Unsupervised Time Series Extraction from Controller Area Network Payloads

This paper introduces a method for unsupervised tokenization of Controller Area Network (CAN) data payloads using bit level transition analysis and a greedy grouping strategy. The primary goal of this proposal is to extract individual time series which have been concatenated together before transmission onto a vehicle's CAN bus. This process is necessary because the documentation for how to properly extract data from a network may not always be available; passenger vehicle CAN configurations are protected as trade secrets. At least one major manufacturer has also been found to deliberately misconfigure their documented extraction methods. Thus, this proposal serves as a critical enabler for robust third-party security auditing and intrusion detection systems which do not rely on manufacturers sharing confidential information.Comment: 2018 IEEE 88th Vehicular Technology Conference (VTC2018-Fall

Controller Area Network

Controller Area Network (CAN) is a popular and very well-known bus system, both in academia and in industry. CAN protocol was introduced in the mid eighties by Robert Bosch GmbH [7] and it was internationally standardized in 1993 as ISO 11898-1 [24]. It was initially designed to distributed automotive control systems, as a single digital bus to replace traditional point-to-point cables that were growing in complexity, weight and cost with the introduction of new electrical and electronic systems. Nowadays CAN is still used extensively in automotive applications, with an excess of 400 million CAN enabled microcontrollers manufactured each year [14]. The widespread and successful use of CAN in the automotive industry, the low cost asso- ciated with high volume production of controllers and CAN's inherent technical merit, have driven to CAN adoption in other application domains such as: industrial communications, medical equipment, machine tool, robotics and in distributed embedded systems in general. CAN provides two layers of the stack of the Open Systems Interconnection (OSI) reference model: the physical layer and the data link layer. Optionally, it could also provide an additional application layer, not included on the CAN standard. Notice that CAN physical layer was not dened in Bosch original specication, only the data link layer was dened. However, the CAN ISO specication lled this gap and the physical layer was then fully specied. CAN is a message-oriented transmission protocol, i.e., it denes message contents rather than nodes and node addresses. Every message has an associated message identier, which is unique within the whole network, dening both the content and the priority of the message. Transmission rates are dened up to 1 Mbps. The large installed base of CAN nodes with low failure rates over almost two decades, led to the use of CAN in some critical applications such as Anti-locking Brake Systems (ABS) and Electronic Stability Program (ESP) in cars. In parallel with the wide dissemination of CAN in industry, the academia also devoted a large eort to CAN analysis and research, making CAN one of the must studied eldbuses. That is why a large number of books or book chapters describing CAN were published. The rst CAN book, written in French by D. Paret, was published in 1997 and presents the CAN basics [32]. More implementation oriented approaches, including CAN node implementation and application examples, can be found in Lorenz [28] and in Etschberger [16], while more compact descriptions of CAN can be found in [11] and in some chapters of [31]. Despite its success story, CAN application designers would be happier if CAN could be made faster, cover longer distances, be more deterministic and more dependable [34]. Over the years, several protocols based in CAN were presented, taking advantage of some CAN properties and trying to improve some known CAN drawbacks. This chapter, besides presenting an overview of CAN, describes also some other relevant higher level protocols based on CAN, such as CANopen [13], DeviceNet [6], FTT-CAN [1] and TTCAN [25]

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability

Hardware Security of the Controller Area Network (CAN Bus)

The CAN bus is a multi-master network messaging protocol that is a standard across the vehicular industry to provide intra-vehicular communications. Electronics Control Units within vehicles use this network to exchange critical information to operate the car. With the advent of the internet nearly three decades ago, and an increasingly inter-connected world, it is vital that the security of the CAN bus be addressed and built up to withstand physical and non-physical intrusions with malicious intent. Specifically, this paper looks at the concept of node identifiers and how they allow the strengths of the CAN bus to shine while also increasing the level of security provided at the data-link level

A SCADA System for Energy Management in Intelligent Buildings

This paper develops an energy management platform for intelligent buildings using a SCADA system (Supervisory Control And Data Acquisition). This SCADA system integrates different types of information coming from the several technologies present in modern buildings (control of ventilation, temperature, illumination, etc.). The developed control strategy implements an hierarchical cascade controller where inner loops are performed by local PLCs (Programmable Logic Controller), and the outer loop is managed by a centralized SCADA system, which interacts with the entire local PLC network. In this paper a Predictive Controller is implemented above the centralized SCADA platform. Tests applied to the control of temperature and luminosity in huge-area rooms are presented. The developed Predictive Controller optimizes the satisfaction of user explicit preferences coming from several distributed user-interfaces, subjected to the overall constraints of energy waste minimization. In order to run the Predictive Controller with the SCADA platform a communication channel was developed to allow communication between the SCADA system and the MATLAB application where the Predictive Controller runs

Adjacency Matrix Based Energy Efficient Scheduling using S-MAC Protocol in Wireless Sensor Networks

Communication is the main motive in any Networks whether it is Wireless Sensor Network, Ad-Hoc networks, Mobile Networks, Wired Networks, Local Area Network, Metropolitan Area Network, Wireless Area Network etc, hence it must be energy efficient. The main parameters for energy efficient communication are maximizing network lifetime, saving energy at the different nodes, sending the packets in minimum time delay, higher throughput etc. This paper focuses mainly on the energy efficient communication with the help of Adjacency Matrix in the Wireless Sensor Networks. The energy efficient scheduling can be done by putting the idle node in to sleep node so energy at the idle node can be saved. The proposed model in this paper first forms the adjacency matrix and broadcasts the information about the total number of existing nodes with depths to the other nodes in the same cluster from controller node. When every node receives the node information about the other nodes for same cluster they communicate based on the shortest depths and schedules the idle node in to sleep mode for a specific time threshold so energy at the idle nodes can be saved.Comment: 20 pages, 2 figures, 14 tables, 5 equations, International Journal of Computer Networks & Communications (IJCNC),March 2012, Volume 4, No. 2, March 201

Active local distribution network management for embedded generation

Traditionally, distribution networks have been operated as passive networks with uni-directional power flows. With the connection of increasing amounts of distributed generation, these networks are becoming active with power flowing in two directions, hence requiring more intelligent forms of management. The report into issues for access to electricity networks published by the Ofgem/DTI Embedded Generation Working Group in January 2001 called for new work in the area of active distribution network management. The report suggested an evolution from the present passive network control philosophy to fully active network control methods. In line with these recommendations Econnect is developing a new type of distribution network controller, called GenAVC. GenAVC is a controller for electricity distribution networks that aims to increase the amount of energy that can be exported onto the distribution networks by generating plants. The UK is leading the world in electricity de-regulation and one aspect of this is the increasing demand for the connection of distributed generation. Active distribution network management is seen to be essential for networks to accommodate the levels of distributed generation that are predicted for 2010. The work being undertaken as part of this project is therefore at the forefront of international network management technology