As the number of devices (things) connected to the Internet (Internet of things: IoT) is growing, achieving robust security and privacy (S&P) is becoming increasingly challenging. With the heavy use of medical things (MT), the S&P in the medical domain poses a serious issue that continues to grow. Due to the criticality and sensitivity of the data in the healthcare domain, ensuring the S&P of the Internet of medical things (IoMT) makes matters even more problematic. Lack of proper S&P in IoMT will not only leave patients' privacy at risk but may also put patients' lives at risk. In this paper, we provide a taxonomy of the S&P issues of IoMT. We also provide an approach to quantify IoMT risks and demonstrate how to assess risks in two IoMT devices. This work aims to increase the S&P awareness among IoMT stakeholders by enabling them to identify and quantify potential IoMT S&P risks.

Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.

Security and privacy are among the key barriers to adopting the Internet of Medical Things (IoMT) solutions. IoMT adopters have to adhere to security and privacy policies to ensure that patient data remains confidential and secure. However, there is confusion among IoMT stakeholders as to what security measures they should expect from the IoMT manufacturers and whether these measures would comply with the adopter’s security and compliance requirements. In this paper, we present a recommendation tool that models IoMT concepts and security issues in addition to successively recommending security measures. The presented tool utilizes semantically enriched ontology to model the IoMT components, security issues, and measures. The developed ontology is equipped with context-aware rules to enable reasoning in order to build a recommendation system that empowers users to make well-educated decisions. The recommendation tool classifies IoMT security threats faced by IoMT stakeholders and automatically recommends security controls that have to be enforced for each threat. We have experimented the proposed tool with respect to the completeness and effectiveness of its output (i.e., security issues and recommended security measures). The results show that the tool was effectively able to recommend necessary security measures.

Cloud computing (CC) is fast-growing and frequently adopted in information technology (IT) environments due to the benefits it offers. Task scheduling and load balancing are amongst the hot topics in the realm of CC. To overcome the shortcomings of the existing task scheduling and load balancing approaches, we propose a novel approach that uses dominant sequence clustering (DSC) for task scheduling and a weighted least connection (WLC) algorithm for load balancing. First, users’ tasks are clustered using the DSC algorithm, which represents user tasks as graph of one or more clusters. After task clustering, each task is ranked using Modified Heterogeneous Earliest Finish Time (MHEFT) algorithm. where the highest priority task is scheduled first. Afterwards, virtual machines (VM) are clustered using a mean shift clustering (MSC) algorithm using kernel functions. Load balancing is subsequently performed using a WLC algorithm, which distributes the load based on server weight and capacity as well as client connectivity to server. A highly weighted or least connected server is selected for task allocation, which in turn increases the response time. Finally, we evaluate the proposed architecture using metrics such as response time, makespan, resource utilization, and service reliability.