Industrial Control Systems are part of our daily life in industries such as transportation, water, gas, oil, smart cities, and telecommunications. Technological development over time have improved their components including operating system platforms, hardware capabilities, and connectivity with networks inside and outside the organization. Consequently, the Industrial Control Systems components are exposed to sophisticated threats with weak security mechanism in place. This paper proposes a supervised energy monitoring-based machine learning approach for anomaly detection in a clean water supply system. A testbed of such a system is implemented using the Festo MPA Control Process Rig. The machine-learning algorithms, which include SVN, KNN, and Random Forest, perform classification tasks process in three different datasets obtained from the testbed. The algorithms are compared in terms of accuracy and F-measure. The results show that Random Forest achieves 5% better performance over KNN and SVM with small datasets and 4% regarding large datasets. For the time taken to build the model, KNN presents the best performance. However, its difference with Random Forest is smaller than with SVM.
Industrial Control Systems (ICS) are frequently used in manufacturing and critical infrastructures like water treatment, chemical plants, and transportation schemes. Citizens tend to take modern-day conveniences such as trains, planes or tap water for granted without considering the critical systems involved for their operations. Interrupting these industries could lead to disastrous consequences, leading to financial losses or even costing human lives. For that reason, researchers have been actively investigating the threats targeting ICS. In this paper, the authors propose a mechanism of attack detection and mitigation for attacks focusing on the input memory of Programming Logic Controllers (PLCs). To help investigate this concept, a testbed that models a clean water supply system was built using components and technologies currently used in the industry. The mechanism supporting attack detection and response for the input memory is implemented within the PLC itself as part of its programming. The mechanism of response involves three different techniques: optimised datablocks, switching between control strategies and obtaining the sensor readings directly from its analogue channel. The results demonstrate the feasibility of the proposed approach along with the effectiveness of each response mechanism.
Critical infrastructures such as nuclear plants or water supply systems are mainly managed through electronic control systems. Such systems comprise of a number of elements, such as programmable logic controllers (PLC), networking devices, and actuators. With the development of online and networking solutions, such electronic control systems can even be managed online. Even though network connected control systems permit users to keep up to date with system operation, it also opens the door to attackers taking advantages of such availability. In this paper, a novel attack vector for modifying PLC memory is proposed, which affects the perceived values of sensors, such as a water flow meter, or the configuration of actuators, such as a pump. In addition, this attack vector can also manipulate control variables located in the PLC working memory, reprogramming decision making rules. To show the impact of the attacks in a real scenario, a model of a clean water supply system is implemented in the Festo rig. The results show that the attacks on the PLC memory can have a significant detrimental effect on control system operations. Further, a mechanism of detecting such attacks on the PLC memory is proposed based on monitoring energy consumption and electrical signals using current-measurement sensors. The results show the successful implementation of the novel PLC attacks as well as the feasibility of detecting such attacks.
This paper proposes a method to investigate into helicopter landing on uneven terrain by means of using a scaled articulated robotic landing gear. A mathematical model of an articulated robotic landing gear that adapts to uneven ground conditions is considered. The model consists of a planar landing gear composed of two legs connected by a base and a skid at each end. Each skid has two degrees of freedom with PID joint controllers to provide stability while landing. A combination of Lagrange and Newton-Euler techniques is used to model the system dynamics. This work also includes a model of the ground interaction, a thrust controller and a level controller to maintain stability while landing. Experimental results with a laboratorybuild scaled prototype are included and compared with the simulations.
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