BotCap: Machine Learning Approach for Botnet Detection Based on Statistical Features

In this paper, we describe a detailed approach to develop a botnet detection system using machine learning (ML)techniques. Detecting botnet member hosts, or identifying botnet traffic has been the main subject of manyresearch efforts. This research aims to overcome two serious limitations of current botnet detection systems:First, the need for Deep Packet Inspection-DPI and the need to collect traffic from several infected hosts. Toachieve that, we have analyzed several botware samples of known botnets. Based on this analysis, we haveidentified a set of statistical features that may help to distinguish between benign and botnet malicious traffic.Then, we have carried several machine learning experiments in order to test the suitability of ML techniques andalso to pick a minimal subset of the identified features that provide best detection. We have implemented ourapproach in a tool called BotCap whose test results showed its proven ability to detect individually infected hostsin a local network

Modelling Multilayer Communication Channel in Terahertz Band for Medical Applications

In this work we present a multi-layer channel model for terahertz communication that incorporates both layers of human body tissues and textile layers. Many research works tackled communication channel modelling in human body alone while some other research focused on textile characterization/modelling alone. There is a real gap in connecting these different models. To investigate this, a multi-layer channel model for terahertz communication is developed, this model assumes external textile layer stacked over layers of human body tissues. The electromagnetic properties of the different layers are extracted from previous works that used time domain spectroscopy (TDS) in the terahertz band to characterize each of the considered layers. The model is implemented as a flexible MATLAB/Octave program that enables the simulation of layers with either fixed or random depths. This paper aims to pave the way to connecting patients’ in-body nano-nodes with off-body (on-cloth) nano-nodes by building such a combined channel model. This helps in many applications especially in the medical field. For example, having connected nano-nodes can help in diagnosing diseases, monitoring health by sending information to the external environment, treatment (e.g., increasing or decreasing a certain dose depending on the monitoring), etc. The obtained results show how the THz signal can be affected when it propagates through heterogeneous mediums (i.e., human body tissues and textile). Various types of path-loss has been calculated for this purpose and verified by comparison with results from previous research on separate models of human body and textile