Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

A Survey on the Applications of Frontier AI, Foundation Models, and Large Language Models to Intelligent Transportation Systems

This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing transportation intelligence, optimizing traffic management, and contributing to the realization of smart cities. Frontier AI refers to the forefront of AI technology, encompassing the latest advancements, innovations, and experimental techniques in the field, especially AI foundation models and LLMs. Foundation models, like GPT-4, are large, general-purpose AI models that provide a base for a wide range of applications. They are characterized by their versatility and scalability. LLMs are obtained from finetuning foundation models with a specific focus on processing and generating natural language. They excel in tasks like language understanding, text generation, translation, and summarization. By leveraging vast textual data, including traffic reports and social media interactions, LLMs extract critical insights, fostering the evolution of ITS. The survey navigates the dynamic synergy between LLMs and ITS, delving into applications in traffic management, integration into autonomous vehicles, and their role in shaping smart cities. It provides insights into ongoing research, innovations, and emerging trends, aiming to inspire collaboration at the intersection of language, intelligence, and mobility for safer, more efficient, and sustainable transportation systems. The paper further surveys interactions between LLMs and various aspects of ITS, exploring roles in traffic management, facilitating autonomous vehicles, and contributing to smart city development, while addressing challenges brought by frontier AI and foundation models. This paper offers valuable inspiration for future research and innovation in the transformative domain of intelligent transportation.Comment: This paper appears in International Conference on Computer and Applications (ICCA) 202

Deep Learning-Based Intrusion Detection Methods for Computer Networks and Privacy-Preserving Authentication Method for Vehicular Ad Hoc Networks

The incidence of computer network intrusions has significantly increased over the last decade, partially attributed to a thriving underground cyber-crime economy and the widespread availability of advanced tools for launching such attacks. To counter these attacks, researchers in both academia and industry have turned to machine learning (ML) techniques to develop Intrusion Detection Systems (IDSes) for computer networks. However, many of the datasets use to train ML classifiers for detecting intrusions are not balanced, with some classes having fewer samples than others. This can result in ML classifiers producing suboptimal results. In this dissertation, we address this issue and present better ML based solutions for intrusion detection. Our contributions in this direction can be summarized as follows: Balancing Data Using Synthetic Data to detect intrusions in Computer Networks: In the past, researchers addressed the issue of imbalanced data in datasets by using over-sampling and under-sampling techniques. In this study, we go beyond such traditional methods and utilize a synthetic data generation method called Con- ditional Generative Adversarial Network (CTGAN) to balance the datasets and in- vestigate its impact on the performance of widely used ML classifiers. To the best of our knowledge, no one else has used CTGAN to generate synthetic samples for balancing intrusion detection datasets. We use two widely used publicly available datasets and conduct extensive experiments and show that ML classifiers trained on these datasets balanced with synthetic samples generated by CTGAN have higher prediction accuracy and Matthew Correlation Coefficient (MCC) scores than those trained on imbalanced datasets by 8% and 13%, respectively. Deep Learning approach for intrusion detection using focal loss function: To overcome the data imbalance problem for intrusion detection, we leverage the specialized loss function, called focal loss, that automatically down-weighs easy ex- amples and focuses on the hard negatives by facilitating dynamically scaled-gradient updates for training ML models effectively. We implement our approach using two well-known Deep Learning (DL) neural network architectures. Compared to training DL models using cross-entropy loss function, our approach (training DL models using focal loss function) improved accuracy, precision, F1 score, and MCC score by 24%, 39%, 39%, and 60% respectively. Efficient Deep Learning approach to detect Intrusions using Few-shot Learning: To address the issue of imbalance the datasets and develop a highly effective IDS, we utilize the concept of few-shot learning. We present a Few-Shot and Self-Supervised learning framework, called FS3, for detecting intrusions in IoT networks. FS3 works in three phases. Our approach involves first pretraining an encoder on a large-scale external dataset in a selfsupervised manner. We then employ few-shot learning (FSL), which seeks to replicate the encoder’s ability to learn new patterns from only a few training examples. During the encoder training us- ing a small number of samples, we train them contrastively, utilizing the triplet loss function. The third phase introduces a novel K-Nearest neighbor algorithm that sub- samples the majority class instances to further reduce imbalance and improve overall performance. Our proposed framework FS3, utilizing only 20% of labeled data, out- performs fully supervised state-of-the-art models by up to 42.39% and 43.95% with respect to the metrics precision and F1 score, respectively. The rapid evolution of the automotive industry and advancements in wireless com- munication technologies will result in the widespread deployment of Vehicular ad hoc networks (VANETs). However, despite the network’s potential to enable intelligent and autonomous driving, it also introduces various attack vectors that can jeopardize its security. In this dissertation, we present efficient privacy-preserving authenticated message dissemination scheme in VANETs. Conditional Privacy-preserving Authentication and Message Dissemination Scheme using Timestamp based Pseudonyms: To authenticate a message sent by a vehicle using its pseudonym, a certificate of the pseudonym signed by the central authority is generally utilized. If a vehicle is found to be malicious, certificates associated with all the pseudonyms assigned to it must be revoked. Certificate revocation lists (CRLs) should be shared with all entities that will be corresponding with the vehicle. As each vehicle has a large pool of pseudonyms allocated to it, the CRL can quickly grow in size as the number of revoked vehicles increases. This results in high storage overheads for storing the CRL, and significant authentication overheads as the receivers must check their CRL for each message received to verify its pseudonym. To address this issue, we present a timestamp-based pseudonym allocation scheme that reduces the storage overhead and authentication overhead by streamlining the CRL management process

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

Deep Learning Based Anomaly Detection for Fog-Assisted IoVs Network

Internet of vehicles (IoVs) allows millions of vehicles to be connected and share information for various purposes. The main applications of IoVs are traffic management, emergency messages delivery, E-health, traffic, and temperature monitoring. On the other hand, IoVs lack in location awareness and geographic distribution, which is critical for some IoVs applications such as smart traffic lights and information sharing in vehicles. To support these topographies, fog computing was proposed as an appealing and novel term, which was integrated with IoVs to extend storage, computation, and networking. Unfortunately, it is also challenged with various security and privacy hazards, which is a serious concern of smart cities. Therefore, we can formulate that Fog-assisted IoVs (Fa-IoVs), are challenged by security threats during information dissemination among mobile nodes. These security threats of Fa-IoVs are considered as anomalies which is a serious concern that needs to be addressed for smooth Fa-IoVs network communication. Here, smooth communication refers to less risk of important data loss, delay, communication overhead, etc. This research work aims to identify research gaps in the Fa-IoVs network and present a deep learning-based dynamic scheme named CAaDet (Convolutional autoencoder Aided anomaly detection) to detect anomalies. CAaDet exploits convolutional layers with a customized autoencoder for useful feature extraction and anomaly detection. Performance evaluation of the proposed scheme is done by using the F1-score metric where experiments are carried out by exploiting a benchmark dataset named NSL-KDD. CAaDet also observes the behavior of fog nodes and hidden neurons and selects the best match to reduce false alarms and improve F1-score. The proposed scheme achieved significant improvement over existing schemes for anomaly detection. Identified research gaps in Fa-IoVs can give future directions to researchers and attract more attention to this new era