Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of interconnected devices, allowing the use of various smart applications The enormous number of IoT devices generates a large volume of data that requires further intelligent data analysis and processing methods such as deep learning (DL) Notably, DL algorithms, when applied to the Industrial IoT (IIoT), can provide various new applications, such as smart assembling, smart manufacturing, efficient networking, and accident detection and prevention Motivated by these numerous applications, in this article, we present the key potentials of DL in IIoT First, we review various DL techniques, including convolutional neural networks, autoencoders, and recurrent neural networks, as well as their use in different industries We then outline a variety of DL use cases for IIoT systems, including smart manufacturing, smart metering, and smart agriculture We delineate several research challenges with the effective design and appropriate implementation of DL-IIoT Finally, we present several future research directions to inspire and motivate further research in this area

Deep Learning in the Industrial Internet of Things: Potentials, Challenges, and Emerging Applications

Currently, the deficiencies of the 5G mobile system as an enabler of Internet of Everything (IoE) applications are stimulating global research activities to focus on the sixth generation (6G) wireless system. The potential of IoE is enormous and growing exponentially. With the dawn of the fifth industrial revolution, IoE is transposing into industrial Internet of Everything (IIoE) projects which are complex and are eventuating to become a prominent technology for all industries offering new opportunities. This study embodies a synthesis of 6G, IoT, IoE, IIoE exhaustive literature review advancing knowledge to facilitate theory development. For the first time, a novel theoretical framework for the 6G-enabled IIoE (henceforth referred to as 6GIIoE) system was developed. Judiciously, sequential methodology is best suited for this emerging discipline research to create significant new knowledge in the literature contributing eternal insights to expound valuable contexts to ruminate significant findings. The theoretical framework developed recognizes 6GIIoE priority areas, challenges, and applications bestowing a guide for 6G-enabled IIoE initiatives divulging opportunities for future research activities.

https://www.mdpi.com/2571-5577/4/1/11/pdf

6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework

The Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, in this paper, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build a universal anomaly detection model with each local model trained by the deep reinforcement learning (DRL) algorithm. Since local data sets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios. 

Toward Accurate Anomaly Detection in Industrial Internet of Things Using Hierarchical Federated Learning

Adversarial attacks have gained popularity recently due to their simplicity and impact. Their applicability to diverse security scenarios is however less understood. In particular, in some scenarios, attackers may come up naturally with ad-hoc black-box attack techniques inspired directly on characteristics of the problem space rather than using generic adversarial techniques. In this paper we explore an intuitive attack technique for Mouse-based Behavioral Biometrics and compare its effectiveness against adversarial machine learning attacks. We show that attacks leveraging on domain knowledge have higher transferability when applied to various machine-learning techniques and are also more difficult to defend against. We also propose countermeasures against such attacks and discuss their effectiveness.

SCRAP: Synthetically Composed Replay Attacks vs. Adversarial Machine Learning Attacks against Mouse-based Biometric Authentication

Prediction of IIoT traffic using a modified whale optimization approach integrated with random forest classifier

As the Industrial Internet of Things (IIoT) develops rapidly, cloud computing and fog computing become effective measures to solve some problems, e.g., limited computing resources and increased network latency. The Industrial Control Systems (ICS) play a key factor within the development of IIoT, whose security affects the whole IIoT. ICS involves many aspects, like water supply systems and electric utilities, which are closely related to people’s lives. ICS is connected to the Internet and exposed in the cyberspace instead of isolating with the outside recent years. The risk of being attacked increases as a result. In order to protect these assets, intrusion detection systems (IDS) have drawn much attention. As one kind of intrusion detection, anomaly detection provides the ability to detect unknown attacks compared with signature-based techniques, which are another kind of IDS. In this paper, an anomaly detection method with a composite autoencoder model learning the normal pattern is proposed. Unlike the common autoencoder neural network that predicts or reconstructs data separately, our model makes prediction and reconstruction on input data at the same time, which overcomes the shortcoming of using each one alone. With the error obtained by the model, a change ratio is put forward to locate the most suspicious devices that may be under attack. In the last part, we verify the performance of our method by conducting experiments on the SWaT dataset. The results show that the proposed method exhibits improved performance with 88.5% recall and 87.0% F1-score.

/pdf/anomaly-detection-for-industrial-control-system-based-on-3x0n5j4g93.pdf

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Biometric security is one of the fundamental problems in user authentication The authentication model based on mouse dy-namics and biometrics has been widely researched in recent years However, the model security against potential attacks is largely ignored In this paper, we propose a mouse movement simulation method to inspect the vulnerability of this authenti-cation technique The authenticity of the mouse movement data generated by the simulation method is validated by five benchmark classifiers Experimental results show the simula-tion method possesses a high authentication pass rate for most users It indicates the proposed method can simulate real user mouse actions and the persuasive assumption that mouse be-havior is difficult to imitate might be too optimistic

Deceive Mouse-Dynamics-Based Authentication Model via Movement Simulation

The security of industrial control systems (ICSs) has received a lot of attention in recent years. ICSs were once closed networks. But with the development of IT technologies, ICSs have become conn...

/pdf/intrusion-detection-for-industrial-control-systems-based-on-2vpsmn5l5s.pdf

Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

With the development of the Internet, more and more people use virtual private network (VPN) to circumvent censorship or hide themselves for privacy purposes. However, VPN itself faces some security and privacy risks. Widely used all over the world, the VPN Gate is a volunteer-organized public VPN relay system launched in 2013. By analyzing the security of the system, we have found that there is a man-in-the-middle attack risk because an attacker may hijack a VPN session and decrypt the traffic. According to our study, the reason of the security issues is the misuse of the SSL certificate. To mitigate the security risks, we offered a series of recommendations.

/pdf/on-man-in-the-middle-attack-risks-of-the-vpn-gate-relay-3swvnztyoj.pdf

On Man-in-the-Middle Attack Risks of the VPN Gate Relay System

Intrusion detection systems (IDSs) play a significant role in the field of network security, dealing with the ever-increasing number of network threats. Machine learning-based IDSs have attracted a lot of interest owing to their powerful data-driven learning capabilities. However, it is challenging to train the supervised learning algorithms when there are no attack data at hand. Semi-supervised anomaly detection algorithms, which train the model with only normal data, are more suitable. In this study, we propose a novel semi-supervised anomaly detection-based IDS that leverages the capabilities of representation learning and two anomaly detectors. In detail, the autoencoder (AE) is applied to extract representative features of normal data in the first step, and then two semi-supervised detectors, the one-class support vector machine (OCSVM) and Gaussian mixture model (GMM), are trained on the derived features. The two detectors collaborate to detect anomalous samples. The OCSVM predicts the abnormal samples initially, and after that, the GMM is applied to recheck the misclassified samples further. The experiments demonstrate that the AE improves the detection rate, and two detectors are more promising than a single one.

https://www.mdpi.com/2079-9292/12/4/930/pdf?version=1676273750

Chao Wang

Papers

Anomaly Detection for Industrial Control System Based on Autoencoder Neural Network

Deceive Mouse-Dynamics-Based Authentication Model via Movement Simulation

Intrusion Detection for Industrial Control Systems Based on Open Set Artificial Neural Network

On Man-in-the-Middle Attack Risks of the VPN Gate Relay System

Intrusion Detection System Based on One-Class Support Vector Machine and Gaussian Mixture Model