A Secure Intrusion Detection System in Cyberphysical Systems Using a Parameter-Tuned Deep-Stacked Autoencoder

Cyber physical systems(CPSs)are a networked system of cyber(computation,communication)and physical(sensors,actuators)elements that interact in a feedback loop with the assistance of human interference.Generally,CPSs authorize critical infrastructures and are considered to be important in the daily lives of humans because they form the basis of future smart devices.Increased utilization of CPSs,however,poses many threats,which may be of major significance for users.Such security issues in CPSs represent a global issue;therefore,developing a robust,secure,and effective CPS is currently a hot research topic.To resolve this issue,an intrusion detection system(IDS)can be designed to protect CPSs.When the IDS detects an anomaly,it instantly takes the necessary actions to avoid harming the system.In this study,we introduce a new parameter-tuned deep-stacked autoencoder based on deep learning(DL),called PT-DSAE,for the IDS in CPSs.The proposed model involves preprocessing,feature extraction,parameter tuning,and classification.First,data preprocessing takes place to eliminate the noise present in the data.Next,a DL-based DSAE model is applied to detect anomalies in the CPS.In addition,hyperparameter tuning of the DSAE takes place using a search-and-rescue optimization algorithm to tune the parameters of the DSAE,such as the number of hidden layers,batch size,epoch count,and learning rate.To assess the experimental outcomes of the PT-DSAE model,a series of experiments were performed using data from a sensor-based CPS.Moreover,a detailed comparative analysis was performed to ensure the effective detection outcome of the PT-DSAE technique.The experimental results obtained verified the superior performance on the applied data over the compared methods.

Consensus of networked control multi-agent systems using a double-layer encryption scheme

This paper addresses the decentralized consensus problem for a system of multiple dynamic agents with remote controllers via networking,known as a networked control multi-agent system(NCMAS).It presents a challenging scenario where partial dynamic entities or remote control units are vulnerable to disclosure attacks,making them potentially malicious.To tackle this issue,we propose a secure decentralized control design approach employing a double-layer cryptographic strategy.This approach not only ensures that the input and output information of the benign entities remains protected from the malicious entities but also practically achieves consensus performance.The paper provides an explicit design,supported by theoretical proof and numerical verification,covering stability,steady-state error,and the prevention of computation overflow or underflow.

Data-Driven Predictive Maintenance Approach for Spinning Cyber-Physical Production System

The fundamental process of predictive maintenance is prognostics and health management,and it is the tool resulting in the development of many algorithms to predict the remaining useful life of industrial equipment.A new data-driven predictive maintenance and an architectural impulse,based on a regularized deep neural network using predictive analytics,are proposed successfully for ring spinning technology.The paradigm shift in computational infrastructures enormously puts pressure on large-scale linear and non-linear automated assembly systems to eliminate and cut down unscheduled downtime and unexpected stoppages.The sensor network designed for the scheduling process comprises different critical components of the same spinning machine frames containing more than thousands of spindles attached to them.We established a genetic algorithm based on multi-sensor performance assessment and prediction procedure for the spinning system.Results show that it operates with a relatively less amount of training data sets but takes advantage of larger volumes of data.This integrated system aims to prognosticate abnormalities,disturbances,and failures by providing condition-based monitoring for each component,which makes it more accurate to locate the defined component failures in the current spinning spindles by using smart agents during the operations through the neural sensing network.A case study has provided to demonstrate the feasibility of the proposed predictive model for highly dynamic,high-speed textile spinning system through real-time data sensing and signal processing via the industrial Internet of Things.

State-of-the-art survey on digital twin implementations

Digital twin(DT)has garnered attention in both industry and academia.With advances in big data and internet of things(IoTs)technologies,the infrastructure for DT implementation is becoming more readily available.As an emerging technology,there are both potential and challenges.DT is a promising methodology to leverage the modern data explosion to aid engineers,managers,healthcare experts and politicians in managing production lines,patient health and smart cities by providing a comprehensive and high fidelity monitoring,prognostics and diagnostics tools.New research and surveys into the topic are published regularly,as interest in this technology is high although there is a lack of standardization to the definition of a DT.Due to the large amount of information present in a DT system and the dual cyber and physical nature of a DT,augmented reality(AR)is a suitable technology for data visualization and interaction with DTs.This paper seeks to classify different types of DT implementations that have been reported,highlights some researches that have used AR as data visualization tool in DT,and examines the more recent approaches to solve outstanding challenges in DT and the integration of DT and AR.

A Hybrid Method for False Data Injection Attack Detection in Smart Grid Based on Variational Mode Decomposition and OS-ELM

Accurate state estimation is critical to wide-area situational awareness of smart grid.However,recent research found that power system state estimators are vulnerable to a new type of cyber-attack,called false data injection attack(FDIA).In order to ensure the security of power system operation and control,a hybrid FDIA detection mechanism utilizing temporal correlation is proposed.The proposed mechanism combines Variational Mode Decomposition(VMD)technology and machine learning.For the purpose of identifying the features of FDIA,VMD is used to decompose the system state time series into an ensemble of components with different frequencies.Furthermore,due to the lack of online model updating ability in a traditional extreme learning machine,an OS-extreme learning machine(OSELM)which has sequential learning ability is used as a detector for identifying FDIA.The proposed detection mechanism is evaluated on the IEEE-14 bus system using real load data from an independent system operator in New York.Apart from detection accuracy,the impact of attack intensity and environment noise on the performance of the proposed method are tested.The simulation results demonstrate the efficiency and robustness of our method.