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. In this paper, we present a threat modeling framework for Industrial Control Systems malware across two different levels: the Extraction Level and the Modeling Level. We evaluate the effectiveness of our model by analyzing the TRISIS cyber attack as a use case. A complex malware developed to cause operational disruption to industrial plants. Our solution outperforms existing malware threat modeling techniques for the ICS environment, and provides useful mitigation strategies to counter malicious activities. @en

Cyber attacks against Industrial Control Systems are one of the major concerns for worldwide manufacturing companies. With the growth of emerging technologies, protecting large-scale Critical Infrastructures has become a considerable research topic in the past decade. Nowadays, software used to monitor Industrial Control Systems might be malicious and cause harm not only to physical processes but also to people working in industrial environments. To that end, integrating safety and security in Industrial Control Systems requires a well-developed understanding of malware-based cyber attacks. In this paper, we present a comparative analysis framework of ICS Malware in a bi-layered approach: A cyber threat intelligence layer based on the ICS cyber kill chain and a hybrid analysis layer based on a static and dynamic analysis of ICS malware. We evaluated our proposed method by experimenting five well-known ICS malware: Stuxnet, Havex, BlackEnergy2, CrashOverride, and TRISIS. Our comparative analysis results show different and similar strategies used by each ICS malware to disrupt the ICS environment.@en

Given their ubiquity, modern Internet of Things (IoT) devices represent a dangerous attack surface for hackers. These devices are strongly heterogeneous by manufacturer, application field, geographic area of deployment, security requirements, and computational performances. This vulnerability problem is more important in the Industrial Internet of Things (IIoT) scenario, where systems are critical for plant processes, such as power grids and water distribution. In this paper, we present ALISI: <u>A</u> <u>L</u>ightweight <u>I</u>dentification <u>S</u>ystem based on <u>I</u>roha, a blockchain-based identification platform conceived for IoT and IIoT systems. The blockchain technology provides a global identification standard, following a distributed approach in order to collaborate and share responsibilities and costs to gain first-class security features. Our scheme handles the performance issues typical of the blockchain systems using a hybrid on-chain and off-chain approach, achieving low response time and small load on the single device.@en

Nowadays, many companies still use old and insecure protocols in Industrial Control Systems (ICSs). An example of such protocols is Modbus, one of the most employed industrial protocols. Also, companies are moving to Modbus/TCP when there are TCP devices involved in the facility. While remaining insecure, this migration also disrupts the assumption of air-gapped industrial networks, opening more attack surface to previously isolated systems. Due to legacy and efficiency constraint, the replacement of Modbus/TCP with secure protocols is not possible, generating big security issues. In this paper, we present TAMBUS (Transmitter Authentication and packet integrity in Modbus/TCP). This method is the first that at the same time: is not implemented in a secure by obscurity design and keeps the Modbus/TCP protocol compatible with legacy devices. TAMBUS allows detecting attacks with high statistical confidence, by leveraging two covert channels as a mean of providing security: 1) Storage-based, that hides authentication messages into the Modbus/TCP protocol fields; 2) Timing-based, that considers the inter-arrival time of packets. We demonstrate the feasibility and effectiveness of our method through a prototype implementation and testing in an industrial testbed environment. Our experiments confirm that TAMBUS introduces only a small overhead, negligible in most application, and it preserves the regular functioning of industrial systems. In particular, considering the storage-based covert channel, TAMBUS introduces an error into transmitted values of only 1.19×10−5%, without traffic overhead. On the other hand, TAMBUS can transmit correct security information through the timing-based covert channel with an accuracy of more than 99.99%.@en

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 the covert 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. @en