Data Mining Practical Machine Learning Tools and Techniques

In this paper, we present a survey of deep learning approaches for cybersecurity intrusion detection, the datasets used, and a comparative study. Specifically, we provide a review of intrusion detection systems based on deep learning approaches. The dataset plays an important role in intrusion detection, therefore we describe 35 well-known cyber datasets and provide a classification of these datasets into seven categories; namely, network traffic-based dataset, electrical network-based dataset, internet traffic-based dataset, virtual private network-based dataset, android apps-based dataset, IoT traffic-based dataset, and internet-connected devices-based dataset. We analyze seven deep learning models including recurrent neural networks, deep neural networks, restricted Boltzmann machines, deep belief networks, convolutional neural networks, deep Boltzmann machines, and deep autoencoders. For each model, we study the performance in two categories of classification (binary and multiclass) under two new real traffic datasets, namely, the CSE-CIC-IDS2018 dataset and the Bot-IoT dataset. In addition, we use the most important performance indicators, namely, accuracy, false alarm rate, and detection rate for evaluating the efficiency of several methods.

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Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study

Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalising previously learned rules to new scenarios. For these reasons, the use of deep learning for vehicle control is becoming increasingly popular. Although important advancements have been achieved in this field, these works have not been fully summarised. This paper surveys a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods. Although there exists overlap between control and perception, the focus of this paper is on vehicle control, rather than the wider perception problem which includes tasks such as semantic segmentation and object detection. The paper identifies the strengths and limitations of available deep learning methods through comparative analysis and discusses the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety. Overall, this survey brings timely and topical information to a rapidly evolving field relevant to intelligent transportation systems.

A Survey of Deep Learning Applications to Autonomous Vehicle Control

We propose a neural network architecture for detecting intrusions on the controller area network (CAN). The latter is the standard communication method between the electronic control units (ECUs) of automobiles. However, CAN lacks security mechanisms and it has recently been shown that it can be attacked remotely. Hence, it is desirable to monitor CAN traffic to detect intrusions. In order to find both, known and unknown intrusion scenarios, we consider a novel unsupervised learning approach which we call CANet. To our knowledge, this is the first deep learning based intrusion detection system (IDS) that naturally handles the data structure of the high dimensional CAN bus, where different message types are sent at different times. Our method is evaluated on real and synthetic CAN data. A comparison with previous machine learning based methods shows that CANet outperforms them by a significant margin. For reproducibility of the method, our synthetic data is publicly available.

CANet: An Unsupervised Intrusion Detection System for High Dimensional CAN Bus Data

Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.

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Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC

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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David Oxtoby

Papers

Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC

Intrusion Detection Systems for Intra-Vehicle Networks: A Review