Biblio

The Controller area network (CAN) is the most extensively used in-vehicle network. It is set to enable communication between a number of electronic control units (ECU) that are widely found in most modern vehicles. CAN is the de facto in-vehicle network standard due to its error avoidance techniques and similar features, but it is vulnerable to various attacks. In this research, we propose a CAN bus intrusion detection system (IDS) based on convolutional neural networks (CNN). U-CAN is a segmentation model that is trained by monitoring CAN traffic data that are preprocessed using hamming distance and saliency detection algorithm. The model is trained and tested using publicly available datasets of raw and reverse-engineered CAN frames. With an F\_1 Score of 0.997, U-CAN can detect DoS, Fuzzy, spoofing gear, and spoofing RPM attacks of the publicly available raw CAN frames. The model trained on reverse-engineered CAN signals that contain plateau attacks also results in a true positive rate and false-positive rate of 0.971 and 0.998, respectively.

Account data of electrical power system is the link of all businesses in the whole life cycle of equipment. It is of great significance to improve the data quality of power equipment account data for improving the information level of power enterprises. In the past, there was only the error correction technology to check whether it was empty and whether it contained garbled code. The error correction technology for same field of the same kind of power equipment account data is proposed in this paper. Combined with the characteristics of production business, the possible similar power equipment can be found through the function location type and other fields of power equipment account data. Based on the principle of search scoring, the horizontal comparison is used to search and score in turn. Finally, the potential spare parts and existing data quality are identified according to the scores. And judge whether it is necessary to carry out inspection maintenance.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.

In this paper we use car games as a simulator for real automobiles, and generate driving logs that contain the vehicle data. This includes values for parameters like gear used, speed, left turns taken, right turns taken, accelerator, braking and so on. From these parameters we have derived some more additional parameters and analyzed them. As the input from automobile driver is only routine driving, no explicit feedback is required; hence there are more chances of being able to accurately profile the driver. Experimentation and analysis from this logged data shows possibility that driver profiling can be done from vehicle data. Since the profiles are unique, these can be further used for a wide range of applications and can successfully exhibit typical driving characteristics of each user.

This paper addresses a robust methodology for developing a statistically sound, robust prognostic condition index and encapsulating this index as a series of highly accurate, transparent, human-readable rules. These rules can be used to further understand degradation phenomena and also provide transparency and trust for any underlying prognostic technique employed. A case study is presented on a wind turbine gearbox, utilising historical supervisory control and data acquisition (SCADA) data in conjunction with a physics of failure model. Training is performed without failure data, with the technique accurately identifying gearbox degradation and providing prognostic signatures up to 5 months before catastrophic failure occurred. A robust derivation of the Mahalanobis distance is employed to perform outlier analysis in the bivariate domain, enabling the rapid labelling of historical SCADA data on independent wind turbines. Following this, the RIPPER rule learner was utilised to extract transparent, human-readable rules from the labelled data. A mean classification accuracy of 95.98% of the autonomously derived condition was achieved on three independent test sets, with a mean kappa statistic of 93.96% reported. In total, 12 rules were extracted, with an independent domain expert providing critical analysis, two thirds of the rules were deemed to be intuitive in modelling fundamental degradation behaviour of the wind turbine gearbox.

In this paper, we propose an adaptive specification-based intrusion detection system (IDS) for detecting malicious unmanned air vehicles (UAVs) in an airborne system in which continuity of operation is of the utmost importance. An IDS audits UAVs in a distributed system to determine if the UAVs are functioning normally or are operating under malicious attacks. We investigate the impact of reckless, random, and opportunistic attacker behaviors (modes which many historical cyber attacks have used) on the effectiveness of our behavior rule-based UAV IDS (BRUIDS) which bases its audit on behavior rules to quickly assess the survivability of the UAV facing malicious attacks. Through a comparative analysis with the multiagent system/ant-colony clustering model, we demonstrate a high detection accuracy of BRUIDS for compliant performance. By adjusting the detection strength, BRUIDS can effectively trade higher false positives for lower false negatives to cope with more sophisticated random and opportunistic attackers to support ultrasafe and secure UAV applications.