In the emerging industrial Internet of Things (IIoT) era, machine-to-machine (M2M) communication technology is considered as a key underlying technology for building IIoT environments, where devices (e.g., sensors, actuators, and gateways) are enabled to exchange information with each other in an autonomous way without human intervention. However, most of the existing M2M protocols that can be also used in the IIoT domain provide security mechanisms based on asymmetric cryptography resulting in high computational cost. As a consequence, the resource-constrained IoT devices are not able to support them appropriately and thus, many security issues arise for the IIoT environment. Therefore, lightweight security mechanisms are required for M2M communications in IIoT in order to reach its full potential. As a step toward this direction, in this paper, we propose a lightweight authentication mechanism, based only on hash and XOR operations, for M2M communications in IIoT environment. The proposed mechanism is characterized by low computational cost, communication, and storage overhead, while achieving mutual authentication, session key agreement, device's identity confidentiality, and resistance against the following attacks: replay attack, man-in-the-middle attack, impersonation attack, and modification attack.

Multi-standard mobile terminals (MTs) are the trend of current and future mobile devices for taking advantage of heterogeneous integration of wireless access networks and providing ubiquitous connectivity and better quality of service. Holding multiple active interfaces, however, incurs significant power consumption burdens to MTs. This not only increases the carbon footprint of MTs but also makes the batteries of MTs deplete rapidly. Consequently, mobile users may have to relentlessly look for power outlets to charge their devices, which may threaten their true mobility freedom. To this end, in this article, we propose a promising approach based on coalition formation game and inter-terminal cooperation. Our innovative approach motivates MTs to cooperate, while addressing the issue of isolating selfish players. MTs assess radio channels and disseminate the acquired information as well as their available resources to sketch a global view of the radio environment. Based on this view, coalitions are formed whenever energy saving is foreseeable. Within a coalition, MTs pool their resources and perform their tasks cooperatively to maximize their energy efficiency. Simulation results validate that the proposed approach can effectively double the battery lifetimes of MTs, while successfully eliminating selfish players from cooperative groups.

The Fourth Industrial Revolution (Industry 4.0) has transformed factories into smart Cyber-Physical Production Systems (CPPSs), where man, product, and machine are fully interconnected across the whole supply chain. Although this digitalization brings enormous advantages through customized, transparent, and agile manufacturing, it introduces a significant number of new attack vectors—e.g., through vulnerable Internet-of-Things (IoT) nodes—that can be leveraged by attackers to launch sophisticated Distributed Denial-of-Service (DDoS) attacks threatening the availability of the production line, business services, or even the human lives. In this article, we adopt a Machine Learning (ML) approach for network anomaly detection and construct different data-driven models to detect DDoS attacks on Industry 4.0 CPPSs. Existing techniques use data either artificially synthesized or collected from Information Technology (IT) networks or small-scale lab testbeds. To address this limitation, we use network traffic data captured from a real-world semiconductor production factory. We extract 45 bidirectional network flow features and construct several labeled datasets for training and testing ML models. We investigate 11 different supervised, unsupervised, and semi-supervised algorithms and assess their performance through extensive simulations. The results show that, in terms of the detection performance, supervised algorithms outperform both unsupervised and semi-supervised ones. In particular, the Decision Tree model attains an Accuracy of 0.999 while confining the False Positive Rate to 0.001.

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-to-day 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.