Recently, mobile devices such as smartphones and tablets have emerged as one of the most popular forms of communication. This trend raises the question about the security of the private data and communication of the people using those devices. With increased computational resources and versatility the number of security threats on such devices is growing rapidly. Therefore, it is vital for security specialists to find adequate anti-measures against the threats. Machine Learning approaches with their ability to learn from and adapt to their environments provide a promising approach to modelling and protecting against security threats on mobile devices. This paper presents a comparative study and implementation of Decision Trees and Neural Network models for the detection of port scanning showing the differences between the responses on a desktop platform and a mobile device and the ability of the Neural Network model to adapt to the different environment and computational resource available on a mobile platform.
|Title of host publication||Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings|
|Editors||Valeri Mladenov, Chrisina Jayne, Lazaros Iliadis|
|Publication status||Published - 2014|
Bibliographical noteThis paper is not available on the repository
- Intrusion Detection Port Scanning Cascade Correlation Neural Networks Decision Trees Android Mobile devices
Panchev, C., Dobrev, P., & Nicholson, J. (2014). Detecting Port Scans against Mobile Devices with Neural Networks and Decision Trees. In V. Mladenov, C. Jayne, & L. Iliadis (Eds.), Engineering Applications of Neural Networks: 15th International Conference, EANN 2014, Sofia, Bulgaria, September 5-7, 2014. Proceedings (pp. 175-182). Springer Verlag. https://doi.org/10.1007/978-3-319-11071-4_17