Biblio

SCADA control systems use programmable logic controller to interface with critical machines. SCADA systems are used in critical infrastructures, for instance, to control smart grid, oil pipelines, water distribution and chemical manufacturing plants: an attacker taking control of a SCADA system could cause various damages, both to the infrastructure but also to people (for instance, adding chemical substances into a water distribution systems). In this paper we propose a method to detect attacks targeting SCADA systems. We exploit model checking, in detail we model logs from SCADA systems into a network of timed automata and, through timed temporal logic, we characterize the behaviour of a SCADA system under attack. Experiments performed on a SCADA water distribution system confirmed the effectiveness of the proposed method.

Industrial Internet-of-Things has been touted as the next revolution in the industrial domain, offering interconnectivity, independence, real-time operation, and self-optimization. Integration of smart systems, however, bridges the gap between information and operation technology, creating new avenues for attacks from the cyber domain. The dismantling of this air-gap, in conjunction with the devices' long lifespan -in the range of 20-30 years-, motivates us to bring the attention of the community to emerging advanced persistent threats. We demonstrate a threat that bridges the air-gap by leaking data from memory to analog peripherals through Direct Memory Access (DMA), delivered as a firmware modification through the supply chain. The attack automatically adapts to a target device by leveraging the Device Tree and resides solely in the peripherals, completely transparent to the main CPU, by judiciously short-circuiting specific components. We implement this attack on a commercial Programmable Logic Controller, leaking information over the available LEDs. We evaluate the presented attack vector in terms of stealthiness, and demonstrate no observable overhead on both CPU performance and DMA transfer speed. Since traditional anomaly detection techniques would fail to detect this firmware trojan, this work highlights the need for industrial control system-appropriate techniques that can be applied promptly to installed devices.

Controllers of security-critical cyber-physical systems, like the power grid, are a very important class of computer systems. Attacks against the control code of a power-grid system, especially zero-day attacks, can be catastrophic. Earlier detection of the anomalies can prevent further damage. However, detecting zero-day attacks is extremely challenging because they have no known code and have unknown behavior. Furthermore, if data collected from the controller is transferred to a server through networks for analysis and detection of anomalous behavior, this creates a very large attack surface and also delays detection. In order to address this problem, we propose Reconstruction Error Distribution (RED) of Hardware Performance Counters (HPCs), and a data-driven defense system based on it. Specifically, we first train a temporal deep learning model, using only normal HPC readings from legitimate processes that run daily in these power-grid systems, to model the normal behavior of the power-grid controller. Then, we run this model using real-time data from commonly available HPCs. We use the proposed RED to enhance the temporal deep learning detection of anomalous behavior, by estimating distribution deviations from the normal behavior with an effective statistical test. Experimental results on a real power-grid controller show that we can detect anomalous behavior with high accuracy (\textbackslashtextgreater99.9%), nearly zero false positives and short (\textbackslashtextless; 360ms) latency.

Nowadays, physical health of equipment controlled by Cyber-Physical Systems (CPS) is a significant concern. This paper reports a work, in which, a hardware is placed between Programmable Logic Controller (PLC) and the actuator as a solution. The proposed hardware operates in two conditions, i.e. passive and active. Operation of the proposed solution is based on the repetitive operational profile of the actuators. The normal operational profile of the actuator is fed to the protective hardware and is considered as the normal operating condition. In the normal operating condition, the middleware operates in its passive mode and simply monitors electronic signals passing between PLC and Actuator. In case of any malicious operation, the proposed hardware operates in its active mode and both slowly stops the actuator and sends an alert to SCADA server initiating execution of the actuator's emergency profile. Thus, the proposed hardware gains control over the actuator and prevents any physical damage on the operating devices. Two sample experiments are reported in which, results of implementing the proposed solution are reported and assessed. Results show that once the PLC sends incorrect data to actuator, the proposed hardware detects it as an anomaly. Therefore, it does not allow the PLC to send incorrect and unauthorized data pattern to its actuator. Significance of the paper is in introducing a solution to prevent destruction of physical devices apart from source or purpose of the encountered anomaly and apart from CPS functionality or PLC model and operation.