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Modern vehicle is considered as a system vulnerable to attacks because it is connected to the outside world via a wireless interface. Although, connectivity provides more convenience and features to the passengers, however, it also becomes a pathway for the attackers targeting in-vehicle networks. Research in vehicle security is getting attention as in-vehicle attacks can impact human life safety as modern vehicle is connected to the outside world. Controller area network (CAN) is used as a legacy protocol for in-vehicle communication, However, CAN suffers from vulnerabilities due to lack of authentication, as the information about sender is missing in CAN message. In this paper, a new CAN intrusion detection system (IDS) is proposed, the CAN messages are converted to temporal graphs and CAN intrusion is detected using machine learning algorithms. Seven graph-based properties are extracted and used as features for detecting intrusions utilizing two machine learning algorithms which are support vector machine (SVM) & k-nearest neighbors (KNN). The performance of the IDS was evaluated over three CAN bus attacks are denial of service (DoS), fuzzy & spoofing attacks on real vehicular CAN bus dataset. The experimental results showed that using graph-based features, an accuracy of 97.92% & 97.99% was achieved using SVM & KNN algorithms respectively, which is better than using traditional machine learning CAN bus features. 

Microphone identification addresses the challenge of identifying the microphone signature from the recorded signal. An audio recording system (consisting of microphone, A/D converter, codec, etc.) leaves its unique traces in the recorded signal. Microphone system can be modeled as a linear time invariant system. The impulse response of this system is convoluted with the audio signal which is recorded using “the” microphone. This paper makes an attempt to identify "the" microphone from the frequency response of the microphone. To estimate the frequency response of a microphone, we employ sine sweep method which is independent of speech characteristics. Sinusoidal signals of increasing frequencies are generated, and subsequently we record the audio of each frequency. Detailed evaluation of sine sweep method shows that the frequency response of each microphone is stable. A neural network based classifier is trained to identify the microphone from recorded signal. Results show that the proposed method achieves microphone identification having 100% accuracy.