Semi-supervised multi-layered clustering model for intrusion detection

A Machine Learning (ML) -based Intrusion Detection and Prevention System (IDPS) requires a large amount of labeled up-to-date training data, to effectively detect intrusions and generalize well to novel attacks. However, labeling of data is costly and becomes infeasible when dealing with big data, such as those generated by IoT (Internet of Things) -based applications. To this effect, building a ML model that learns from non- or partially-labeled data is of critical importance. This paper proposes a novel Semi-supervised Multi-Layered Clustering Model (SMLC) for network intrusion detection and prevention tasks. The SMLC has the capability to learn from partially labeled data while achieving a comparable detection performance to supervised ML-based IDPS. The performance of the SMLC is compared with well-known supervised ensemble ML models, namely, RandomForest, Bagging, and AdaboostM1 and a semi-supervised model (i.e., tri-training) on a benchmark network intrusion dataset, the Kyoto 2006+. Experimental results show that the SMLC outperforms all other models and can achieve better detection accuracy using only 20% labeled instances of the training data

Deep Learning-based Vehicle Behaviour Prediction For Autonomous Driving Applications: A Review

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behaviour prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their superior performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behaviour prediction in this paper. We firstly give an overview of the generic problem of vehicle behaviour prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The paper also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions

Deep learning-based vehicle behaviour prediction for autonomous driving applications : a review

Behaviour prediction function of an autonomous vehicle predicts the future states of the nearby vehicles based on the current and past observations of the surrounding environment. This helps enhance their awareness of the imminent hazards. However, conventional behavior prediction solutions are applicable in simple driving scenarios that require short prediction horizons. Most recently, deep learning-based approaches have become popular due to their promising performance in more complex environments compared to the conventional approaches. Motivated by this increased popularity, we provide a comprehensive review of the state-of-the-art of deep learning-based approaches for vehicle behavior prediction in this article. We firstly give an overview of the generic problem of vehicle behavior prediction and discuss its challenges, followed by classification and review of the most recent deep learning-based solutions based on three criteria: input representation, output type, and prediction method. The article also discusses the performance of several well-known solutions, identifies the research gaps in the literature and outlines potential new research directions

Cooperative object classification for driving applications

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise

A survey on 3D object detection methods for autonomous driving applications

An autonomous vehicle (AV) requires an accurate perception of its surrounding environment to operate reliably. The perception system of an AV, which normally employs machine learning (e.g., deep learning), transforms sensory data into semantic information that enables autonomous driving. Object detection is a fundamental function of this perception system, which has been tackled by several works, most of them using 2D detection methods. However, the 2D methods do not provide depth information, which is required for driving tasks, such as path planning, collision avoidance, and so on. Alternatively, the 3D object detection methods introduce a third dimension that reveals more detailed object's size and location information. Nonetheless, the detection accuracy of such methods needs to be improved. To the best of our knowledge, this is the first survey on 3D object detection methods used for autonomous driving applications. This paper presents an overview of 3D object detection methods and prevalently used sensors and datasets in AVs. It then discusses and categorizes the recent works based on sensors modalities into monocular, point cloud-based, and fusion methods. We then summarize the results of the surveyed works and identify the research gaps and future research directions

A novel detection approach of unknown cyber-attacks for intra-vehicle networks using recurrence plots and neural networks

Proliferation of connected services in modern vehicles could make them vulnerable to a wide range of cyber-attacks through intra-vehicle networks that connect various vehicle systems. Designers usually equip vehicles with predesigned counter-measures, but these may not be effective against novel cyber-attacks. Intrusion Detection Systems (IDSs) serve as an additional layer of defence when conventional measures that are implemented by the designers fail. Several intrusion detection techniques have been proposed in the literature but these techniques have limited capability in detecting novel cyber-attacks. This paper proposes a new Machine Learning (ML)-based IDS for detecting novel cyber-attacks in intra-vehicle networks, specifically in Controller Area Networks (CANs). The proposed IDS generates high-level representations of CAN messages transmitted on the bus exploiting their temporal properties as well as the intra and inter message dependencies through the use of Recurrence Plot (RP), which are then fed into a bespoke Neural Network, designed and trained to detect novel intrusions. Evaluation of the performance of the proposed IDS in comparison with that of the state-of-the-art existing IDS schemes demonstrates the superiority of the proposed IDS

Data for Cooperative object classification for driving applications

3D object classification can be realised by rendering views of the same object from different angles and aggregating all the views to build a classifier. Although this approach has been previously proposed for general objects classification, most existing works did not consider visual impairments. In contrast, this paper considers the problem of 3D object classification for driving applications under impairments (e.g. occlusion and sensor noise) by generating an application-specific dataset. We present a cooperative object classification method where multiple images of the same object seen from different perspectives (agents) are exploited to generate more accurate classification. We consider model generalisation capability and its resilience to impairments. We introduce an occlusion model with higher resemblance to real-world occlusion and use a simplified sensor noise model. The experimental results show that the cooperative model, relying on multiple views, significantly outperforms single-view methods and is effective in mitigating the effects of occlusion and sensor noise

Design and development of industrial cyber-physical system Testbed

Cyber-physical systems (CPS) are integral components of Industry 4.0. However, there is a lack of benchmark systems for the design, development and testing of CPS. To this end, this article presents the design and development of an industrial CPS testbed using a stand of two coupled tanks, a Programmable Logic Controller (PLC), and an Internet-of-Things (IoT) gateway. The testbed can connect to cloud services, combining the industrial and management components together, where a cloud-based service can be used to manage the system. It also can be used to design, develop and evaluate fault diagnosis, fault-tolerant control, and cyber-security algorithms for CPS. Due to its versatility and reconfigurability, the proposed testbed can be used to test various scenarios of possible faults and cyber-attacks in industrial systems