This article surveys AI-based in-vehicle IDS from 2016 to 2022 (August) with a novel taxonomy. It reviews the detection techniques, attack types, features, and benchmark datasets. Furthermore, the article discusses the security of AI models, necessary steps to develop AI-based IDSs in the CAN bus, identifies the limitations of existing proposals, and gives recommendations for future research directions

Intrusion detection for in-vehicle communication networks: An unsupervised kohonen SOM approach

The diffusion of embedded and portable communication devices on modern vehicles entails new security risks since in-vehicle communication protocols are still insecure and vulnerable to attacks. Increasing interest is being given to the implementation of automotive cybersecurity systems. In this work we propose an efficient and high-performing intrusion detection system based on an unsupervised Kohonen Self-Organizing Map (SOM) network, to identify attack messages sent on a Controller Area Network (CAN) bus. The SOM network found a wide range of applications in intrusion detection because of its features of high detection rate, short training time, and high versatility. We propose to extend the SOM network to intrusion detection on in-vehicle CAN buses. Many hybrid approaches were proposed to combine the SOM network with other clustering methods, such as the k-means algorithm, in order to improve the accuracy of the model. We introduced a novel distance-based procedure to integrate the SOM network with the K-means algorithm and compared it with the traditional procedure. The models were tested on a car hacking dataset concerning traffic data messages sent on a CAN bus, characterized by a large volume of traffic with a low number of features and highly imbalanced data distribution. The experimentation showed that the proposed method greatly improved detection accuracy over the traditional approach

Anomaly detection with machine learning for automotive cyber-physical systems

2022 Spring.Includes bibliographical references.Today's automotive systems are evolving at a rapid pace and there has been a seismic shift in automotive technology in the past few years. Automakers are racing to redefine the automobile as a fully autonomous and connected system. As a result, new technologies such as advanced driver assistance systems (ADAS), vehicle-to-vehicle (V2V), 5G vehicle to infrastructure (V2I), and vehicle to everything (V2X), etc. have emerged in recent years. These advances have resulted in increased responsibilities for the electronic control units (ECUs) in the vehicles, requiring a more sophisticated in-vehicle network to address the growing communication needs of ECUs with each other and external subsystems. This in turn has transformed modern vehicles into a complex distributed cyber-physical system. The ever-growing connectivity to external systems in such vehicles is introducing new challenges, related to the increasing vulnerability of such vehicles to various cyber-attacks. A malicious actor can use various access points in a vehicle, e.g., Bluetooth and USB ports, telematic systems, and OBD-II ports, to gain unauthorized access to the in-vehicle network. These access points are used to gain access to the network from the vehicle's attack surface. After gaining access to the in-vehicle network through an attack surface, a malicious actor can inject or alter messages on the network to try to take control of the vehicle. Traditional security mechanisms such as firewalls only detect simple attacks as they do not have the ability to detect more complex attacks. With the increasing complexity of vehicles, the attack surface increases, paving the way for more complex and novel attacks in the future. Thus, there is a need for an advanced attack detection solution that can actively monitor the in-vehicle network and detect complex cyber-attacks. One of the many approaches to achieve this is by using an intrusion detection system (IDS). Many state-of-the-art IDS employ machine learning algorithms to detect cyber-attacks for its ability to detect both previously observed as well as novel attack patterns. Moreover, the large availability of in-vehicle network data and increasing computational power of the ECUs to handle emerging complex automotive tasks facilitates the use of machine learning models. Therefore, due to its large spectrum of attack coverage and ability to detect complex attack patterns, we adopt and propose two novel machine learning based IDS frameworks (LATTE and TENET) for in-vehicle network anomaly detection. Our proposed LATTE framework uses sequence models, such as LSTMs, in an unsupervised setting to learn the normal system behavior. LATTE leverages the learned information at runtime to detect anomalies by observing for any deviations from the learned normal behavior. Our proposed LATTE framework aims to maximize the anomaly detection accuracy, precision, and recall while minimizing the false-positive rate. The increased complexity of automotive systems has resulted in very long term dependencies between messages which cannot be effectively captured by LSTMs. Hence to overcome this problem, we proposed a novel IDS framework called TENET. TENET employs a novel convolutional neural attention (TCNA) based architecture to effectively learn very-long term dependencies between messages in an in-vehicle network during the training phase and leverage the learned information in combination with a decision tree classifier to detect anomalous messages. Our work aims to efficiently detect a multitude of attacks in the in-vehicle network with low memory and computational overhead on the ECU

A distributed anomaly detection system for in-vehicle network using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall

A distributed anomaly detection system for in-vehicle network using HTM

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall