This paper uses a hybrid feature selection process and classification techniques to classify cyber‐attacks in the UNSW‐NB15 dataset. A combination of k‐means clustering, and a correlation‐based feature selection, were used to come up with an optimum subset of features and then two classification techniques, one probabilistic, Naïve Bayes (NB), and a second, based on decision trees (J48), were employed. Our results show that this hybrid feature selection method in combination with the NB model was able to improve the classification accuracy of most attacks, especially the rare attacks. The false alarm rates were lower for most of the attacks, and particularly the rare attacks, with this combination of feature selection and the NB model. The J48 decision tree model, however, did not perform any better with the feature selection, but its classification rate for all attack families was already very high, with or without feature selection.

The inception of malware disguised as a solution to fix a problem to the current sophisticated malware demanding huge money, ransomware has attained significant proliferation through various advancements in information technologies. As a result of this development, cybercriminals have become adept and highly successful in compromising advanced information systems. Computer and network systems infected with ransomware are also often infected with various forms of malicious software. Ransomware targets have become more educated, aware and cautious of malware, motivating cybercriminals to respond with innovative attacks. In this paper, we analyse the evolution of ransomware from the perspective of what makes an individual or an organisation susceptible to the succumbing demands of ransomware. Finally, we conclude with few suggestions about the predictions of future trends of ransomware.

The IEEE 802.15.4 standard specifies two network topologies: star and cluster-tree. A cluster-tree network comprises of multiple clusters that allow the network to scale by connecting devices over multiple wireless hops. The role of a cluster-head (CH) is to aggregate data from all the devices in the cluster and then transmit it to the overall personal area network (PAN) coordinator. This specific role of CH needs to be rotated among multiple coordinators in the cluster to prevent it from energy drain out. Prior works on CH rotation are either based on threshold energy levels or rely on periodic rotation. Both approaches have their respective limitations and, at times, result in unnecessary CH rotations or non-optimal selection of CH. To address this, we propose a non-threshold cluster-head rotation scheme (NCHR), which incurs minimal rotation overhead. It supports topological changes, node heterogeneity, and can also handle CH failures. Through simulations and hardware implementation, the performance of the proposed NCHR scheme is analyzed in terms of network lifetime, CH rotation overhead, and the number of CH rotations. It is shown that the proposed scheme boosts network lifetime, incurs less rotation overhead, and needs fewer CH rotations compared to other related schemes.

Today, Supervisory Control and Data Acquisition (SCADA) devices find their use across a wide range of industries. SCADA applications include industrial, infrastructure, facility, and unique private systems. The evolution of SCADA to interconnect multiple remote locations and networks, communicate over Transmission Control Protocol/Internet Protocol (TCP/IP) on Local Area Networks (LAN) connected to the internet, and integrate with Internet of Things (IoT) and other Internet Enabled Devices (IED) extends vulnerability exposure risks to the internet and presents a greater need for security considerations for design, implementation, and use. The research in this paper presents the SCADA vulnerability baseline and examines mitigation through an integrating and encapsulating Framework We present a securely engineered SCADA framework which provides an improvement in security, performance and return of investment for industrial applications.