Automated and intelligent hacking detection system

Dissertação de mestrado integrado em Informatics EngineeringThe Controller Area Network (CAN) is the backbone of automotive networking, connecting many Electronic ControlUnits (ECUs) that control virtually every vehicle function from fuel injection to parking sensors. It possesses,however, no security functionality such as message encryption or authentication by default. Attackers can easily inject or modify packets in the network, causing vehicle malfunction and endangering the driver and passengers. There is an increasing number of ECUs in modern vehicles, primarily driven by the consumer’s expectation of more features and comfort in their vehicles as well as ever-stricter government regulations on efficiency and emissions. Combined with vehicle connectivity to the exterior via Bluetooth, Wi-Fi, or cellular, this raises the risk of attacks. Traditional networks, such as Internet Protocol (IP), typically have an Intrusion Detection System (IDS) analysing traffic and signalling when an attack occurs. The system here proposed is an adaptation of the traditional IDS into the CAN bus using a One Class Support Vector Machine (OCSVM) trained with live, attack-free traffic. The system is capable of reliably detecting a variety of attacks, both known and unknown, without needing to understand payload syntax, which is largely proprietary and vehicle/model dependent. This allows it to be installed in any vehicle in a plug-and-play fashion while maintaining a large degree of accuracy with very few false positives.A Controller Area Network (CAN) é a principal tecnologia de comunicação interna automóvel, ligando muitas Electronic Control Units (ECUs) que controlam virtualmente todas as funções do veículo desde injeção de combustível até aos sensores de estacionamento. No entanto, não possui por defeito funcionalidades de segurança como cifragem ou autenticação. É possível aos atacantes facilmente injetarem ou modificarem pacotes na rede causando estragos e colocando em perigo tanto o condutor como os passageiros. Existe um número cada vez maior de ECUs nos veículos modernos, impulsionado principalmente pelas expectativas do consumidores quanto ao aumento do conforto nos seus veículos, e pelos cada vez mais exigentes regulamentos de eficiência e emissões. Isto, associada à conexão ao exterior através de tecnologias como o Bluetooth, Wi-Fi, ou redes móveis, aumenta o risco de ataques. Redes tradicionais, como a rede Internet Protocol (IP), tipicamente possuem um Intrusion Detection Systems (IDSs) que analiza o tráfego e assinala a presença de um ataque. O sistema aqui proposto é uma adaptação do IDS tradicional à rede CAN utilizando uma One Class Support Vector Machine (OCSVM) treinada com tráfego real e livre de ataques. O sistema é capaz de detetar com fiabilidade uma variedade de ataques, tanto conhecidos como desconhecidos, sem a necessidade de entender a sintaxe do campo de dados das mensagens, que é maioritariamente proprietária. Isto permite ao sistema ser instalado em qualquer veículo num modo plug-and-play enquanto mantém um elevado nível de desempenho com muito poucos falsos positivos

An investigation into non-destructive testing strategies and in-situ surface finish improvement for direct metal printing with SS 17-4 PH : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand

Figure 1.1 is re-used under an Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licenceAdditive Manufacturing (AM) technologies have the potential to create complex geometric parts that can be used in high-end product industries, aerospace, automotive, medical etc. However, the surface finish, part-to-part reliability, and machine-to-machine reliability has made it difficult to qualify the process for load dependent structures. The improvement of surface finish on metal printed parts, is a widely sought solution by these high-end industries and non-destructively characterizing the mechanical aptitude of metal printed parts, would pave the way for quality assessment strategies used to certify additively manufactured parts. This thesis examines the capability of laser polishing and non-destructive testing technologies and methods to address these difficulties. This research study presents an investigation into quality management strategies for Direct Metal Printing (DMP) with powdered Stainless Steel 17-4 PH. The research aim is split into two key categories: to improve the surface finish of metal additive manufactured parts and to non-destructively characterize the impact of defects (metallurgical anomalies) on the mechanical properties of the printed part. To improve surface finish of a printed part, a novel methodology was tested to laser polish the Laser-Powder Bed Fusion (L-PBF) parts during print with the built-in laser. Numerous technologies for non-destructive testing techniques already exist, and in the duration of this doctoral study various technologies were explored. However, the final solution focuses on layer-wise capture with a versatile low-cost imaging system, retrofitted within the DMP machine, to capture each layer following the lasering process. In addition, the study also focuses on progressing the characterization of data (images), using a combination of image processing, 3D modelling and Finite-Element-Analysis to create a novel strategy for replicating the as-built specimen as a computer-aided design model and performing simulated fatigue failure analysis on the part. This thesis begins with a broadened justification of the research need for the solutions described, followed by a review of literature defining existing techniques and methods pertaining to the solutions, with validation of the research gap identified to provide novel contribution to the metal additive manufacturing space. This is followed by the methodologies developed, to firstly, control the laser parameters within the DMP and examine the influence of these parameters using surface profilometry, scanning electron microscopy and mechanical hardness testing. The control variables in this methodology combines laser parameters (laser power, scan speed and polishing iterations) and print orientation (polished surface angled at 0º, 20º, 40º, 60º, 80º and 90º degree increments from the laser), using several Taguchi designs of experiments and statistical analysis to characterize the experimental results. The second methodology describes the retrofitted imaging system, image processing techniques and analysis methods used to reconstruct the 3D model of a standard square shaped part and one with synthesized defects. The method explores various 2D to 3D extrusion-based techniques using a combination of code-based image processing (Python 3, OpenCV and MATLAB image processing toolbox) and ready-made software tools (Solidworks, InkTrace, ImageJ and more). Finally, the new research findings are presented, including the results of the laser polishing study demonstrating the successful improvement of surface finish. The discussion surrounding these results, highlights the most effective part orientation for laser polishing the outline of an AM part and the most effective laser parameter combination resulting in the most significant improvement to surface finish (roughness and profile height variation). Summarily, the best improvement in surface roughness was achieved with the <80 angled surface with the laser speed, laser power and polishing iterations set to 500mm/s, 30W, 3 respectively. The sample set total average measured a 16.7% decrease in Ra. NDT digital imaging, thermal imaging and acoustic technologies were considered for defect capture in metal AM parts. The solution presented is primarily focused on the expansion of research to process digital images of each part layer and examine strategies to move the research from a data capture stage to a data processing strategy with quantitative measurement (FEA analysis) of the printed part’s mechanical properties. In addition, the results discuss a method to create feedback to the DMP to selectively melt problematic areas, by re-creating the sliced part layers but removing the well-melted areas from the laser scanning pattern. The methods and technological solutions developed in this research study, have presented novel data to further research these methods in the pursuit of quality assurance for AM parts. The work done has paved the way for more the research opportunities and alternative methods to be explored that complement the methods detailed here. For example, using a combination of in-situ laser polishing, followed by post-processing the AM specimens in an acid-based chemical bath. Alternatively, further exploring acoustic NDT techniques to create an in-built acoustic-based imaging device within the AM machine. Finally, this thesis cross-examines the work done to answer the research questions established at the start of the thesis and verify the hypotheses stated in the methods chapter

NOVELTY DETECTION FOR PREDICTIVE MAINTENANCE

Since the advent of Industry 4. 0 significant research has been conducted to apply machine learning to the vast array of Internet of Things (IoT) data produced by Industrial Machines. One such topic is to Predictive Maintenance. Unlike some other machine learning domains such as NLP and computer vision, Predictive Maintenance is a relatively new area of focus. Most of the published work demonstrates the effectiveness of supervised classification for predictive maintenance. Some of the challenges highlighted in the literature are the cost and difficulty of obtaining labelled samples for training. Novelty detection is a branch of machine learning that after being trained on normal operations detects if new data comes from the same process or is different, eliminating the requirement to label data points. This thesis applies novelty detection to both a public data set and one that was specifically collected to demonstrate a its application to predictive maintenance. The Local Optimization Factor showed better performance than a One-Class SVM on the public data. It was then applied to data from a 3-D printer and was able to detect faults it had not been trained on showing a slight lift from a random classifier

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

Road Condition Estimation with Data Mining Methods using Vehicle Based Sensors

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation

Specialized IoT systems: Models, Structures, Algorithms, Hardware, Software Tools

Монография включает анализ проблем, модели, алгоритмы и программно- аппаратные средства специализированных сетей интернета вещей. Рассмотрены результаты проектирования и моделирования сети интернета вещей, мониторинга качества продукции, анализа звуковой информации окружающей среды, а также технология выявления заболеваний легких на базе нейронных сетей. Монография предназначена для специалистов в области инфокоммуникаций, может быть полезна студентам соответствующих специальностей, слушателям факультетов повышения квалификации, магистрантам и аспирантам

Eco-friendly Naturalistic Vehicular Sensing and Driving Behaviour Profiling

PhD ThesisInternet of Things (IoT) technologies are spurring of serious games that support training directly in the field. This PhD implements field user performance evaluators usable in reality-enhanced serious games (RESGs) for promoting fuel-efficient driving. This work proposes two modules – that have been implemented by processing information related to fuel-efficient driving – to be employed as real-time virtual sensors in RESGS. The first module estimates and assesses instantly fuel consumption, where I compared the performance of three configured machine learning algorithms, support vector regression, random forest and artificial neural networks. The experiments show that the algorithms have similar performance and random forest slightly outperforms the others. The second module provides instant recommendations using fuzzy logic when inefficient driving patterns are detected. For the game design, I resorted to the on-board diagnostics II standard interface to diagnostic circulating information on vehicular buses for a wide diffusion of a game, avoiding sticking to manufacturer proprietary solutions. The approach has been implemented and tested with data from the enviroCar server site. The data is not calibrated for a specific car model and is recorded in different driving environments, which made the work challenging and robust for real-world conditions. The proposed approach to virtual sensor design is general and thus applicable to various application domains other than fuel-efficient driving. An important word of caution concerns users’ privacy, as the modules rely on sensitive data, and provide information that by no means should be misused