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.

Training Finnish and English Speakers to Produce Non-native Vowels

The subject of this thesis was the acquisition of difficult non-native vowels by speakers of two different languages. In order to study the subject, a group of Finnish speakers and another group of American English speakers were recruited and they underwent a short listen-and-repeat training that included as stimuli the semisynthetically created pseudowords /ty:ti/ and /tʉ:ti/. The aim was to study the effect of the training method on the subjects as well as the possible influence of the speakers’ native language on the process of acquisition. The selection of the target vowels /y/ and /ʉ/ was made according to the Speech Learning Model and Perceptual Assimilation Model, both of which predict that second language speech sounds that share similar features with sounds of a person’s native language are most difficult for the person to learn. The vowel /ʉ/ is similar to Finnish vowels as well as to vowels of English, whereas /y/ exists in Finnish but not in English, although it is similar to other English vowels. Therefore, it can be hypothesized that /ʉ/ is a difficult vowel for both groups to learn and /y/ is difficult for English speakers. The effect of training was tested with a pretest-training-posttest protocol in which the stimuli were played alternately and the subjects’ task was to repeat the heard stimuli. The training method was thought to improve the production of non-native sounds by engaging different feedback mechanisms, such as auditory and somatosensory. These, according to Template Theory, modify the production of speech by altering the motor commands from the internal speech system or the feedforward signal which translates the motoric commands into articulatory movements. The subjects’ productions during the test phases were recorded and an acoustic analysis was performed in which the formant values of the target vowels were extracted. Statistical analyses showed a statistically significant difference between groups in the first formant, signaling a possible effect of native motor commands. Furthermore, a statistically significant difference between groups was observed in the standard deviation of the formants in the production of /y/, showing the uniformity of native production. The training had no observable effect, possibly due to the short nature of the training protocol.