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.

Feature-based camera pose estimation

The estimating of the relative orientation and position of a camera is one of the integral topics in the field of computer vision. The accuracy of a certain Finnish technology company’s traffic sign inventory and localization process can be improved by utilizing the aforementioned concept. The company’s localization process uses video data produced by a vehicle installed camera. The accuracy of estimated traffic sign locations depends on the relative orientation between the camera and the vehicle. This thesis proposes a computer vision based software solution which can estimate a camera’s orientation relative to the movement direction of the vehicle by utilizing video data. The task was solved by using feature-based methods and open source software. When using simulated data sets, the camera orientation estimates had an absolute error of 0.31 degrees on average. The software solution can be integrated to be a part of the traffic sign localization pipeline of the company in question.

Integrin traffic and signalling – from plasma membrane to endosomes

Integrins are the main cell surface receptors by which cells adhere to the surrounding extracellular matrix (ECM). Cells regulate integrin-mediated adhesions by integrin endo/exocytic trafficking or by altering the integrin activation status. Integrin binding to ECM-components induces several intracellular signalling cascades, which regulate almost every aspect of cell behaviour from cell motility to survival, and dysregulation of integrin traffic or signalling is associated with cancer progression. Upon detachment, normal cells undergo a specialised form of programmed cell death namely anoikis and the ECM-integrin -mediated activation of focal adhesion kinase (FAK) signalling at the cell surface has been considered critical for anoikis suppression. Integrins are also constantly endocytosed and recycled back to the plasma membrane, and so far the role of integrin traffic in cancer has been linked to increased adhesion site turnover and cell migration. However, different growth factor receptors are known to signal also from endosomes, but the ability of integrins to signal from endosomes has not been previously studied. In this thesis, I demonstrate for the first time that integrins are signalling also from endosomes. In contrast to previous believes, integrin-induced focal adhesion kinase (FAK) signalling occurs also on endosomes, and the endosomal FAK signalling is critical for anoikis suppression and for cancer related processes such as anchorage-independent growth and metastasis. Moreover, we have set up a new integrin trafficking assay and demonstrate for the first time in a comprehensive manner that active and inactive integrins undergo distinct trafficking routes. Together these results open up new horizons in our understanding of integrins and highlight the fundamental connection between integrin traffic and signalling.