Extracurricular language activities in higher education: Perspectives of teachers and students

This Master’s thesis researches the topic “Extracurricular language activities in higher education: Perspectives of teachers and students”. In the light of several learning theories, namely, Self-Determination Theory, Social Learning Theory and Incidental Learning Theory, extracurricular participation in language related activities is studied. The main aims of the research are as follows: to study how extracurricular language activities can be organized and supported by the education institution; to investigate how such activities can promote the participants’ learning; and, to research how these activities can be developed and improved in the future. Due to the qualitative character of this research, the empirical data collected through interviews and their thematic analysis allow to study the participants’ perceptions on the above-mentioned issues. Among other results of the research, it can be noted that the organizers of extracurricular language activities and the participants of the activities may have different perspectives on the aims of the activities, as well as their advantages. Additionally, it has been found that the participants of activities would often speak on certain categories that imply the connection to some learning theories, which allows to hypothesize that some learning could be observed in those participants, following participation in extracurricular activities. This is an implication for further research in the area, which can focus on correlations between participation in extracurricular language activities and learning outcomes of the participants.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.