Internet is considered a Critical Infrastructure (CI) that is vital for both the economy and the society. Disruptions caused by natural disasters, malicious human actions and even hardware failure pose serious risks. Emulation testbeds are increasingly used to study the Internet in order to improve protection and response mechanisms. These are frequently considered more adequate than software simulators to realistically recreate the complex behavior of networks. In this paper we study how testbeds based on the Emulab software can be used to conduct scientifically rigorous experiments, specifically in terms of: a) experiment fidelity, b) repeatability, c) measurement accuracy, and d) interference. Our study, which is based on extensive experimentation on different testbeds, indicates that the current trend of using emulation testbeds is justified as both realistic and efficient. We show that Emulab-based experiments are representative of real systems in terms of emerging behavior (qualitative) and that repeatable experiments are possible. The main contribution of this tutorial article is that based on experimental results we identified caveats and provided insights to significant configuration parameters and limitations that are further elaborated into a set of guidelines that any Emulab user should be aware of. Then, he/she can decide about the importance of each guideline in the context of a specific study and experiment.

Industrial Internet of Things (IIoT) has been designed to perform a more agile and efficient automation, control and orchestration of future industrial systems, while improving the energy efficiency in smart factories. Unfortunately, while the benefits of IIoT are undeniable, their pervasive adoption as key enablers for future industries also paved the way for new security risks. In fact, the damaging effects of exploiting vulnerable IIoT have been repeatedly demonstrated and publicly reported. The Mirai botnet, various reports on hackable and invasive devices, alongside the infamous Stuxnet malware, constitute significant proof on the undisputed and disruptive effect of malware targeting IIoT systems. As a response, a plethora of solutions have been developed to address the issue of securing IIoT systems in specific sectors. Nevertheless, we believe that the gradual decay of the IIoT’s physical dimension (e.g., the physical process), also called aging, is a natural component of IIoT’s life cycle, which has not received sufficient attention from the scientific community. This paper develops a methodology for detecting abnormal behavior in the context of aging IIoT. The approach leverages multivariate statistical analysis (e.g. principle component analysis – PCA), alongside Hotelling’s T2 statistics, and the univariate cumulative sum in order to detect abnormal process events. An innovative feature of the developed approach is the detection of stealth attacks attempting to influence the dataset in each age. Extensive experimental results on a continuous stirred-tank reactor (CSTR) model demonstrate its applicability to IIoT and its superior performance to recently reported techniques.

The fourth industrial revolution, also known as Industry 4.0, brings many advantages including innovative applications and services, new technologies and advanced features, increased operational benefits, and reduced installation costs. However, this technological advancement also exposes several challenges pertaining to the development of cyber-physical industrial architectures, resilient communication systems, and secure data exchange. This paper develops a systematic methodology for designing intrusion detection systems (IDS) specially tailored to address the cyber and physical dimensions of these systems. The approach is aimed at reducing the number of monitored parameters by adopting a three-phase design strategy embracing sensitivity analysis, cross-association, and optimal IDS design. To this end, phase 1 embraces sensitivity analysis to identify sensitive variables to specific interventions (e.g., control signals and cyber attacks), phase 2 adopts the cross-association assessment to optimally structure the process variables in groups that are the most sensitive to groups of interventions, and finally, phase 3 optimally assigns the most sensitive process variables to IDS, while enforcing the IDS capacity limitations and redundancy requirements. Numerical results on a realistic vinyl acetate monomer process show that the approach can reduce the number of variables by 76.8%, thus reducing the complexity and the costs of the detection infrastructure.

EPIC helps assess cyberthreats against the cyber and physical dimensions of networked critical infrastructures.