Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats

Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber threats in IoT networks with promising performance. Advanced persistent threat (APT) is prominent for cybercriminals to compromise networks, and it is crucial to long-term and harmful characteristics. However, it is difficult to apply ML-based approaches to identify APT attacks to obtain a promising detection performance due to an extremely small percentage among normal traffic. There are limited surveys to fully investigate APT attacks in IoT networks due to the lack of public datasets with all types of APT attacks. It is worth to bridge the state-of-the-art in network attack detection with APT attack detection in a comprehensive review article. This survey article reviews the security challenges in IoT networks and presents the well-known attacks, APT attacks, and threat models in IoT systems. Meanwhile, signature-based, anomaly-based, and hybrid intrusion detection systems are summarized for IoT networks. The article highlights statistical insights regarding frequently applied ML-based methods against network intrusion alongside the number of attacks types detected. Finally, open issues and challenges for common network intrusion and APT attacks are presented for future research.Comment: ACM Computing Surveys, 2022, 35 pages, 10 Figures, 8 Table

CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties

The increasing processing power of today’s HW/SW platforms leads to the integration of more and more functions in a single device. Additional design challenges arise when these functions share computing resources and belong to different criticality levels. CONTREX complements current activities in the area of predictable computing platforms and segregation mechanisms with techniques to consider the extra-functional properties, i.e., timing constraints, power, and temperature. CONTREX enables energy efficient and cost aware design through analysis and optimization of these properties with regard to application demands at different criticality levels. This article presents an overview of the CONTREX European project, its main innovative technology (extension of a model based design approach, functional and extra-functional analysis with executable models and run-time management) and the final results of three industrial use-cases from different domain (avionics, automotive and telecommunication).The work leading to these results has received funding from the European Community’s Seventh Framework Programme FP7/2007-2011 under grant agreement no. 611146

Specifying, Analyzing, Integrating Mobile Apps and Location Sensors as part of Cyber-Physical Systems in the Classroom Environment

Cyber-Physical Systems (CPS) are characterized as complex systems usually networked, composed of several heterogeneous components that make the connection between events in the physical environment with computation. We can observe that this kind of systems is increasingly used in different areas such as automotive facilities, construction (civil engineering), health care and energy industry, providing a service or activity which depends on the interaction with users and the physical environment in which they are installed. Nowadays, in the educational context, the process of control and monitor of evaluation activities is conducted in a non-automated way by lecturers. This control is performed before, during and after the beginning of the evaluation activity, and include logistical processes such as classroom reservation, distribution of students per classroom, attendance record or fraud control. However, in an environment involving a large number of students, the execution of these tasks becomes difficult to perform efficiently and safely, requiring innovative techniques or assistance tools. In this work, the creation/design of a cyber-physical system through a modeling approach is proposed, aiming to help teachers to control and monitor evaluation activities. Based on a systematic literature study, we claim that there are no studies presenting the modeling of cyber-physical systems in an educational context, enhancing the interest of the proposed case study. In this document, we show how we used a framework named ModelicaML to model this system during the design phase. Also, this framework will offer a simulation component to simulate the behavior of the prescribed system. On the side of the hardware architecture, for the purpose of identifying the valid seats for the specific students inclass during the examination period, an indoor location system will be used, allowing to blueprint the physical layout of the room and globally manage the activity workflow. We finish this work by showing with empirical studies the gains of our solution when compared to the traditional method

A Multi-Criteria Framework to Assist on the Design of Internet-of-Things Systems

The Internet-of-Things (IoT), considered as Internet first real evolution, has become immensely important to society due to revolutionary business models with the potential to radically improve Human life. Manufacturers are engaged in developing embedded systems (IoT Systems) for different purposes to address this new variety of application domains and services. With the capability to agilely respond to a very dynamic market offer of IoT Systems, the design phase of IoT ecosystems can be enhanced. However, select the more suitable IoT System for a certain task is currently based on stakeholder’s knowledge, normally from lived experience or intuition, although it does not mean that a proper decision is being made. Furthermore, the lack of methods to formally describe IoT Systems characteristics, capable of being automatically used by methods is also an issue, reinforced by the growth of available information directly connected to Internet spread. Contributing to improve IoT Ecosystems design phase, this PhD work proposes a framework capable of fully characterise an IoT System and assist stakeholder’s on the decision of which is the proper IoT System for a specific task. This enables decision-makers to perform a better reasoning and more aware analysis of diverse and very often contradicting criteria. It is also intended to provide methods to integrate energy consumptionsimulation tools and address interoperability with standards, methods or systems within the IoT scope. This is addressed using a model-driven based framework supporting a high openness level to use different software languages and decision methods, but also for interoperability with other systems, tools and methods