IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Nagarajan, Senthil Murugan; Deverajan, Ganesh Gopal; Mahapatra, Rajendra Prasad; Al-Numay, Mohammed S.

doi:10.1016/j.comcom.2022.02.022

Cited by 41 publications

(12 citation statements)

References 35 publications

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“…Nagarajan et al [14] suggested the Data-Correlation-Aware Unsupervised DL method for AD in CPS that utilizes an undigraph framework for storing samples and implied relation amongst samples. The authors devise a dual-AE for training both original features and implied correlation features amongst data.…”

Section: Related Workmentioning

confidence: 99%

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

Ramachandran¹,

Gayathri²,

Alkhayyat³

et al. 2023

Computer Systems Science and Engineering

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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%.

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Section: Related Workmentioning

confidence: 99%

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

Ramachandran¹,

Gayathri²,

Alkhayyat³

et al. 2023

Computer Systems Science and Engineering

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“…A real‐time monitoring system for IoT devices was created utilizing flow‐level telemetry and machine learning in this article. Nagarajan et al (2022) It can see the network behaviour of IoT devices and their statuses, such as booting or user engagement. A set of classification models was then trained for three‐stage inference architecture utilizing real traffic traces of 17 IoT devices gathered over 6 months.…”

Section: Existing Research Work On Iot Devices Based On Cyber Securitymentioning

confidence: 99%

Challenges and vulnerability evaluation of smart cities in IoT device based on cybersecurity mechanism

Almagrabi

2022

Expert Systems

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In recent times, Cyber thieves discover new software vulnerabilities and attack vectors to take advantage of them. Cyber Security (CS) specialists are having a constant battle to stay on top of the latest threats. Knowledge sharing, sometimes called intelligence sharing, can enhance collective resilience and responsiveness in the face of dangers if peers are willing to share information proactively. Internet of Things (IoT) is the buzz word which integrates almost all the technology concerning computation and communication. Before the IoT goal can be fully realized, some barriers must be addressed, with security and privacy being two of the most essential. When IoT devices connect to an IoT gateway or other edge device, the sensor data they gather is either transmitted to the cloud for analysis or processed locally. Based on the received information, they act and it can expose a device as well as its data. Insufficient or no device authentication and authorization are common problems, as is poor or no encryption. Due to restricted energy supplies and low processing power, IoT security and privacy are complex, especially for resource‐constrained devices. In this article, IoT‐CS security problems and associated threats are discussed. The overarching goals were identifying assets and exploring possible threats, assaults, and vulnerabilities encountered by the IoT in the sectors like healthcare, transportation, utilities, safety, and environmental health that might be significantly improved with IoT‐CS deployment in smart cities. The predominant challenges in the deployment of the IoT related technologies had been studied in the aspects of cyber security and mitigation strategies had been discussed.

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“…In addition, the authors develop a forecasting method dependent upon the enhanced NN infrastructure. Some other methods in the literature are available in [20][21][22][23].…”

Section: Literature Reviewmentioning

confidence: 99%

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

et al. 2022

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As cyberattacks develop in volume and complexity, machine learning (ML) was extremely implemented for managing several cybersecurity attacks and malicious performance. The cyber-physical systems (CPSs) combined the calculation with physical procedures. An embedded computer and network monitor and control the physical procedure, commonly with feedback loops whereas physical procedures affect calculations and conversely, at the same time, ML approaches were vulnerable to data pollution attacks. Improving network security and attaining robustness of ML determined network schemes were the critical problems of the growth of CPS. This study develops a new Stochastic Fractal Search Algorithm with Deep Learning Driven Intrusion Detection system (SFSA-DLIDS) for a cloud-based CPS environment. The presented SFSA-DLIDS technique majorly focuses on the recognition and classification of intrusions for accomplishing security from the CPS environment. The presented SFSA-DLIDS approach primarily performs a min-max data normalization approach to convert the input data to a compatible format. In order to reduce a curse of dimensionality, the SFSA technique is applied to select a subset of features. Furthermore, chicken swarm optimization (CSO) with deep stacked auto encoder (DSAE) technique was utilized for the identification and classification of intrusions. The design of a CSO algorithm majorly focuses on the parameter optimization of the DSAE model and thereby enhances the classifier results. The experimental validation of the SFSA-DLIDS model is tested using a series of experiments. The experimental results depict the promising performance of the SFSA-DLIDS model over the recent models.

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IADF-CPS: Intelligent Anomaly Detection Framework towards Cyber Physical Systems

Cited by 41 publications

References 35 publications

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

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

Challenges and vulnerability evaluation of smart cities in IoT device based on cybersecurity mechanism

Evolutionary-Based Deep Stacked Autoencoder for Intrusion Detection in a Cloud-Based Cyber-Physical System

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