As a result of the widespread adoption of the Internet of Things, there are now hundreds of millions of connected devices, increasing the likelihood that they may be vulnerable to various types of cyberattacks. In recent years, distributed denial of service (DDoS) has emerged as one of the most destructive tools utilized by attackers. Traditional machine learning approaches are typically ineffective and unable to cope with actual traffic properties when used to identify DDoS attacks. This paper introduces a novel deep learning-based intrusion detection system, specifically designed for deployment at either the Cloud or Fog level in the IoT environment. The proposed model aims to detect all types of DDoS attacks with their specific subcategory. Our hybrid model combines different types of deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), Deep Autoencoder, and Deep Neural Networks (DNNs). Our proposed model is made up of two main levels. The first one contains different parallel sub-neural networks trained with specific algorithms. The second level uses the output of the frozen first level combined with the initial data as input. The idea behind the combination of these various types of deep neural networks is to exploit their different properties to achieve very high performance. To evaluate our model, we used the CIC-DDoS2019 dataset, which satisfies all the constraints of an intrusion detection dataset. The results obtained demonstrate that our proposed model outperformed various well-known machine learning and deep learning models in terms of the true positive rate, accuracy, false alarm rate, average accuracy, and average detection rate.

The Internet of Things is gaining more importance in the present era of Internet technology. It is considered as one of the most important technologies of everyday life. Moreover, IoT systems are ceaselessly growing with more and more devices. They are scalable, dynamic, and distributed, hence the origin of the crucial security requirements in IoT. One of the most challenging issues that the IoT community must handle recently is how to ensure an access control approach that manages the security requirements of such a system. Traditional access control technologies are not suitable for a large-scale and distributed network structure. Most of them are based on a centralized approach, where the use of a trusted third party (TTP) is obligatory. Furthermore, the emergence of blockchain technology has allowed researchers to come up with a solution for these security issues. This technology is highly used to record access control data. Additionally, it has great potential for managing access control requests. This paper proposed a blockchain-based access control taxonomy according to the access control nature: partially decentralized and fully decentralized. Furthermore, it presents an overview of blockchain-based access control solutions proposed in different IoT applications. Finally, the article analyzes the proposed works according to certain criteria that the authors deem important.

The coming 6G wireless network is poised to achieve unprecedented data rates, latency, and integration with newer technologies like AI and IoE. On the other hand, along with this kind of growth in the AI domain and the large-scale connectivity in 6G, it is also going to raise many security concerns at the level of intrusion detection and prevention. For intrusion detection, centralized approaches won’t be able to work effectively, therefore there is an utmost need to design decentralized and privacy-preserving solutions. In this work, we propose a novel secure gradients exchange algorithm for distributed intrusion detection in 6G networks. Our method is designed, to take into account the use of Federated Learning with secure multi-party computation and blockchain technology to ensure that collaborating parties are able to conduct training of intrusion detection models in a secure and collaborative way by retaining privacy in the data. Gradient compression and adaptive secure aggregation strategies are used to further optimize communication overhead and computational complexity so that our design works in a robust and efficient manner with the high data rates and huge connectivity that 6G networks will provide. To achieve our goal, experiments using the CICIoT2023 dataset were performed, and results showed that a federated learning-based hybrid model composed of CNN1D and a multi-head attention mechanism outperformed other well-known deep learning models in terms of performance. It achieved the highest average accuracy with 79.92%, the highest average detection rate with 77.41%, and a low false alarm rate with 2.55%.

In the era of the Fourth Industrial Revolution, cybercriminals are targeting critical infrastructures such as traffic light systems and smart grids. A major concern is the security of such systems, which can be broken down into a number of categories, such as the authentication of data collection devices, secure data transmission, and use of the data by authorized and authenticated parties. The majority of research studies in the literature have largely focused on data integrity and user authentication. So far, no published work has addressed the security of a traffic light system from data collection to data access. Furthermore, it is evident that the conventional cloud computing architecture is incapable of analyzing and managing the massive amount of generated data. As a result, the fog computing paradigm combined with blockchain technology may be the best way to ensure data privacy in a decentralized manner while reducing overheads, latency, and maintaining security. This paper presents a blockchain-based authentication scheme named VDAS using the fog computing paradigm. The formal and informal verifications of the proposed solution are presented. The evaluation of the proposed scheme VDAS showed that it has low communication and computation costs compared to existing lightweight authentication techniques.

Grid is a collection of distributed computing resources. These resources are available over a local or wide area network that appears to an end user or application as one large virtual computing system. The vision is to create virtual dynamic organizations through secure and coordinated resource-sharing among individuals and institutions. Access control to these resources is a problem difficult to manage, how to store and manage the security policies of such a system plays a key role. If these policies are not properly stored, the response time to access control requests will be dramatically increased. Grid Authorization Graph (GAG) was proposed to improve the authorization efficiency by eliminating the redundancy in checking security rules. This article proposes the Weighted Grid Authorization Graph (WGAG) as an enhancement to GAG which further improves the authorization efficiency by avoiding a lots of security rule checking. Finally, as proof of concept, we implement the WGAG simulator where simulations were done. The obtained results show that the proposed model can effectively reduce the complexity of security rule checking and gave better results than GAG.

Access control mechanisms are the way to guarantee secure access to grid resources. Recent research works were focused on how to improve the representation of the resources' security policies for faster decisions making. PCM, HCM, GAG, and WGAG are all different ways to represent these security policies. This paper presents an enhancement to WGAG, the action-weighted grid authorization graph (Action-WGAG). A security policy-parser (SP-Parser) has been developed to implement the Action-WGAG. The evaluation results of the proposed model showed that it assures a smaller number of security rule checking in some cases and a reduction of the answer time to an access control request.