Android is increasingly being targeted by malware since it has become the most popular mobile operating system worldwide. Evasive families, such as Chamois, designed to turn devices into bots that form part of a larger botnet are becoming prevalent. This calls for more effective methods detection botnets. Recently, deep learning gained attention machine based approach enhance detection. However...

In this paper, some clustering techniques are analyzed to compare their ability to detect botnet traffic by selecting features that distinguish connections belonging to or not belonging to a botnet. By considering the history of network’s connections, some clustering algorithms are used to derive a set of rules to decide which should be considered as a botnet. Our main contribution is to evalua...

Due to the growing number of Internet Things (IoT) devices, network attacks like denial service (DoS) and floods are rising for security reliability issues. As a result these attacks, IoT devices suffer from disruption. Researchers have implemented different techniques identify aimed at vulnerable devices. In this study, we propose novel features selection algorithm FGOA-kNN based on hybrid fil...

Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML) techniques have widely used for detection with flow-based features. The prime challenges features are that they high computational overhead and do not fully capture network communication patterns. Recently, graph-based ML witnessed a dramatic increase in attention. In networks, graph data offers insights infor...