Industrial Internet Of Things Environment Research Articles

Integrated Genetic Algorithm and Deep Learning Approach for Effective Cyber-Attack Detection and Classification in Industrial Internet of Things (IIoT) Environments

AbstractCyber-attack detection within Industrial Internet of Things (IIoT) environments presents unique challenges due to the complex, resource-constrained, and real-time nature of these networks. Traditional detection techniques often struggle to adapt to the dynamic environment of IIoT. For instance, many existing methods rely on signature-based detection, which fails to identify evolving threats. Other approaches, such as anomaly-based detection, can generate a high rate of False Positives, leading to inefficiencies in threat management. To address these challenges, we propose a novel detection and classification model specifically tailored for IIoT environments. The proposed model integrates Genetic Algorithms (GA) and Deep Learning (DL) to enhance cyber-attack detection within IIoT environments. The GA component optimises feature selection from raw network data, ensuring the extraction of meaningful and relevant features. Leveraging these selected features, the DL component constructs a robust model capable of accurately detecting and classifying various cyber-attack patterns across IIoT devices. Through experimentation on real-world IIoT network traffic (UNSW-NB 15 dataset), the proposed approach demonstrates its efficacy in improving attack detection accuracy and adaptability. The integration of GA and DL offers a synergistic solution that addresses the complexities of IIoT cybersecurity, contributing to a more secure and resilient IIoT ecosystem. The integrated GA–DL classification model developed in this work achieved 98% precision, 96% accuracy, 94% recall, and 12% losses with only less than 50% of the features of the UNSW-NB 15 dataset. The reduction in features required for the identification and classification of cyber-attacks reduces the processing time by 50%.

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

A model proposal for enhancing cyber security in industrial IoT environments

A model proposal for enhancing cyber security in industrial IoT environments

Norm Emergence through Conflict-Blocking Interactions in Industrial Internet of Things Environments.

Norms have been effectively utilized to facilitate smooth interactions among agents. Norms are usually the global data that agents cannot directly access in complex environments; instead, norms can only be indirectly accessed by agents via maintaining their own beliefs about norms. Establishing norms using decentralized interaction-based methods has attracted much attention. However, the current methods overlook Industrial Internet of Things (IIoT) environments. In IIoT, there is a prevalent feature called "conflict-blocking", where agents' conflicting action strategies can block an interaction from being completed or even cause danger. To facilitate norm emergence in IIoT, we propose a framework to support agent decisions in conflict-blocking interactions. The framework aids in achieving system scalability by integrating the fusion of agent beliefs about norms. We prove that the proposed framework guarantees norm emergence. We also theoretically and experimentally analyze the time required for norm emergence under the influence of various factors, such as the number of agents. A vehicle movement simulator is also developed to vividly illustrate the process of norm emergence.

Robust and efficient three-factor authentication solution for WSN-based industrial IoT deployment

The relentless advancements in Cyber-Physical Systems (CPS) and Wireless Sensor Networks (WSN) have paved the way for various practical applications across networking, public safety, smart transportation, and industrial sectors. The Industrial Internet of Things (IIoT) integrates these technologies into complex, interconnected environments where vast amounts of data are transmitted between devices and systems. However, the inherent openness of communication channels in IIoT systems introduces distinctive security and privacy vulnerabilities, where malevolent entities can effortlessly intercept, forge, or delete communication messages. These vulnerabilities are exacerbated by the critical nature of industrial applications, where breaches can lead to significant operational disruptions or safety hazards To address these challenges, several authentication protocols have been proposed. Nevertheless, many of these protocols remain susceptible to various security attacks. To ensure privacy and security in IIoT environments, we introduce a robust and efficient authentication protocol for a WSN-based IIoT environment. This protocol preserves the privacy of information transmitted among all entities and provides an effective solution for securing this sensitive information. Additionally, we present a detailed security analysis of the proposed protocol to formally and informally demonstrate its security strength. The performance analysis is carried out to compare the proposed protocol against existing related protocols, with results unequivocally demonstrating that our protocol offers enhanced privacy and security with reduced costs.

Uncertainty-Aware Federated Reinforcement Learning for Optimizing Accuracy and Energy in Heterogeneous Industrial IoT

The Internet of Things (IoT) technology has revolutionized various industries by allowing data collection, analysis, and decision-making in real time through interconnected devices. However, challenges arise in implementing Federated Learning (FL) in heterogeneous industrial IoT environments, such as maintaining model accuracy with non-Independent and Identically Distributed (non-IID) datasets and straggler IoT devices, ensuring computation and communication efficiency, and addressing weight aggregation issues. In this study, we propose an Uncertainty-Aware Federated Reinforcement Learning (UA-FedRL) method that dynamically selects epochs of individual clients to effectively manage heterogeneous industrial IoT devices and improve accuracy, computation, and communication efficiency. Additionally, we introduce the Predictive Weighted Average Aggregation (PWA) method to tackle weight aggregation issues in heterogeneous industrial IoT scenarios by adjusting the weights of individual models based on their quality. The UA-FedRL addresses the inherent complexities and challenges of implementing FL in heterogeneous industrial IoT environments. Extensive simulations in complex IoT environments demonstrate the superior performance of UA-FedRL on both MNIST and CIFAR-10 datasets compared to other existing approaches in terms of accuracy, communication efficiency, and computation efficiency. The UA-FedRL algorithm attain an accuracy of 96.83% on the MNIST dataset and 62.75% on the CIFAR-10 dataset, despite the presence of 90% straggler IoT devices, attesting to its robust performance and adaptability in different datasets.

Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm

This paper offers a fresh perspective to memory allocation in edge-PLCs within industrial IoT environments, leveraging a hybrid a structure that incorporates Deep Q-Networks (DQN) and Bidirectional Long Short-Term Memory Networks (BiLSTM), complemented by Quantum Genetic Algorithm Optimization. In addressing the dynamic challenges of memory management, our method combines the reinforcement learning capabilities of DQN with the temporal contextualization provided by BiLSTM networks. The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. Moreover, we introduce Quantum Genetic Algorithm Optimization, a cutting-edge approach that infuses quantum-inspired principles into the traditional genetic algorithm framework, to further refine the memory allocation process. By leveraging principles of quantum computing, this optimization algorithm explores the solution space more efficiently, allowing for faster convergence and improved performance. Through simulation experiments, we exhibit the potency of our hybrid approach in reducing data loss probability and enhancing system performance in edge-PLCs. Our findings underscore the significance of integrating advanced machine learning techniques with quantum-inspired optimization algorithms to address complex challenges in industrial IoT environments, offering a promising avenue for enhancing memory allocation efficiency in edge computing systems.

A hybrid intrusion detection approach based on message queuing telemetry transport (MQTT) protocol in industrial internet of things

AbstractThe number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber‐attack detection approaches scrap to address and support the run‐time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber‐attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT‐IOT‐IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber‐attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F‐score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

A Novel Image Encryption Algorithm Based on Cyclic Chaotic Map in Industrial IoT Environments

A Novel Image Encryption Algorithm Based on Cyclic Chaotic Map in Industrial IoT Environments

TSFed: A three-stage optimization mechanism for secure and efficient federated learning in industrial IoT networks

This paper presents a three-stage optimization mechanism designed to enhance Federated Learning (FL) in Industrial Internet of Things (IIoT) networks. Traditional FL optimizations, which typically focus on a single aspect, fall short in IIoT environments. Our approach integrates a multi-criteria enhancement: first, an Ensembled Client Selection Mechanism (ECSM) selects participants based on accuracy, reputation, and randomness. Second, Adaptive Distributed Client Training (ADCT) dynamically adjusts based on participant performance. Lastly, a Secure and Efficient Communication Channel (SECC), backed by blockchain, meets IIoT’s stringent security demands. The evaluation shows TSFed outperforms baseline methods, enhancing FL performance by increasing accuracy and F1-score. Importantly, TSFed improves the efficiency of achieving 80% accuracy on the MNIST dataset by 29.09% over baseline methods, showcasing significant gains in both security and efficiency. This mechanism also exhibits robustness against malicious attacks, setting a new benchmark for FL in IIoT environments.

Dynamic Cooperative Communications with Mutual Information Accumulation for Mobile Robots in Industrial Internet of Things.

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.

An ECC based Secure Authentication Protocol for M2M Communication in Industrial IOT Edge Device

Abstract: Machine to machine communication in industrial IoT is becoming a common phenomenon now-a-days where all the machines like IoT edge devices communicate independently. It’s always been a challenging task to create a protocol for authentication that is both secure and efficient with regard to resources for use in industrial IoT environments' M2M communication. Here we present a safe and effective authentication method that makes use of elliptic curve cryptography (ECC). We demonstrated these vulnerabilities by testing the current protocol with various assaults, including SSL strip attack and MITM attack. To further evaluate the protocol's security against various IoT threats, we employed both official and informal approaches, such as the Scyther tool and BAN logic. Secured from a variety of IoT Threats, such as Man-in-the-middle (MITM) attacks, replay attacks, and impersonation attacks, our suggested protocol, absence of device anonymity, weak mutual authentication etc. but also suitable for resource constraint IoT edge devices like RFID devices, sensors, routers and gateways.

Integrated Framework for Industrial Internet of Things Authentication Leveraging Block chain, Proxy Re-encryption, and AI Techniques

The digital evolution has fostered the rise of the Internet of Things (IoT) and its industrial counterpart, the Industrial Internet of Things (IIoT). While IoT is used to connect everyday devices for daily use, IIoT is responsible on optimizing the industrial processes. This paper first explores blockchain-enhanced authentication mechanisms in industrial settings, highlighting their benefits, challenges, and practical applications. In response to the challenges faced, we propose a novel framework that integrates and enhances upon the leading solutions. Our proposed solution is integrated with block chain and RFID technologies, proxy re-encryption, and a block chain authentication mechanism empowered with Transfer Learning (TL). This integrated approach aims to enhance security, efficiency, and adaptability in IIoT environments. Through comprehensive analysis, the paper aims to provide in-depth review into the potential applications and future directions of blockchain-enabled authentication methods in IIoT, contributing to the advancement of secure and efficient industrial operations.

Enhancing cybersecurity in Edge IIoT networks: An asynchronous federated learning approach with a deep hybrid detection model

In the rapidly evolving field of the Industrial Internet of Things (IIoT), advancements in wireless technology have resulted in significant cybersecurity vulnerabilities. These weaknesses pose serious risks such as damage to manufacturing systems, theft of intellectual property, and substantial financial losses. This study introduces an advanced deep hybrid learning model in an asynchronous federated learning setup, aimed at improving the detection of cyberattacks and ensuring robust data privacy. The combination of Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Long Short-Term Memory (LSTM) networks provides an effective solution for quickly identifying anomalies in IIoT sensor traffic. Our model operates asynchronously, ensuring data remains localised to improve security while avoiding the need for complete node synchronisation. Demonstrating outstanding effectiveness, the model achieves an accuracy of 1.00%, precision of 1.00%, recall of 1.00%, and an F1 score of 1.00% across a variety of IIoT environments. These results highlight the model’s exceptional adaptability and its capability to rapidly respond to emergent threats, marking a significant step forward in the protection of IIoT infrastructures and the rigorous maintenance of data privacy.

Analysis and Research on Secure Access Control Technology of Industrial Internet of Things Based on ZTM Model

The security threat of IIoT is becoming increasingly serious. In order to address this challenge, security access control technology based on the ZTM model has become a hot research topic. The aim of this study is to conduct in-depth analysis and research on the security access control technology applied by the ZTM model in industrial Internet of Things environments. By analyzing the current challenges of IIOT security and the limitations of traditional security models, this paper proposes a series of security access control technologies related to the ZTM model, aiming to quantify and evaluate the effectiveness of access control policies, zero trust of the system, and comprehensive risk assessment. By using empirical research methods, this study verified the feasibility of the proposed technology in actual industrial Internet of Things environments and demonstrated the significant effect of the ZTM model in reducing security risks and improving system credibility. The experimental results showed that the optimized security access control technology improved security performance by 28% and the missed detection rate was as low as 3.2%. This study provides useful insights for practical applications in the field of secure access control and provides a solid foundation for future research.

An explainable machine learning-based web attack detection system for industrial IoT web application security

ABSTRACT Within Industry 4.0, the integration of Industrial IoT (IIoT) marks a transformative phase for modern industries, fostering the development of smart factories and intelligent manufacturing systems. Despite the substantial growth of IIoT, a critical research gap persists in web security within IIoT environments. This paper addresses this gap by proposing an explainable and robust machine learning-based web attack detection system to ensure IIoT web application security using ToN-IoT and NF-ToN-IoTv2 datasets that accurately reflects IIoT traffic. Given eXplainable Artificial Intelligence’s (XAI) capability to instill trustworthiness and transparency in learning models, the authors opted for the SHAP technique for feature selection, leveraging its global insights to explain feature contributions to the system’s decision-making. Compared to the existing works, the system demonstrated strong performance across various metrics, including accuracy, recall, precision, specificity, F-value, FPR, FNR, AUC-ROC curve, MCE, training, and prediction times, in binary and multi-classification scenarios.

A secure and efficient authentication key agreement scheme for industrial internet of things based on edge computing

Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes.

An end-to-end learning approach for enhancing intrusion detection in Industrial-Internet of Things

The Industrial-Internet of Things (I-IoT) stands out as one of the most dynamically evolving subfields within the expansive realm of the Internet of Things (IoT). Its exponential growth is reshaping industrial landscapes, bringing forth transformative innovations and advancements at an unprecedented pace, as the core of Industry 4.0. Among the formidable challenges faced by the Industrial-Internet of Things, cybersecurity stands out as a critical concern. Deep learning-based Intrusion Detection System (IDS) solutions showcase their steadfast ability to secure resource-limited, investigation-demanding, and complex I-IoT environments. However, their effectiveness hinges not only on the model but also on the dataset on which they are trained. While numerous literature studies delve into this field, existing proposed models often grapple with challenges. They are frequently trained on outdated, non-diverse datasets or lack specific features crucial for I-IoT networks. Recent efforts, thankfully, introduce more adequate datasets like Edge-IIoTset. Researchers leverage this extensive dataset to train models, focusing on detecting the 14 sophisticated attacks. These attacks predominantly target real I-IoT networks. Despite these efforts, none of the existing models proves entirely efficient. A review of literature solutions reveals that many models cannot detect all 15 classes in the dataset. Some are multi-staged or overly complex. In response to these challenges, this paper presents an End-to-End learning , non-complex CNN1D model tailored to the specific problem of detecting 14 sophisticated threats targeting I-IoT environments. Our proposed model demonstrated remarkable efficiency with an accuracy of 99.96%, successfully detecting all 15 classes in the Edge-IIoTset dataset with a minimal loss of 0.0011. Not only that, but our model was validated with k-fold cross-validation, demonstrating its efficiency in preserving the same performance on unseen data and its ability to be generalized for real-world I-IoT environments.

Deep learning enabled intrusion detection system for Industrial IOT environment

The prevalence of security vulnerabilities in Internet of Things (IoT) applications poses a serious threat to enterprise systems, necessitating sophisticated and reliable defense solutions to counter emerging and evolving threats. For the Industrial Internet of Things (IIoT), stakeholders require trustworthy and sustainable systems that can prevent the loss of human life during critical operations. The impact of multi-variant persistent and sophisticated bot attacks on connected IIoTs is potentially catastrophic, and their detection presents a highly complex and critical challenge. Therefore, there is a pressing need for efficient and timely detection of IIoT botnet attacks. This research paper proposes a robust deep learning model named AttackNet for the detection and classification of different botnet attacks in IIoT based on adaptive based CNN-GRU model. The model is extensively evaluated using the latest dataset and standard performance evaluation metrics, demonstrating its capacity to protect IIoT networks against sophisticated cyber-attacks with a testing accuracy of 99.75%, a loss of 0.0063, precision and recall score of 99.75% and 99.74% respectively. Our proposed model demonstrates superior accuracy, particularly within the N_BaIoT dataset. It achieves an outstanding accuracy of 99.75% across ten classes, surpassing state-of-the-art techniques by a substantial margin ranging from 3.2% to 16.07%. Moreover, the proposed model outperforms state-of-the-art anomaly detection systems in IIoT based on a real-time IoT device dataset in terms of detecting and classifying botnet attacks accurately.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.