Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.INDEX TERMS Electric vehicles, controller area network (CAN Bus), anomaly detection, one-class support vector machine, optimization algorithm.

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The Proportional Integral Derivative (PID) Fuzzy Sliding Mode Controller (FSMC) is the most widely used control strategy in the Industry (control of robotic arm). The popularity of PID FSMC controllers can be attributed to their robust performance in a wide range of operating conditions and partly to their functional simplicity. The process of setting of PID FSMC controller can be determined as an optimization task. Over the years, use of intelligent strategies for tuning of these controllers has been growing. Biologically inspired evolutionary strategies have gained importance over other strategies because of their consistent performance over wide range of process models and their flexibility. This paper analyses the modified PID FSMC controllers based on minimum rule base for flexible robot manipulator system and test the quality of process control in the simulation environment of MATLAB/SIMULINK Simulator. Index Terms-PID Control, Sliding Mode Control, Fuzzy Logic Methodology, Robust Controller, Flexible Robot Manipulator Design a Novel SISO Off-line Tuning of Modified PID Fuzzy Sliding Mode Controller

Abstract-The Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle data communication. Simplicity, robustness, and suitability for real-time systems are the salient features of the CAN bus protocol. However, it lacks the basic security features such as massage authentication, which makes it vulnerable to the spoofing attacks. In a CAN network, linking CAN packet to the sender node is a challenging task. This paper aims to address this issue by developing a framework to link each CAN packet to its source. Physical signal attributes of the received packet consisting of channel and node (or device) which contains specific unique artifacts are considered to achieve this goal. Material and design imperfections in the physical channel and digital device, which are the main contributing factors behind the device-channel specific unique artifacts, are leveraged to link the received electrical signal to the transmitter. Generally, the inimitable patterns of signals from each ECUs exist over the course of time that can manifest the stability of the proposed method. Uniqueness of the channel-device specific attributes are also investigated for time-and frequency-domain. Feature vector is made up of both time and frequency domain physical attributes and then employed to train a neural network-based classifier. Performance of the proposed fingerprinting method is evaluated by using a dataset collected from 16 different channels and four identical ECUs transmitting same message. Experimental results indicate that the proposed method achieves correct detection rates of 95.2% and 98.3% for channel and ECU classification, respectively.

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