Cyber-physical System Environment Research Articles

Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber-Physical Systems Environment.

The arising of the Cyber-Physical Systems' vision and concepts drives technological evolution toward a new architectural design for the infrastructure of an environment referred to as a Smart Environment. This perspective alters the way systems within Smart City landscapes are conceived, designed, and ultimately realized. Modular architecture, resource-sharing techniques, and precise deployment approaches (such as microservices-oriented or reliant on the FaaS paradigm) serve as the cornerstones of a Smart City cognizant of multiple Cyber-Physical Systems composing it. This paper presents a framework integrating Digital Decisioning, encompassing the automated combination of human-derived knowledge and data-derived knowledge (e.g., business rules and machine learning), to enhance decision-making processes and application definition within the Smart City context.

Using Ensemble Learning for Anomaly Detection in Cyber–Physical Systems

The swift embrace of Industry 4.0 paradigms has led to the growing convergence of Information Technology (IT) networks and Operational Technology (OT) networks. Traditionally isolated on air-gapped and fully trusted networks, OT networks are now becoming more interconnected with IT networks due to the advancement and applications of IoT. This expanded attack surface has led to vulnerabilities in Cyber–Physical Systems (CPSs), resulting in increasingly frequent compromises with substantial economic and life safety repercussions. The existing methods for the anomaly detection of security threats typically use simple threshold-based strategies or apply Machine Learning (ML) algorithms to historical data for the prediction of future anomalies. However, due to the high levels of heterogeneity across different CPS environments, minimizing the opportunities for transfer learning, and the scarcity of real-world data for training, the existing ML-based anomaly detection techniques suffer from a poor predictive performance. This paper introduces a hybrid anomaly detection approach designed to identify threats to CPSs by combining the signature-based anomaly detection typically utilized in IT networks, the threshold-based anomaly detection typically utilized in OT networks, and behavioural-based anomaly detection using Ensemble Learning (EL), which leverages the strengths of multiple ML algorithms against the same dataset to increase the accuracy. Multiple public research datasets were used to validate the proposed approach, with the hybrid methodology employing a divide-and-conquer strategy to offload the detection of certain cyber threats to computationally inexpensive signature-based and threshold-based methods using domain knowledge to minimize the size of the behavioural-based data needed for ML model training, thus achieving a higher accuracy over a reduced timeframe. The experimental results showed accuracy improvements of 4–7% over those of the conventional ML classifiers in performing anomaly detection across multiple datasets, which is particularly important to the operators of CPS environments due to the high financial and life safety costs associated with interruptions to system availability.

ADT: Time series anomaly detection for cyber-physical systems via deep reinforcement learning

Cyber-physical systems (CPS) play a vital role in modern society across various sectors, ranging from smart grid to water treatment, and their security has become one of the major concerns. Due to the significantly growing complexity and scale of CPS and cyber-attacks, it is imperative to develop defense and prevention strategies specifically for CPS that are adaptive, scalable, and robust. An important research and application direction in this domain is time series anomaly detection within CPS utilizing advanced machine learning techniques, such as deep learning and reinforcement learning. However, many anomaly detectors fail to balance between detection performance and computational overhead, limiting their applicability in CPS. In this paper, we introduce a novel agent-based dynamic thresholding (ADT) method based on the deep reinforcement learning technique, i.e. deep Q-network (DQN), to model thresholding in anomaly detection as a Markov decision process. By utilizing anomaly scores generated from an autoencoder and other useful information perceived from a simulated environment, ADT performs the optimal dynamic thresholding control, facilitating real-time adaptive anomaly detection for time series. Rigorous evaluations were conducted on realistic datasets from water treatment and industrial control systems, specifically SWaT, WADI, and HAI, comparing against established benchmarks. The experimental results demonstrate ADT's superior detection performance, dynamic thresholding capability, data-efficient learning, and robustness. Notably, ADT, even when trained on minimal labeled data, consistently outperforms benchmarks with F1 scores ranging from 0.995 to 0.999 across all datasets. It is effective even in challenging scenarios where the environmental feedback is noisy, delayed, or partial. Beyond its direct application as an advanced anomaly detector, ADT possesses the versatility to act as a lightweight dynamic thresholding controller, boosting other anomaly detection models. This underscores ADT's considerable promise in sophisticated and dynamic CPS environments.

Lightweight privacy-preserving authentication mechanism in 5G-enabled industrial cyber physical systems

With the deep integration of informatization and industrialization, cyber-physical system (CPS), which integrates computing, communication and control technologies, comes into being and has been widely used in industrial applications. A large number of information physical system devices and control systems are based on open internet connections, and security and privacy protection issues are gradually emerging, especially in the process of dynamic and difficult to control information physical system stream data transmission and storage, which will face new security threats. To alleviate these issues, user authentication mechanisms can prevent unauthorized access by adversaries in the CPS environment. For this purpose, we put forth a lightweight privacy-preserving authentication scheme for 5G-enabled ecosystems. This method utilizes edge devices to save online transaction data in network and securely transmits IoT devices data from the sensors to the edge gateway. Then, the edge gateway delivers and stores these data in the cloud. A detailed comparative study based on experimental results indicates that our proposal achieves a better balance between security and functionality features, communication and capitulation costs compared to other existing competitive solutions.

ConGraph: Advanced Persistent Threat Detection Method Based on Provenance Graph Combined with Process Context in Cyber-Physical System Environment

With the wide use of Cyber-Physical Systems (CPS) in many applications, targets of advanced persistent threats (APTs) have been extended to the IoT and industrial control systems. Provenance graph analysis based on system audit logs has become a promising way for APT detection and investigation. However, we cannot afford to ignore that existing provenance-based APT detection systems lack the process–context information at system runtime, which seriously limits detection performance. In this paper, we proposed ConGraph, an approach for detecting APT attacks using provenance graphs combined with process context; we presented a module for collecting process context to detect APT attacks. This module collects file access behavior, network access behavior, and interactive relationship features of processes to enrich semantic information of the provenance graph. It was the first time that the provenance graph was combined with multiple process–context information to improve the detection performance of APT attacks. ConGraph extracts process activity features from the provenance graphs and submits the features to a CNN-BiLSTM model to detect underlying APT activities. Compared to some state-of-the-art models, our model raised the average precision rate, recall rate, and F-1 score by 13.12%, 25.61%, and 24.28%, respectively.

Toward cyber-physical systems for monitoring and analyzing energy consumption of machine tools

ABSTRACT This paper presents a digital twin and cyber-physical systems (CPS) environment for monitoring and analyzing the energy consumption of machine tools. Machine tools, which account for a large portion of the manufacturing industry, should be developed in response to environmental issues through carbon neutrality and digital transformation for the Fourth Industrial Revolution. The digital twin, which is at the core of CPS, must support the visualization and analysis of energy consumption issues from various perspectives, and at the same time, support multi-faceted analyses. In this paper, a digital twin that supports the monitoring and analysis of the energy consumption of machine tools and an established CPS environment are proposed. The digital twin consists of a static model containing structural information, a dynamic model expressing the behavior of the object, a visualization model containing a three-dimensional model, and a configuration model linking those three together. In addition, a CPS environment that supports this is implemented through a commercial internet-of-things platform. Lastly, the practicality of the proposed model and system are verified through case studies and a comparative analysis. This research is expected to prompt innovation in the development of cutting-edge machine tools that are more intelligent and closer to carbon neutrality.

Election-based optimization algorithm with deep learning-enabled false data injection attack detection in cyber-physical systems

<abstract> <p>Cyber-physical systems (CPSs) are affected by cyberattacks once they are more connected to cyberspace. Advanced CPSs are highly complex and susceptible to attacks such as false data injection attacks (FDIA) targeted to mislead the systems and make them unstable. Leveraging an integration of anomaly detection methods, real-time monitoring, and machine learning (ML) algorithms, research workers are developing robust frameworks to recognize and alleviate the effect of FDIA. These methods often scrutinize deviations from predictable system behavior, using statistical analysis and anomaly detection systems to determine abnormalities that can indicate malicious activities. This manuscript offers the design of an election-based optimization algorithm with a deep learning-enabled false data injection attack detection (EBODL-FDIAD) method in the CPS infrastructure. The purpose of the EBODL-FDIAD technique is to enhance security in the CPS environment via the detection of FDIAs. In the EBODL-FDIAD technique, the linear scaling normalization (LSN) approach can be used to scale the input data into valuable formats. Besides, the EBODL-FDIAD system performs ensemble learning classification comprising three classifiers, namely the kernel extreme learning machine (KELM), long short-term memory (LSTM), and attention-based bidirectional recurrent neural network (ABiRNN) model. For optimal hyperparameter selection of the ensemble classifiers, the EBO algorithm can be applied. To validate the enriched performance of the EBODL-FDIAD technique, wide-ranging simulations were involved. The extensive results highlighted that the EBODL-FDIAD algorithm performed well over other systems concerning numerous measures.</p> </abstract>

An equilibrium optimizer with deep recurrent neural networks enabled intrusion detection in secure cyber-physical systems

<abstract> <p>Cyber-physical systems (CPSs) are characterized by their integration of physical processes with computational and communication components. These systems are utilized in various critical infrastructure sectors, including energy, healthcare, transportation, and manufacturing, making them attractive targets for cyberattacks. Intrusion detection system (IDS) has played a pivotal role in identifying and mitigating cyber threats in CPS environments. Intrusion detection in secure CPSs is a critical component of ensuring the integrity, availability, and safety of these systems. The deep learning (DL) algorithm is extremely applicable for detecting cyberattacks on IDS in CPS systems. As a core element of network security defense, cyberattacks can change and breach the security of network systems, and then an objective of IDS is to identify anomalous behaviors and act properly to defend the network from outside attacks. Deep learning (DL) and Machine learning (ML) algorithms are crucial for the present IDS. We introduced an Equilibrium Optimizer with a Deep Recurrent Neural Networks Enabled Intrusion Detection (EODRNN-ID) technique in the Secure CPS platform. The main objective of the EODRNN-ID method concentrates mostly on the detection and classification of intrusive actions from the platform of CPS. During the proposed EODRNN-ID method, a min-max normalization algorithm takes place to scale the input dataset. Besides, the EODRNN-ID method involves EO-based feature selection approach to choose the feature and lessen high dimensionality problem. For intrusion detection, the EODRNN-ID technique exploits the DRNN model. Finally, the hyperparameter related to the DRNN model can be tuned by the chimp optimization algorithm (COA). The simulation study of the EODRNN-ID methodology is verified on a benchmark data. Extensive results display the significant performance of the EODRNN-ID algorithm when compared to existing techniques.</p> </abstract>

Chaotic tumbleweed optimization algorithm with stacked deep learning based cyberattack detection in industrial CPS environment

Cyberattacks on cyber-physical systems (CPS) have led to severe concerns, and then it is most significant to identify the attacks in the initial phase. But, there are major problems that are resolved in this area; it contains the capability of the security method for detecting earlier unknown attacks. These challenges are resolved with system behaviour analysis manners and semi-supervised or unsupervised machine learning (ML) approaches. The efficacy of the attack recognition method is strongly dependent upon the databases utilized for training the ML approaches. Anomaly detection is the procedure of recognizing anomalous procedures, which could not equal the predictable system behaviour. It permits the recognition of novel and secret attacks. Presently, anomaly detection systems are frequently executed utilizing ML like shallow (or traditional) learning and deep learning (DL). This article develops a Chaotic Tumbleweed Optimization Algorithm with Stacked Deep Learning based Cyberattack Detection (CTOASDL-CD) approach in the Industrial CPS platform. The CTOASDL-CD technique intends to exploit the FS with an optimal hyperparameter-tuned DL model for the detection of cyberattacks. To resolve the high dimensionality issue, the CTOASDL-CD technique uses CTOA for feature selection (FS) purposes. Besides, the stacked deep belief network (SDBN) model can be utilized for the recognition of cyberattacks. Finally, the tunicate swarm algorithm (TSA) has been deployed for the capable selection of hyperparameter values of the SDBN approach. To determine the improved performance of the CTOASDL-CD approach, a wide-ranging simulation value was performed. The experimental values demonstrated the capable outcome of the CTOASDL-CD methodology in accomplishing security in the industrial CPS environment.

Cyber-physical attack graphs (CPAGs): Composable and scalable attack graphs for cyber-physical systems

Attack graphs are a fundamental security tool focused on depicting how multi-stage attacks can be carried out through a network to compromise specific assets and systems. While attack graphs have been widely utilised in the IT cyber domain, their use in Operational Technology (OT) environments requires new approaches able to properly model and analyse Cyber-Physical Systems (CPS). In this paper, we introduce Cyber-Physical Attack Graphs (CPAGs) as a class of attack graphs able to cover both cyber and physical aspects. CPAGs aim at extending the reach of standard attack graphs to cyber-physical networks typically observed in industrial environments and critical infrastructure systems, analyse how an attacker can move within the network, and understand the impact that these actions may have on the system. We propose a constructive methodology to design CPAGs backed up by a formal rule-based approach that specifies how integral parts of the model can be generated and later composed to build more complex CPAGs. We then explore the semantics of CPAGs associated to cyber and physical attack actions as well as their impact on CPS environments. We also discuss potential CPAG-based analysis techniques and focus on risk analysis using Bayesian CPAGs. Finally, we show the application of the proposed model over a realistic scenario on smart farming using our open source tool T-CITY.

Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-Based Intrusion Detection for Cybersecurity in CPS Environment.

A Cyber-Physical System (CPS) is a network of cyber and physical elements that interact with each other. In recent years, there has been a drastic increase in the utilization of CPSs, which makes their security a challenging problem to address. Intrusion Detection Systems (IDSs) have been used for the detection of intrusions in networks. Recent advancements in the fields of Deep Learning (DL) and Artificial Intelligence (AI) have allowed the development of robust IDS models for the CPS environment. On the other hand, metaheuristic algorithms are used as feature selection models to mitigate the curse of dimensionality. In this background, the current study presents a Sine-Cosine-Adopted African Vultures Optimization with Ensemble Autoencoder-based Intrusion Detection (SCAVO-EAEID) technique to provide cybersecurity in CPS environments. The proposed SCAVO-EAEID algorithm focuses mainly on the identification of intrusions in the CPS platform via Feature Selection (FS) and DL modeling. At the primary level, the SCAVO-EAEID technique employs Z-score normalization as a preprocessing step. In addition, the SCAVO-based Feature Selection (SCAVO-FS) method is derived to elect the optimal feature subsets. An ensemble Deep-Learning-based Long Short-Term Memory-Auto Encoder (LSTM-AE) model is employed for the IDS. Finally, the Root Means Square Propagation (RMSProp) optimizer is used for hyperparameter tuning of the LSTM-AE technique. To demonstrate the remarkable performance of the proposed SCAVO-EAEID technique, the authors used benchmark datasets. The experimental outcomes confirmed the significant performance of the proposed SCAVO-EAEID technique over other approaches with a maximum accuracy of 99.20%.

Swarm intelligence for IoT attack detection in fog-enabled cyber-physical system

To provide remote access, surveillance, and analysis, network integration is common in Cyber-Physical Systems (CPSs). This leads to cyber attacks due to the integration of insecure networking devices. In violating internet security, the attackers interfere with system function, which leads to shattering consequences. With the incorporation of Fog with IoT, the attacks in the CPS can be detected in less time than in cloud-based CPS. The detection of attacks on CPS is targeted by cybercriminals which increases the identification difficulty. This paper proposes a new swarm-based feature selection algorithm to improve attack detection in an IoT-based CPS environment. An Enhanced Chicken swarm optimization (ECSO) with self-learning ability-based feature selection is used to select the relevant features from the preprocessed data. Next, the ensemble classifiers are executed with the desired features on the cloud. The proposed ECSO-based ensemble classifier has experimented against the NSL-KDD dataset. The evaluated results show the adequate performance of the proposed system using various statistical measures.

Optimal feature selection for malware detection in cyber physical systems using graph convolutional network

Due to the fast growth of Internet of Things (IoT) technology, the detection and analysis of malware have become a challenge for industrial applications of Cyber-Physical System (CPS), which delivers a variety of services based on the IoT paradigm. The malicious programme presents a security concern to CPSs because to their reliance on the internet, ICT services and commodities, and the internet itself. The purpose of this research is to build a Snake optimizer-based feature selection with optimum graph convolutional network for malware detection (SOFS-OGCNMD) for the CPS environment. The primary purpose of the SOFS-OGCNMD model is the identification and categorization of harmful software in a CPS environment. To recognize and categorize malware assaults, the SOFS-OGCNMD model use the flower pollination algorithm (FPA) in combination with the GCN method. The design of the FPA permits the selection of the most appropriate parameters for the GCN method, hence enhancing the detection performance. The experimental validation of the SOFS-OGCNMD system is studied using benchmark datasets, and comparative analysis indicates that the system's average accuracy is 98.28%, its average precision is 98.65%, its recall is 98.53%, and its F1-Score is 98.47. The findings indicate that the SOFS-OGCNMD technique outperform more recent models.

On Two Sensors Scheduling for Remote State Estimation With a Shared Memory Channel in a Cyber-Physical System Environment.

This article studies two sensors scheduling with a shared memory channel for remote state estimation in cyber-physical systems (CPSs). We consider that each sensor monitors a plant and sends its local estimate to the remote estimator over a shared memory communication channel, of which the packet reception results between two successive time instants are correlated. This article focuses on how the two sensors are scheduled to minimize the total estimation errors at the remote side. The problem is formulated as the Markov decision process (MDP) and the optimal policy is derived. Moreover, the threshold structure of the optimal policy is given to reduce computation overhead. After proving the Whittle indexability of the overall system under a given condition, the Whittle index policy is adopted to further reduce the computation overhead. Numerical simulations are given to illustrate the theoretical results.

Chaotic marine predators optimization based task scheduling scheme for resource limited cyber-physical systems

Cyber–Physical System (CPS) is the most advanced technological advancement that is set to become the future trend in the domain of Information Technology. In CPS, Task Scheduling (TS) plays a crucial role in terms of increasing the resource consumption and enhancing the performance of the system. In this aspect, the current study develops a novel Chaotic Marine Predators Optimization-based Task Scheduling Scheme for Resource Limited CPS environment, abbreviated as CMPO-TSSCPS technique. The primary aim of the presented CMPO-TSSCPS technique is to allocate n independent tasks, under resource-limited CPS environment, in such a way that the time taken to complete the whole set of tasks gets reduced, while the maximum utilization of the resources is achieved. The presented CMPO-TSSCPS technique is developed by integrating the chaotic concepts and MPO algorithm. This algorithm is inspired by the foraging mechanism of ocean predators such as Lévy and Brownian movements with optimum encounter rate policy. The experimental results establish the superior performance of the proposed CMPO-TSSCPS technique over existing task schedulers.

Aquila Optimization with Machine Learning-Based Anomaly Detection Technique in Cyber-Physical Systems

Cyber-physical system (CPS) is a concept that integrates every computer-driven system interacting closely with its physical environment. Internet-of-things (IoT) is a union of devices and technologies that provide universal interconnection mechanisms between the physical and digital worlds. Since the complexity level of the CPS increases, an adversary attack becomes possible in several ways. Assuring security is a vital aspect of the CPS environment. Due to the massive surge in the data size, the design of anomaly detection techniques becomes a challenging issue, and domain-specific knowledge can be applied to resolve it. This article develops an Aquila Optimizer with Parameter Tuned Machine Learning Based Anomaly Detection (AOPTML-AD) technique in the CPS environment. The presented AOPTML-AD model intends to recognize and detect abnormal behaviour in the CPS environment. The presented AOPTML-AD framework initially pre-processes the network data by converting them into a compatible format. Besides, the improved Aquila optimization algorithm-based feature selection (IAOA-FS) algorithm is designed to choose an optimal feature subset. Along with that, the chimp optimization algorithm (ChOA) with an adaptive neuro-fuzzy inference system (ANFIS) model can be employed to recognise anomalies in the CPS environment. The ChOA is applied for optimal adjusting of the membership function (MF) indulged in the ANFIS method. The performance validation of the AOPTML-AD algorithm is carried out using the benchmark dataset. The extensive comparative study reported the better performance of the AOPTML-AD technique compared to recent models, with an accuracy of 99.37%.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently, with the growth of cyber physical systems (CPS), several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively. Besides, the cloud computing (CC) enabled CPS offers huge processing and storage resources for CPS that finds helpful for a range of application areas. At the same time, with the massive development of applications that exist in the CPS environment, the energy utilization of the cloud enabled CPS has gained significant interest. For improving the energy effectiveness of the CC platform, virtualization technologies have been employed for resource management and the applications are executed via virtual machines (VMs). Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS, this paper focuses on the design of chaotic sandpiper optimization based VM scheduling (CSPO-VMS) technique for energy efficient CPS. The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy. The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory, which substitutes the main parameter and combines it with the chaos. In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm, the chaotic concept is included in the SPO algorithm. The CSPO-VMS technique also derives a fitness function to choose optimal scheduling strategy in the CPS environment. In order to demonstrate the enhanced performance of the CSPO-VMS technique, a wide range of simulations were carried out and the results are examined under varying aspects. The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

Cyber-Attacks in IoT-enabled Cyber-physical Systems

Cyber physical systems (CPS) that are Internet of Things (IoT) enabled might be difficult to secure since security measures designed for general data / value through the development (IT / OT) systems may not work as well in a CPS environment. Consequently, this research provides a two-level ensemble attack detection and attribution framework created for CPS, and more particularly in an industrial control system (ICS). For identifying assaults in unbalanced ICS environments, a decision tree integrated to an unique ensemble deep representation learning model is created at the first extent. An ensemble deep neural network is created for assault features at the second level. Applying actual data collections from the gas pipeline and water treatment system, Findings show that the suggested type is more effective than other competing methods with a similar level of computational complexity.

Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies.

Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability for the identification of attacks promptly became more challenging. Network traffic classification is a multi-faceted process that involves preparation of datasets by handling missing and redundant values. Machine learning (ML) models have been employed to classify network traffic effectively. In this article, we introduce a hybrid Deep learning (DL) model which is designed for enhancing the accuracy of network traffic classification (NTC) within the domain of cyber-physical systems (CPS). Our novel model capitalizes on the synergies among CPS, network traffic classification (NTC), and DL techniques. The model is implemented and evaluated in Python, focusing on its performance in CPS-driven network security. We assessed the model's effectiveness using key metrics such as accuracy, precision, recall, and F1-score, highlighting its robustness in CPS-driven security. By integrating sophisticated hybrid DL algorithms, this research contributes to the resilience of network traffic classification in the dynamic CPS environment.

Metaheuristics Based Energy Efficient Task Scheduling Scheme for Cyber-Physical Systems Environment

The widespread applicability of cyber-physical systems (CPS) necessitates efficient schemes to optimize the performance of both computing units and physical plant. Task scheduling (TS) in CPS is of vital importance to enhance resource usage and system efficiency. Traditional task schedulers in embedded real-time systems are unable to fulfill the performance requirements of CPS because of the task diversity and system heterogeneities. In this study, we designed a new artificial rabbit optimization enabled energy-efficient task-scheduling scheme (ARO-EETSS) for the CPS environment. The presented ARO-EETSS technique is based on the natural survival practices of rabbits, comprising detour foraging and arbitrary hiding. In the presented ARO-EETSS technique, the TS process is performed via the allocation of n autonomous tasks to m different resources. In addition, the objective function is based on the reduction of task completion time and the effective utilization of resources. In order to demonstrate the higher performance of the ARO-EETSS system, a sequence of simulations was implemented. The comparison study underlined the improved performance of the ARO-EETSS system in terms of different measures.