Techniques for utilizing classification towards securing automotive controller area network and machine learning towards the reverse engineering of CAN messages

The vehicle industry is quickly becoming more connected and growing. This growth is due to advancements in cyber physical systems (CPSs) that enhance the safety and automation in vehicle. The modern automobile consists of more than 70 electronic control units (ECUs) that communicate and interact with each other over automotive bus systems. Passenger comforts, infotainment features, and connectivity continue to progress through the growth and integration of Internet-of-Things (IoT) technologies. Common networks include the Controller Area Network (CAN), Local Interconnect Network (LIN), and FlexRay. However, the benefits of increased connectivity and features comes with the penalty of increased vulnerabilities. Security is lacking in preventing attacks on safety-critical control systems. I will explore the state of the art methods and approaches researchers have taken to identify threats and how to address them with intrusion detection. I discuss the development of a hybrid based intrusion detection approach that combines anomaly and signature based detection methods. Machine learning is a hot topic in security as it is a method of learning and classifying system behavior and can detect intrusions that alter normal behavior. In this paper, we discuss utilizing machine learning algorithms to assist in classifying CAN messages. I present work that focuses on the reverse engineering and classification of CAN messages. The problem is that even though CAN is standardized, the implementation may vary for different manufacturers and vehicle models. These implementations are kept secret, therefore CAN messages for every vehicle needs to be analyzed and reverse engineered in order to get information. Due to the lack of publicly available CAN specifications, attackers and researchers need to reverse engineer messages to pinpoint which messages will have the desired impact. The reverse engineering process is needed by researchers and hackers for all manufacturers and their respective vehicles to understand what the vehicle is doing and what each CAN message means. The knowledge of the specifications of CAN messages can improve the effectiveness of security mechanisms applied to CAN

Analysis and optimisation through mathematical modelling: Muresk farm photovoltaic reverse osmosis water treatment plant

Photovoltaic reverse osmosis water treatment units can be deployed into remote regions to provide remote communities with a clean water source without the need for on site electricity supply to operate. Optimisation of these units has the potential to maximise the output of purified water and to improve the overall effectiveness of the PVRO unit once it has been deployed. The aim of this project is to develop a mathematical model for the optimisation of the Muresk PVRO unit. This is achieved using a local monitoring system that can log the operational data of the PVRO unit and utilising this data to validate and tune a Microsoft Excel based mathematical model of the Muresk PVRO unit. In this project an ESP32 microcontroller running an Arduino program was used to log the electrical and water flow data from the PVRO unit to a ThingSpeak IOT portal and a local SD card. A mathematical model of the Muresk PVRO system was developed, and two months of data were compared with the data from the monitoring unit to tune and validate the model. With the model tuned the mathematical model was used to investigate optimising the PVRO output by adjusting the tilt angle of the solar array. By increasing the array tilt from 30 degrees to 45-degrees the daily minimum output improved by 9% with a marginal loss of 1% to the annual water output. This increases the suitability of the unit to applications where a consistent output of clean water is more desired than just maximising the annual output

MEMS graphene strain sensor

Graphene is a two dimensional honeycomb structure of sp^2 hybridized carbon atoms that has possibilities in many applications due to its excellent mechanical and electrical properties. One application for Graphene is in the field of sensors. Graphene’s electronic properties do not degrade when it undergoes mechanical strain which is advantageous for strain sensors. In this thesis, certain properties, such as the piezo-resistivity and flexibility, of graphene will be explored to show how they can be utilized to make a strain sensing device. Our original fabrication process of patterning graphene and the transfer process of graphene onto a flexible substrate will be discussed. The development of a stretchable and flexible graphene based rosette strain sensor will also be detailed. Developing a novel, reliable patterning process for the graphene is the first step to manufacture a stretchable graphene based sensor. The graphene was patterned using a photolithography and etching process that was developed by our research team, then it was transferred to a flexible polymer substrate with the use of a combination of soft lithography and wet etching of the Ni foil with ferric chloride solution. Graphene patterning is an essential step in fabricating reliable and sensitive sensors. With this process, graphene can be consistently patterned into different shapes and sizes. To utilize the graphene as the sensing material it also needs to be transferred onto a flexible substrate. The innovative transfer process developed by our research team consistently adheres graphene to a flexible PDMS substrate while removing the original nickel substrate. In the end, the graphene was transferred from the metal substrate to the desired flexible substrate. This process was repeated multiple times to create a stack and multilayer device. While many graphene-based strain sensors have been developed, they are uni-directional and can only measure the strain applied on the sensor in a principle direction. This issue was solved in this thesis by arranging the graphene sensors in a rosette pattern which enabled for multi-directional strain detection. The strain sensor was further improved by stacking the graphene sensors in a rosette pattern; which was possible by leveraging the advantages of soft lithography and bonding processes and the flexibility of graphene. Our final device was a stacked rosette graphene strain sensor that was able to successfully measure strain in multiple directions and magnitudes simultaneously

Use of novel serum markers in clinical follow-up of Sertoli-Leydig cell turnours

Background: Sertoli-Leyclig cell tumours of the ovary account for only 0.2% of malignant ovarian tumours. Two-thirds of all patients become apparent due to the tumour's hormone production. Methods: A 41-year-old patient (gravida 4, para 4) presented with dyspnoea, enlarged abdominal girth and melaena. Diagnostic imaging was suspicious for an ovarian cancer. The standard tumour marker for ovarian cancer (CA 125) was elevated to 984 U/mL. Results: Surgical exploration of the abdomen revealed a mouldering tumour of both adnexes extending to the level of the navel. Frozen sections showed an undifferentiated carcinoma of unknown origin. Radical surgery was performed. The final histological report described a malignant sex-cord stroma tumour, a Sertoli-Leydig cell tumour, emanating from both ovaries. Analysis of preoperative blood serum showed elevated levels of CYFRA 21-1 (10.4 ng/mL), neuron-specific enolase (36.2 ng/mL), oestradiol (485 pg/mL) and CA-125 (984 U/mL). Adjuvant chemotherapy and regional hyperthermia were performed due to the malignant potential and incomplete resection of the tumour. Conclusions: Undifferentiated Sertoli-Leyclig cell tumours show a poor clinical course. As only two-thirds of patients with this rare disease present with elevated hormone levels, new markers deserve further investigation to offer more specific, individualised tumour monitoring

Automotive Intrusion Detection Based on Constant CAN Message Frequencies Across Vehicle Driving Modes

The modern automobile relies on numerous electronic control units communicating over the de facto standard of the controller area network (CAN) bus. This communication network was not developed with cybersecurity in mind. Many methods based on constant time intervals between messages have been proposed to address this lack of security issue with the CAN bus. However, these existing methods may struggle to handle variable time intervals between messages during transitions of vehicle driving modes. This paper proposes a simple and cost-effective method to ensure the security of the CAN bus that is based on constant message frequencies across vehicle driving modes. This proposed method does not require any modifications on the existing CAN bus and it is designed with the intent for efficient execution in platforms with very limited computational resources. Test results with the proposed method against two different vehicles and a frequency domain analysis are also presented in the paper

Survey of Automotive Controller Area Network Intrusion Detection Systems

Novel attacks continue to appear against in-vehicle networks due to the increasing complexity of heterogeneous software and hardware components used in vehicles. These new components introduce challenges when developing efficient and adaptable security mechanisms. Several intrusion detection systems (IDS) have been proposed to identify and protect in-vehicle networks against malicious activities. We describe the state-of-the-art intrusion detection methods for securing automotive networks, with special focus on the Controller Area Network (CAN). We provide a description of vulnerabilities, highlight threat models, identify known attack vectors present in CAN, and discuss the advantages and disadvantages of suggested solutions

Ketamine for Refractory Headache: A Retrospective Analysis.

BACKGROUND AND OBJECTIVES: The burden of chronic headache disorders in the United States is substantial. Some patients are treatment refractory. Ketamine, an N-methyl-D-aspartate antagonist, provides potent analgesia in subanesthetic doses in chronic pain, and limited data suggest it may alleviate headache in some patients. METHODS: We performed a retrospective study of 61 patients admitted over 3 years for 5 days of intravenous therapy that included continuous ketamine to determine responder rate and patient and ketamine infusion characteristics. Pain ratings at 2 follow-up visits were recorded. An immediate responder was a patient with decrease of 2 points or greater in the numerical rating scale (0-10) from start to final pain in the hospital. Sustained response at office visits 1 and 2 was determined based on maintaining the 2-point improvement at those visits. Patients were assessed daily for pain and adverse events (AEs). RESULTS: Forty-eight (77%) of the 61 patients were immediate responders. There were no differences regarding demographics, opioid use, or fibromyalgia between immediate responders and nonresponders. Maximum improvement occurred 4.56 days (mean) into treatment. Sustained response occurred in 40% of patients at visit 1 (mean, 38.1 days) and 39% of patients at visit 2 (mean, 101.3 days). The mean maximum ketamine rate was 65.2 ± 2.8 mg/h (0.76 mg/kg per hour). Ketamine rates did not differ between groups. Adverse events occurred equally in responders and nonresponders and were mild. CONCLUSIONS: Ketamine was associated with short-term analgesia in many refractory headache patients with tolerable adverse events. A prospective study is warranted to confirm this and elucidate responder characteristics

Towards Reverse Engineering Controller Area Network Messages Using Machine Learning

The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle

Anomaly Detection Approach Using Adaptive Cumulative Sum Algorithm for Controller Area Network

The modern vehicle has transformed from a purely mechanical system to a system that embeds several electronic devices. These devices communicate through the in-vehicle network for enhanced safety and comfort but are vulnerable to cyber-physical risks and attacks. A well-known technique of detecting these attacks and unusual events is by using intrusion detection systems. Anomalies in the network occur at unknown points and produce abrupt changes in the statistical features of the message stream. In this paper, we propose an anomaly-based intrusion detection approach using the cumulative sum (CUSUM) change-point detection algorithm to detect data injection attacks on the controller area network (CAN) bus. We leverage the parameters required for the change-point algorithm to reduce false alarm rate and detection delay. Using real dataset generated from a car in normal operation, we evaluate our detection approach on three different kinds of attack scenarios