recently, smart phones have been used not only for communication, but also for, storing confidential and business information. As a result, the theft or hacking of a mobile phone can lead to disastrous implications, such as intrusion of privacy and monetary loss. In this paper, the application of different biometric features for authentication in smart phones is presented. Also, the differences between various touchscreen patterns in terms of data capturing and template creation are shown. In the experiments, device orientation and speed are used to present the effectiveness and efficiency of using biometrics for authentication in smart phones. Two applications were implemented to collect the different biometric features. After that, kmeans clustering technique was applied on the collected data and the accuracy was measured. The main conclusion is that biometrics related to touch behavior is feasible to authenticate users.
Internet of Things (IoT) is a network which connects different communication devices with the internet to attain quick, robust and realtime information transfer and communication, achieving intelligent management. IoT is still in its infancy so it faces numerous challenges varying from data management to security concerns. Sensors generate enormous quantities of data that need to be handled efficiently to have successful deployment of IoT applications. Concerning data management, a great challenge that faces the IoT environment is the detection of contextual anomalies. Contextual anomaly detection is a sophisticated task because the context has to be taken into consideration in the anomaly detection process rather than checking only the deviation of the data value as in point anomaly detection. As a result, in this paper, a novel clustering based algorithm is proposed to detect contextual anomalies in Internet of Things. Attributes were separated into two different categories, namely contextual attributes and behavioral attributes. K-Means clustering technique was applied on the contextual and behavioral attributes separately, then the intersection between the contextual and behavioral clusters was used to detect the contextual anomalies. Moreover, the algorithm was applied on a real room occupation dataset of size around 20,000 records and the experiments showed promising results.
Internet of Things (IoT) is considered a huge enhancement in the field of information technology. IoT is the integration of physical devices which are embedded with electronics, software, sensors, and connectivity that allow them to interact and exchange data. IoT is still in its beginning so it faces a lot of obstacles ranging from data management to security concerns. Regarding data management, sensors generate huge amounts of data that need to be handled efficiently to have successful employment of IoT applications. Detection of data anomalies is a great challenge that faces the IoT environment because, the notion of anomaly in IoT is domain dependent. Also, the IoT environment is susceptible to a high noise rate. Actually, there are two main sources of anomalies, namely: an event and noise. An event refers to a certain incident which occurred at a specific time, whereas noise denotes an error. Both event and noise are considered anomalies as they deviate from the remaining data points, but actually they have two different interpretations. To the best of our knowledge, no research exists addressing the question of how to differentiate between an event and noise in IoT. As a result, in this paper, an algorithm is proposed to differentiate between an event and noise in the IoT environment. At first, anomalies are detected using exponential moving average technique, then the proposed algorithm is applied to differentiate between an event and noise. The algorithm uses the sensors' values and correlation existence between sensors to detect whether the anomaly is an event or noise. Moreover, the algorithm was applied on a real traffic dataset of size 5000 records to evaluate its effectiveness and the experiments showed promising results.
Apparently, most life activities that people perform depend on their unique characteristics. Personal characteristics vary across people, so they perform tasks in different ways based on their skills. People have different mental, psychological, and behavioral features that affect most life activities. This is the same case with students at various educational levels. Students have different features that affect their academic performance. The academic score is the main indicator of the student's performance. However, other factors such as personality features, intelligence level, and basic personal data can have a great influence on the student's performance. This means that the academic score is not the only indicator that can be used in predicting students' performance. Consequently, an approach based on personal data, personality features, and intelligence quotient is proposed to predict the performance of university undergraduates. Five machine learning techniques were used in the proposed approach. In order to evaluate the performance of the proposed approach, a real student's dataset was used, and various performance measures were computed. Several experiments were performed to determine the impact of various features on the student's performance. The proposed approach gave promising results when tested on the dataset.
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