Biblio

Industry 4.0 advent opens several cyber-threats scenarios originally designed for classic information technology, drawing the attention to the serious risks for the modern industrial control networks. To cope with this problem, in this paper we address the security issues related to covert channels applied to industrial networks, identifying the new vulnerability points when information technologies converge with operational technologies such as edge computing infrastructures. Specifically, we define two signaling strategies where we exploit the Modbus/TCP protocol as target to set up a covert channel. Once the threat channel is established, passive and active offensive attacks (i.e. data exfiltration and command an control, respectively) are further exploited by implementing and testing them on a real Industrial Internet of Things testbed. The experimental results highlight the potential damage of such specific threats, and the easy extrapolation of the attacks to other types of channels in order to show the new risks for Industry 4.0. Related to this, we discuss some countermeasures to offer an overview of possible mitigation and defense measures.
 

Internet of Things (IoT) technologies have enabled Cyber-Physical Systems (CPS) to become fully interconnected. This connectivity however has radically changed their threat landscape. Existing risk assessment methodologies often fail to identify various attack paths that stem from the new connectivity/functionality features of IoT-enabled CPS. Even worse, due to their inherent characteristics, IoT systems are usually the weakest link in the security chain and thus many attacks utilize IoT technologies as their key enabler. In this paper we review risk assessment methodologies for IoT-enabled CPS. In addition, based on our previous work (Stellios et al. in IEEE Commun Surv Tutor 20:3453–3495, 2018, [47]) on modeling IoT-enabled cyberattacks, we present a high-level risk assessment approach, specifically suited for IoT-enabled CPS. The mail goal is to enable an assessor to identify and assess non-obvious(indirect or subliminal) attack paths introduced by IoT technologies, that usually target mission critical components of an CPS.

As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled: The attacker initially exploits some vulnerable IoT technology as a first step towards compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical backend systems. However, in sectors where IoT is usually at the enduser side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect and subliminal attack paths against critical targets. Our goal is threefold: (i) To assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; (ii) To identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services, and (iii) To examine mitigation strategies for all application domains.

The smart grid is an electronically controlled electrical grid that connects power generation, transmission, distribution, and consumers using information communication technologies. One of the key characteristics of the smart grid is its support for bi-directional information flow between the consumer of electricity and the utility provider. This two-way interaction allows electricity to be generated in real-time based on consumers’ demands and power requests. As a result, consumer privacy becomes an important concern when collecting energy usage data with the deployment and adoption of smart grid technologies. To protect such sensitive information it is imperative that privacy protection mechanisms be used to protect the privacy of smart grid users. We present an analysis of recently proposed smart grid privacy solutions and identify their strengths and weaknesses in terms of their implementation complexity, efficiency, robustness, and simplicity.