The detection of sensor signal attacks in industrial control systems

U cilju povećanja produktivnosti i efikasnosti proizvodnje, četvrta industrijska revolucija vodi ka implementaciji kibernetsko fizičkih sistema i interneta stvari u industrijskom okruženju. Sveobuhvatna komunikacija čini kibernetsko fizičke sisteme podložnim na spoljašnje uticaje, koji često mogu imati negativnu nameru, npr. napadi i smetnje proistekli od različitih uzročnika. Uticaj napada na sistem može dovesti do anomalija i ozbiljnih posledica po delove sistema ili sistem u celosti. Stoga, odbrambeni mehanizmi za pravovremenu detekciju napada moraju biti razvijeni, kako bi se sistem zaštitio i održala njegova funkcionalnost. U ovom radu, predložen je metod za detekciju napada na senzorske signale u kontinualno upravljanim sistemima. Metod je baziran na mašinama sa nosećim vektorima, a testiran na skupu podataka iz sistema za preradu vode.To improve productivity and efficiency in industrial manufacturing, the fourth industrial revolution leads to the implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in the industrial environment. Ubiquitous communication makes CPS susceptible to external influences, which can have a negative intention; for instance, CPS are prone to various attacks and malicious threats by different adversaries. The impact of an attack on the system can lead to anomalies and serious consequences for system parts or the system as a whole. Security mechanisms must be developed in order to timely detect different attacks and to keep the system safe and protected. In this paper, a method for sensor signal attacks detection in a continuous time controlled systems has been proposed. The method is based on Support Vector Machines (SVM) and tested on the data obtained from the Secure Water Treatment (SWaT) testbed, a scaled-down plant that produces purified water

The detection of sensor signal attacks in industrial control systems

U cilju povećanja produktivnosti i efikasnosti proizvodnje, četvrta industrijska revolucija vodi ka implementaciji kibernetsko fizičkih sistema i interneta stvari u industrijskom okruženju. Sveobuhvatna komunikacija čini kibernetsko fizičke sisteme podložnim na spoljašnje uticaje, koji često mogu imati negativnu nameru, npr. napadi i smetnje proistekli od različitih uzročnika. Uticaj napada na sistem može dovesti do anomalija i ozbiljnih posledica po delove sistema ili sistem u celosti. Stoga, odbrambeni mehanizmi za pravovremenu detekciju napada moraju biti razvijeni, kako bi se sistem zaštitio i održala njegova funkcionalnost. U ovom radu, predložen je metod za detekciju napada na senzorske signale u kontinualno upravljanim sistemima. Metod je baziran na mašinama sa nosećim vektorima, a testiran na skupu podataka iz sistema za preradu vode.To improve productivity and efficiency in industrial manufacturing, the fourth industrial revolution leads to the implementation of Cyber Physical Systems (CPS) and Internet of Things (IoT) in the industrial environment. Ubiquitous communication makes CPS susceptible to external influences, which can have a negative intention; for instance, CPS are prone to various attacks and malicious threats by different adversaries. The impact of an attack on the system can lead to anomalies and serious consequences for system parts or the system as a whole. Security mechanisms must be developed in order to timely detect different attacks and to keep the system safe and protected. In this paper, a method for sensor signal attacks detection in a continuous time controlled systems has been proposed. The method is based on Support Vector Machines (SVM) and tested on the data obtained from the Secure Water Treatment (SWaT) testbed, a scaled-down plant that produces purified water

Motor oil effects on characteristics of engine

Over the last decades there has been more and more severe requirements set to producers of motor vehicles and lubricants. Beside the fact they should provide functional, reliable and permanent operation, a vehicle as a whole is expected to give an adequate answer to wider and various market requirements in terms of environmental protection, improvement of vehicle performance, reduction of fuel consumption and increased traffic safety. When it comes to requirements related to lubricant characteristics, ways of use and intervals in which their replacement should take place, they are becoming more severe, since the motor vehicles' constructors keep making new and harder conditions in reference with performance improvement and lubricant efficiency. This paper describes the results of motor oil quality measurements in relation with exterior speed characteristics of engine as well as the testing programme for oil in the engine on motor brake. The evaluation of motor oil quality is monitored by measuring sediments in vital parts of engine on one hand, and on the other, by measuring output characteristics of engine

Motor oil effects on characteristics of engine

Over the last decades there has been more and more severe requirements set to producers of motor vehicles and lubricants. Beside the fact they should provide functional, reliable and permanent operation, a vehicle as a whole is expected to give an adequate answer to wider and various market requirements in terms of environmental protection, improvement of vehicle performance, reduction of fuel consumption and increased traffic safety. When it comes to requirements related to lubricant characteristics, ways of use and intervals in which their replacement should take place, they are becoming more severe, since the motor vehicles' constructors keep making new and harder conditions in reference with performance improvement and lubricant efficiency. This paper describes the results of motor oil quality measurements in relation with exterior speed characteristics of engine as well as the testing programme for oil in the engine on motor brake. The evaluation of motor oil quality is monitored by measuring sediments in vital parts of engine on one hand, and on the other, by measuring output characteristics of engine

Waveguiding in planar photonic crystals

Photonic crystal planar circuits designed and fabricated in silicon on silicon dioxide are demonstrated. Our structures are based on two-dimensional confinement by photonic crystals in the plane of propagation, and total internal reflection to achieve confinement in the third dimension. These circuits are shown to guide light at 1550 nm around sharp corners where the radius of curvature is similar to the wavelength of light

Gan-based data augmentation in the design of Cyber-attack detection methods

The advent of the Industry 4.0 paradigm that relies on the concepts of Cyber-Physical Systems (CPS) and the Industrial Internet of Things (IIoT) leads to the transition from centralized to distributed control. In this approach, interconnected smart devices (sensors, actuators, etc.) as the key enablers achieve system control through coordinated work. Introduction of IIoT leads to ubiquitous communication between smart devices, thus opening up a vast area for potential malicious threats and attacks which can cause serious consequences, take to system dysfunction or even endanger human lives. Therefore, security mechanisms have to be developed to provide timely detection of different cyber-attacks and to keep the system safe and protected. Since industrial processes are often very complex and their analytical model is very difficult to determine, deep learning based methods for cyber-security mechanisms development are imposed as a technique of choice. Successful employment of data-driven solutions, particularly based on deep learning approaches usually requires a big amount of data. However, due to various limitations in the acquisition of data from the real process, its availability is still a major challenge. For instance, the Industry 4.0 factory implies frequent reconfiguration which reduces the time intervals available for experimental procedures such as data acquisition. One of the ways to deal with this issue is called data augmentation. In this paper, we apply data augmentation in the design of cyber-attack detection methods in Industrial Control Systems (ICS). In particular, we explore the possibilities for utilization of Generative Adversarial Networks (GAN) to generate the necessary amount of data for deep learning based modeling sing a relatively small number of available samples on input

Cyber security in continuous-time controlled systems – overview of the results within the project of mission4.0

U okviru ovog rada navode se rezultati istraživanja sprovedenih u okviru projekta MISSION4.0 pod nazivom Optimizacioni algoritmi za upravljanje i terminiranje kibernetsko fizičkih sistema u okviru Industrije 4.0 zasnovani na dubokom mašinskom učenju i inteligenciji roja, finansiranog od strane Fonda za nauku Republike Srbije u periodu od 2020-2022. godine. Prikazani rezultati odnose se na oblast sajber bezbednosti u kontinualnim sistemima upravljanja što predstavlja jedan od radnih paketa projekta MISSION4.0. U skladu sa tim, pravci istraživanja odnosili su se na razvoj algoritama za detekciju napada u industrijskim sistemima upravljanja sa centralizovanom i distribuiranom arhitekturom, kao i na primenu otvorene platforme za komunikaciju, u cilju bezbedne razmene podataka između uređaja različitih proizvođača. Pored toga, dobijeni rezultati i njihova integracija u predavanja i laboratorijske vežbe poslužili su kao osnova za edukaciju inženjera u oblastima kibernetsko fizičkih sistema, industrijskog interneta stvari i sajber bezbednosti

Cyber-attack detection method based on RNN

Current and forthcoming market requirements bring huge challenges to today manufacturing. Answer to the changing demands and high product variety is found in the integration of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) into industrial plants. CPS as smart devices capable of data processing and information exchange enable fast adaptation of manufacturing resources to production of diversified products. Nevertheless, fully implemented internet communication at factory shop floor opens up a whole new area for potential cyber-attacks. The consequences of attacks can have a negative influence on the system or even endanger human lives. Therefore, defence techniques must be developed to ensure a high level of protection. Early detection of cyber-attacks is crucial to minimize or completely avoid the negative effects of the attack and keep the system safe and reliable. In this work, we propose an attack detection method based on deep learning approach. We explore the application of several deep learning architectures based on Simple Recurrent Neural Networks (Simple RNN) and Long Short-Term Memory (LSTM) based RNN for generation of the detection mechanisms tailored to the concrete process. Our method was experimentally verified using real world data and it proved to be effective, as it detected all considered attacks without false positives

Cybersecurity issues in motion control – an overview of challenges

The fourth industrial revolution known as Industry 4.0 brings digitalization of manufacturing processes to a new level through ubiquitous interconnection and real-time information flow between information technologies (IT) and operational technologies (OT) as parts of Industrial Control Systems (ICS). This information flow is not limited to but expands beyond factory walls enabling manufacturing systems to adapt quickly and efficiently to changing customer demands and diversified products. The adaptation is carried out through physical and/or functional reconfiguration of manufacturing systems where Industrial Internet of Things (IIoT) based on Cyber-Physical Systems (CPS) represents the key technical enabler. These changes result in a transition from centralized to distributed control systems architecture where the whole control task is achieved through intensive cooperation between smart devices (e.g., sensors and actuators) with integrated communication and computation capabilities. However, introducing IIoT in ICS brings about new cybersecurity issues due to increased communication between system elements and connection to the global network, making ICS vulnerable to different cyber-attacks with potentially catastrophic consequences. Recently, the research in ICS cybersecurity has intensified leading to significant results for continuous time and discrete events-controlled systems. However, cybersecurity issues in motion control systems that are frequently employed in different manufacturing resources such as machine tools and industrial robots were not sufficiently explored. This work provides an overview of the cybersecurity related challenges in motion control tasks