Intelligent Transportation Systems (ITSs) are transforming the global monitoring of road safety. These systems, including vehicular networks and transportation infrastructure, are vulnerable to several security issues, which could disrupt services and potentially cause harm to the users. It is crucial to establish robust security measures to protect against evolving attacks and ensure the safe and reliable operation of ITS. Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) are mainly used to enhance the security of ITS. The adoption of AI-based techniques to secure ITS against new emerging threats has been limited due to a lack of realistic and recent data on these types of attacks ( $i.e.,$ zero-day attacks). In this context, we introduce a novel Edge-based Framework that uses Federated Learning (FL) and blockchain to secure ITS against new emerging threats. In particular, our proposed framework consists of (1) a novel distributed Edge-based architecture that allows multiple Edge nodes to securely collaborate while preserving their privacy; and (2) a decentralized and secure reputation system based on blockchain technology to maintain the reliability and trustworthiness of the FL process within the ITS; This system manages reputation data for individual nodes (such as vehicles), guaranteeing the integrity of the FL training process. Experiment results using the UNSW-NB15 dataset show that our proposed framework achieves high accuracy and F1 score (99%) in detecting new threats while ensuring the privacy and reliability of the whole ITS. These results demonstrate the effectiveness of our proposed framework in securing ITS.

Open RAN (ORAN or O-RAN) revolutionizes Radio Access Networks (RAN) by offering flexibility and cost-efficiency through inter-vendor equipment interoperability. More importantly, it addresses emerging security threats, such as jamming attacks, by incorporating network softwarization and leveraging Artificial Intelligence (AI) techniques. However, AI-based systems face challenges such as limited training data, slow convergence, and vulnerability to dynamic attack patterns like Zero-day attacks. To enhance jamming attack mitigation in O-RAN, Multi-Agent Reinforcement Learning (MARL) has been introduced for improved flexibility and robustness. However, MARL requires data sharing, which consumes network bandwidth and slows down training, and the curse of dimensionality limits its benefits due to the exponential growth of the state-action space. To overcome these limitations, we provide a novel framework that combines federated learning (FL) and deep reinforcement learning (DRL) for efficient jamming attack detection in O-RAN. FL allows decentralized agents to train local models using their data sources, and the models are aggregated into a global model at a Non-real-time RAN Intelligent Controller (RIC) to guide decision-making. The federated learning process enables distributed intelligence, while deep reinforcement learning ensures adaptive and robust jamming attack detection. Our proposed framework improves security, privacy, and resilience in ORAN through collaborative FL and adaptive DRL. Extensive simulations demonstrate its superiority in detection accuracy, resource efficiency, and scalability.

False data injection (FDI) attacks aim to threaten the security of Internet of Things (IoT) systems by falsifying a device's measurements without being detected. In this paper, we propose a process for detecting and predicting FDI attacks, which aims to predict future attacks before they occur and induce IoT devices to behave reliably. First, we propose a novel artificial intelligence (AI)-based detection and prediction module that uses a hidden Markov model (HMM) to observe the behavior of IoT devices and predict their future actions. Next, we design a distributed trust management module that establishes trust between devices using a set of weighted votes. To defend against FDI attacks in communication channels, we formulate a bandwidth optimization problem to meticulously allocate bandwidth to trusted devices. In addition, we propose an efficient incentive mechanism that uses reputation rewards to encourage trustworthy behavior and uses a punishment mechanism to neutralize malicious behavior. Simulations show that the proposed process outperforms recent benchmark FDI attack detection algorithms in the literature in terms of significantly improving attack detection accuracy and reducing attack detection latency.

Wireless Sensor Networks (WSNs) show significant potential through their ability to collect and analyze real-time data, notably enhancing various sectors. The new emerging security threats present a severe risk to the security and reliability of WSNs. Data-driven Artificial Intelligence (AI) leverages WSNs data to deal with new emerging threats like zero-day attacks. However, AI-based models suffer from poor adoption due to the lack of realistic/up-to-date attack data. Recently, Multi-Agent Deep Reinforcement Learning (MARL) has gained significant attention for enhancing Intrusion Detection Systems (IDS) capabilities. MARL offers improved flexibility, efficiency, and robustness. However, this requires data sharing, leading to network bandwidth consumption and slower training. Additionally, the curse of dimensionality hampers its benefits, given the exponential expansion of the state-action space. Privacy-aware collaborative methods such as Federated Learning (FL) emerge as a new approach, enabling decentralized model training across a network of devices while preserving the privacy of each participant. In this context, we introduce a novel framework (MAF-DRL) that leverages FL and MARL to efficiently detect WSN-based attacks. MAF-DRL enables distributed learning across multiple agents with adaptive, flexible, and robust attack detection. We also introduce a trust-based scheduling mechanism that dynamically allocates resources based on agent reliability. This trust-aware approach allows FL systems to adapt to changing network conditions and device behaviors. By prioritizing reliable devices, our method improves the energy efficiency of WSNs and enhances the resilience and effectiveness of the distributed FL paradigm. Finally, we assess the robustness of our framework by testing it against real-world WSN attacks. This evaluation demonstrates its efficiency for secure and communication-efficient federated edge learning across various agents.

The proliferation of consumer networks has increased vulnerabilities to network intrusions, emphasizing the critical need for robust intrusion detection systems (IDS). The data-driven Artificial Intelligence (AI) approach has gained attention for enhancing IDS capabilities to deal with emerging security threats. However, these AI-based IDS face challenges in scalability and privacy preservation. More importantly, they are time-consuming and may perform poorly on high-dimensional and complex data due to the lack of computational resources. To address these shortcomings, in this paper, we introduce a novel framework, called Quantum Federated Learning IDS (QFL-IDS), that merges Quantum Computing (QC) with Federated Learning (FL) to allow for an efficient, robust, and privacy-preserving approach for detecting network intrusions in consumer networks. Leveraging the decentralized nature of FL, QFL-IDS enables multiple consumer devices to collaboratively train a global intrusion detection model while preserving the privacy of individual user data. Furthermore, we leverage the computational power of quantum computing to improve the efficiency of model training and inference processes. We demonstrate the efficacy of our framework through extensive experiments. The obtained results show significant improvements in detection accuracy and computational efficiency compared to the current traditional centralized and federated learning approaches. This makes QFL-IDS a promising framework to cope with the new emerging security threats in a timely and effective manner.

Recent advances in 5G and beyond have further expanded the potential of IoT applications, bringing unprecedented levels of connectivity, speed, and low latency. However, these advancements come with significant security threats that can cause widespread damage. An effective approach to addressing these issues involves the integration of cutting-edge technologies like machine learning (ML), particularly deep reinforcement learning (DRL). DRL is a specialized area of ML that integrates the concepts of deep learning and reinforcement learning to create effective solutions for various tasks. In particular, DRL can facilitate the creation of intelligent security systems that can adapt to dynamic and intricate IoT applications connected to 5G and beyond networks. However, effectively implementing DRL-based intrusion detection frameworks in IoT applications connected to 5G networks poses significant challenges due to bandwidth utilization and device behavior. The data generated by IoT devices is often limited, and malicious behavior may be infrequent, making it difficult to accurately identify and train the algorithm to detect such behavior. Moreover, DRL algorithms pose a significant challenge for IoT devices constrained by limited bandwidth, as communicating large amounts of data required by DRL algorithms can cause network congestion and delay critical communications. In this article, we introduce a novel approach to improving the security of IoT applications in the 5G and beyond era by developing an intrusion detection system that employs DRL algorithms. Our approach involves a distributed Q-learning algorithm that observes the behavior of connected devices and predicts anomalous actions. Additionally, to overcome the challenges associated with bandwidth utilization and device behavior, we introduce a bandwidth allocation problem based on a reputation mechanism that allocates bandwidth to only trustworthy devices. Finally, we evaluate our proposed intrusion detection system on the selected indicators. Numerical results demonstrate that our proposed approach outperforms referenced solutions on the selected indicators.