Biblio
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Context-Oriented Privacy Protection in Wireless Sensor Networks. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–4.
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2019. As more devices become connected to the internet and new technologies emerge to connect them, security must keep up to protect data during transmission and at rest. Several instances of security breaches have forced many companies to investigate the effectiveness of their security measures. In this paper, we discuss different methodologies for protecting data as it relates to wireless sensor networks (WSNs). Data collected from these sensors range in type from location data of an individual to surveillance for military applications. We propose a solution that protects the location of the base station and the nodes while transmitting data.
Use of Artificial Neural Networks to Identify Fake Profiles. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–4.
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2019. In this paper, we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution.
Security in Wireless Sensors Networks. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–3.
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2019. Many routing mechanisms of the wireless sensor network have been suggested in the literature, but there has not been a successful one that was designed with security. In this paper, we discuss the vulnerabilities of wireless sensor networks, how attackers can exploit these vulnerabilities, and the solutions to defend against these attacks. Furthermore, we will suggest solutions and measures secure routing mechanisms in sensor networks and study how it will affect it positively.