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Advances in wearable technologies provide the opportunity to monitor many physiological variables continuously. Stress detection has gained increased attention in recent years, mainly because early stress detection can help individuals better manage health to minimize the negative impacts of long-term stress exposure. This paper provides a unique stress detection dataset created in a natural working environment in a hospital. This dataset is a collection of biometric data of nurses during the COVID-19 outbreak. Studying stress in a work environment is complex due to many social, cultural, and psychological factors in dealing with stressful conditions. Therefore, we captured both the physiological data and associated context pertaining to the stress events. We monitored specific physiological variables such as electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. A periodic smartphone-administered survey also captured the contributing factors for the detected stress events. A database containing the signals, stress events, and survey responses is publicly available on Dryad.

The lack of security in today's in-vehicle network make connected vehicles vulnerable to many types of cyber-attacks. Replay-based injection attacks are one of the easiest type of denial-of-service attacks where the attacker floods the in-vehicle network with malicious traffic with intent to alter the vehicle's normal behavior. The attacker may exploit this vulnerability to launch targeted low-rate injection attacks which are difficult to detect because the network traffic during attacks looks like regular network traffic. In this paper, we propose a sequence mining approach to detect low-rate injection attacks in Control Area Network (CAN). We discuss four different types of replay attacks that can be used by the adversary, and evaluate the effectiveness of proposed method for varying attack characteristics and computational performance for each of the attacks. We observe that the proposed sequence-based anomaly detection achieves over 99% f-score, and outperforms existing dictionary based and multi-variate Markov chain based approach. Given that the proposed technique only uses CAN identifiers, the techniques could be adaptable to any type of vehicle manufacturer.

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