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Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin “powertrace” and the Cooja tool “Radio messages”.

Previous research efforts on developing an Intrusion Detection and Prevention Systems (IDPS) for Android mobile devices rely mostly on centralized data collection and processing on a cloud server. However, this trend is characterized by two major limitations. First, it requires a continuous connection between monitored devices and the server, which might be infeasible, due to mobile network's outage or partial coverage. Second, it increases the risk of sensitive information leakage and the violation of user's privacy. To help alleviate these problems, in this paper, we develop a novel Host-based IDPS for Android (HIDROID), which runs completely on a mobile device, with a minimal computation burden. It collects data in run-time, by periodically sampling features reflecting the utilization of scarce resources on a mobile device (e.g. CPU, memory, battery, bandwidth, etc.). The detection engine exploits statistical and machine learning algorithms to build a data-driven model for the benign behavior. Any observation failing to match this model triggers an alert, and the preventive agent takes proper countermeasure(s) to minimize the risk. HIDROID requires no malicious data for training or tuning, which makes it handy for day-today usage. Experimental test results, on a real-life device, show that HIDROID is well able to learn and discriminate normal from malicious behavior, with very promising accuracy of up to 0.9, while maintaining false positive rate by 0.03. INDEX TERMS Android, security and privacy, intrusion detection and prevention system (IDPS), anomaly detection, malware detection, behavior analysis, machine learning, prototype development.

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The new IEEE 802.21 standard specifies link layer intelligence and other related network information to upper layers in order to optimize handovers between networks of different types, such as WiMAX, Wi-Fi, and 3GPP. This paper makes a short description of 802.21 standard, how it is implemented in ns-2, and the signaling used in a handover between WiMAX and Wi-Fi. The paper also proposes a novel and very simple approach to determine the expected number of handovers in an ns-2 simulation and also evaluates the reliability and scalability of ns-2 when simulating 802.21 scenarios with multiple nodes.

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Abstract-TV White Spaces (TVWS) technology allows wireless devices to opportunistically use locally-available TV channels enabled by a geolocation database. The UK regulator Ofcom has initiated a pilot of TVWS technology in the UK. This paper concerns a large-scale series of trials under that pilot. The purposes are to test aspects of white space technology, including the white space device and geolocation database interactions, the validity of the channel availability/powers calculations by the database and associated interference effects on primary services, and the performances of the white space devices, among others. An additional key purpose is to perform research investigations such as on aggregation of TVWS resources with conventional resources and also aggregation solely within TVWS, secondary coexistence issues and means to mitigate such issues, and primary coexistence issues under challenging deployment geometries, among others. This paper provides an update on the trials, giving an overview of their objectives and characteristics, some aspects that have been covered, and some early results and observations.

Over the past few years, the healthcare sector is being transformed due to the rise of the Internet of Things (IoT) and the introduction of the Internet of Medical Things (IoMT) technology, whose purpose is the improvement of the patient’s quality of life. Nevertheless, the heterogenous and resource-constrained characteristics of IoMT networks make them vulnerable to a wide range of threats. Thus, novel security mechanisms, such as accurate and efficient anomaly-based intrusion detection systems (AIDSs), considering the inherent limitations of the IoMT networks, need to be developed before IoMT networks reach their full potential in the market. Towards this direction, in this paper, we propose an efficient and effective anomaly-based intrusion detection system (AIDS) for IoMT networks. The proposed AIDS aims to leverage host-based and network-based techniques to reliably collect log files from the IoMT devices and the gateway, as well as traffic from the IoMT edge network, while taking into consideration the computational cost. The proposed AIDS is to rely on machine learning (ML) techniques, considering the computation overhead, in order to detect abnormalities in the collected data and thus identify malicious incidents in the IoMT network. A set of six popular ML algorithms was tested and evaluated for anomaly detection in the proposed AIDS, and the evaluation results showed which of them are the most suitable.