Yes / Multi-stage attacks (MSAs) are among the most serious threats in cyberspace today. Criminals target big organisations and government critical infrastructures mainly for financial gain. These attacks are becoming more advanced and stealthier, and thus have capabilities to evade Intrusion Detection Systems (IDSs). As a result, the attack strategies used in the attack render IDSs ineffective, particularly because of new security challenges introduced by some of the key emerging technologies such as 5G wireless networks, cloud computing infrastructure and Internet of Things (IoT), Advanced persistent threats (APTs) and botnet attacks are examples of MSAs, these are serious threats on the Internet. This work analyses recent MSAs, outlines and reveals open issues, challenges and opportunities with existing detection methods.

Yes / The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on Maximal Frequent Itemset (MFI) mining. The proposed feature selection method uses Gini Impurity (GI) and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a True Positive Rate (TPR) of 100% and a False Positive Rate (FPR) of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.