Abstract-During the last years, Wireless Sensor Networks (WSNs) have attracted considerable attention within the scientific community. The applications based on Wireless Sensor Networks, whose areas include, agriculture, military, hospitality management, etc. are growing swiftly. Yet, they are vulnerable to various security threats, like Denial Of Service (DOS) attacks. Such issues can affect and absolutely degrade the performances and cause a dysfunction of the network and its components. However, key management, authentication and secure routing protocols aren't able to offer the required security for WSNs. In fact, all they can offer is a first line of defense especially against outside attacks. Therefore, the implementation of a second line of defense, which is the Intrusion Detection System (IDS), is deemed necessary as part of an integrated approach, to secure the network against malicious and abnormal behaviors of intruders, hence the goal of this paper. This allows improving security and protecting all resources related to a WSN. Recently, different detection methods have been proposed to develop an effective intrusion detection system for WSNs. In this regard, we proposed an integral mechanism which is an hybrid Intrusion Detection approach based on anomaly, detection using support vector machine (SVM), specifications based technique, signature and clustering algorithm to decrease the consumption of resources, by reducing the amount of information forwarded. So, our aim is to protect WSN, without disturbing networks performances through a good management of their resources, especially the energy.
<p><span lang="EN-US">E-learning has shown significant growth in recent years due to its unavoidable benefits in unexpected situations such as the coronavirus disease 2019 (COVID-19) pandemic. Indeed, online exam is a very important component of an online learning program. It allows higher education institutions to assess student learning outcomes. However, cheating in exams is a widespread phenomenon worldwide, which creates several challenges in terms of integrity, reliability and security of online examinations. In this study, we propose a continuous authentication system for online exam. Our intelligent inference system based on machine learning algorithms and rules, detects continuously any inappropriate behavior in order to limit and prevent fraud. The proposed model includes several modules to enhance security, namely the registration module, the continuous students’ identity verification and control module, the live video stream and the end-to-end sessions recording.</span></p>
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