In recent years, the security of automotive Cyber-Physical Systems (CPSs) is facing urgent threats due to the widespread use of legacy in-vehicle communication systems. As a representative legacy bus system, the Controller Area Network (CAN) hosts Electronic Control Units (ECUs) that are crucial for the vehicles functioning. In this scenario, malicious actors can exploit the CAN vulnerabilities, such as the lack of built-in authentication and encryption schemes, to launch CAN bus attacks (e.g., suspension, injection, and masquerade attacks) with life-threatening consequences (e.g., disabling brakes). In this article, we present TACAN (Transmitter Authentication in CAN), which provides secure authentication of ECUs on the legacy CAN bus by exploiting thecovert channels, without introducing CAN protocol modifications or traffic overheads (no extra bits or CAN messages are used). TACAN turns upside-down the originally malicious concept of covert channels and exploits it to build an effective defensive technique that facilitates transmitter authentication via a centralized, trusted Monitor Node. TACAN consists of three different covert channels for ECU authentication: 1) the Inter-Arrival Time (IAT)-based, leveraging the IATs of CAN messages; 2) the Least Significant Bit (LSB)-based, concealing authentication messages into the LSBs of normal CAN data; and 3) a hybrid covert channel, exploiting the combination of the first two. In order to validate TACAN, we implement the covert channels on the University of Washington (UW) EcoCAR (Chevrolet Camaro 2016) testbed. We further evaluate the bit error, throughput, and detection performance of TACAN through extensive experiments using the EcoCAR testbed and a publicly available dataset collected from Toyota Camry 2010. We demonstrate the feasibility of TACAN and the effectiveness of detecting CAN bus attacks, highlighting no traffic overheads and attesting the regular functionality of ECUs.

Cyber-physical attacks against plants and Critical Infrastructures (CIs) are among the most significant concerns in the 21st century and can lead to devastating consequences. In particular, with the convergence between the Operational Technology (OT) network and the traditional IT network, malware threats for Industrial Control Systems (ICSs) are gradually increasing. In these scenarios, we need to identify potential cyber threats by developing innovative modeling techniques. However, existing malware-based cyber threats modeling techniques are not fully designed for industrial environment.

The rapid evolution towards the Industry 4.0 improves the performances of Industrial Control Systems (ICSs). However, due to the unmanageable re-engineering cost of pre-existing industrial devices, insecure protocols continue to be used to manage these systems. In this scenario, legacy protocols, such as the Modbus/TCP, are still largely used to control a range of industrial processes alongside with modern technologies. Consequently, hybrid industrial infrastructures with both legacy and innovative devices require novel security and prevention methodologies.

Complex and heterogeneous systems characterize the Industry 4.0. Due to the Information Technology (IT) convergence towards the Operational Technology (OT), the development of innovative cyber-physical security tools represents a milestone for the Industrial Control Systems (ICSs) protection. In this context, honeypots are systems used as decoys to detect and analyze malicious actions. However, industrial networks require specific honeypot development capabilities. In this work, we present MimePot, a cyber-physical honeypot conceived for industrial control networks. Compared to classic honeypots, MimePot offers a model-based approach: it is able to simulate physical processes to lure skilled attackers targeting industrial plants. Moreover, MimePot uses the Software Defined Networking (SDN) technology to provide a consistent future proof security approach. We demonstrate the usefulness of MimePot performing data integrity attacks against a water distribution system in a simulated environment.

The protection of Critical Infrastructures represents a fundamental topic of interest for the modern societies. The testing in real scenarios is hard to achieve due to the severe interdependence between these critical systems and the human well-being. The paper is devoted to introduce a low-cost testbed developed to emulate a Critical Infrastructure as complex cyber-physical system. Moreover, it proposes an approach to identify cyber threats exploiting both cyber and physical behavior of the system. Specifically, common tools from model based fault diagnosis and intrusion detection are applied to determine incipient threats and support for the operator in decision making. All the hardware and software developed are implemented in an open-source fashion in order to share investigation resources with the scientific community.

Due to their relevance, systems such as water distribution, power grids, and gas pipelines are studied in order to guarantee robustness and security. Control systems, fault detection algorithms, and intrusion detection systems represent the crucial systems for critical infrastructure protection development. Behind each of these tools, a human operator is called to be the last decision maker in case of emergency. In this paper, a decision support system, based on the Analytic Hierarchy Process, is proposed and tested in order to suggest to the security operator the best way to recover the safe and secure state of a critical infrastructure under attack. Supported by a laboratory testbed, the behavior of a water distribution system has been emulated. A specific cyber threat has been exploited and identified to highlight the methodology designed for the decision support.