5G emerges as the bedrock for the Industrial Internet of Things (IIoT), it facilitates the seamless, low-latency fusion of artificial intelligence and cloud computing, thereby fortifying the entire industrial procedure within a framework of smart and intelligent IIoT ecosystems. Concurrently, the continuously changing landscape of cybersecurity threats in the realm of the Internet of Things (IoT) is giving rise to unparalleled security complexities. These challenges are particularly pronounced in the context of zero-day attacks, and integration of 5G technology further exacerbates the intricacy of the situation. Thus this paper introduces a cutting-edge 5G-enabled framework for cyberthreat detection leveraging Federated Learning (FL) without the need for data sharing. It employs a dual Autoencoder (AE) based model. Distinctly, our model utilizes two synchronized AEs for each client, integral to FL mechanism. While one AE evaluates the IIoT environment based on normal network patterns, another focuses on attack scenarios. For decisive threat assessment, the system uses the capabilities of a one-class SVM classifier with AEs. Furthermore, our method ensures a synergistic blend of self-learning and collaborative learning by implementing a polling mechanism between overarching AE classifier and those tailored to individual client data and counters zero-day threats and out performs traditional AI/ML techniques.

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries. The detection of cyber threats against such extensive, complex, and heterogeneous smart manufacturing industries is very challenging due to the lack of sufficient attack traces. Therefore, in this work, a Federated Learning enabled Deep Intrusion Detection framework is proposed to detect cyber threats in smart manufacturing industries. The proposed FLDID framework allows multiple smart manufacturing industries to build a collaborative model to detect threats and overcome the limited attack example problem with individual industries. Moreover, to ensure the privacy of model gradients, Paillier-based encryption is used in communication between edge devices (representative of smart industries) and the server. The deep learning-based hybrid model, which consists of a Convolutional Neural Network, Long Short Term Memory, and Multi-Layer Perceptron is used in the intrusion detection model. An exhaustive set of experiments on the publically available dataset proves the effectiveness of the proposed framework for detecting cyber threats in smart industries over the state-of-the-art approaches.

This paper introduces a sophisticated approach to network security, with a primary emphasis on utilizing deep learning for intrusion detection. In real-world scenarios, the high dimensionality of training data poses challenges for simple deep learning models and can lead to vanishing gradient issues with complex neural networks. Additionally, uploading network traffic data to a central server for training raises privacy concerns. To tackle these issues, the paper introduces a Residual Network (ResNet)-based deep learning model trained using a federated learning approach. The ResNet effectively tackles the vanishing gradient problem, while federated learning enables multiple Internet Service Providers (ISPs) or clients to engage in joint training without sharing their data with third parties. This approach enhances accuracy through collaborative learning while maintaining privacy. Experimental results on the X-IIoTID dataset indicate that the proposed model outperforms conventional deep learning and machine learning methods in terms of accuracy and other metrics used for evaluation. Specifically, the proposed methodology achieved 99.43% accuracy in a centralized environment and 99.16% accuracy in a federated environment.

In the digital age, the concept of cyber resilience has become a cornerstone for industries aiming to safeguard their operational integrity, data security, and service continuity against the backdrop of an ever-evolving cyber threat landscape. Businesses need cyber-resilient solutions that can anticipate, withstand, recover, or adapt to cyber-crisis situations affecting their operations. This paper provides a comprehensive view of cyber resilience concepts and techniques, offering a foundational understanding for both researchers and security experts. Additionally, it addresses prevalent discrepancies and conceptual ambiguities in resilience literature, specifically focusing with respect to cyber security i.e., cyber resilience. As the concept of cyber resilience is currently shrouded in ambiguity due to the absence of in-depth thorough consensus on its definition, this paper establishes a consolidated scientific definition of cyber resilience, refining theoretical constructs and operational paradigms in the field. By exploring key actors in cyberspace, delineating the characteristics, goals, and objectives, of cyber resilience, and distinguishing it from similar concepts, this paper enhances the foundational understanding of cyber resilience. Furthermore, it delivers critical insights, identifies limitations and challenges, and outlines future research directions in the domain of cyber resilience. This can guide organizations to comprehensively assess, measure, and build their cyber resilience to anticipate, withstand, recover, or adapt to cyber crises that threaten the survival of their organization across a broad range of business processes.