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The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures. As the frequency and diversity of cybersecurity attacks continue to rise, the importance of incident detection has significantly increased. IoT devices are expanding rapidly, resulting in a growing need for efficient techniques to autonomously identify network-based attacks in IoT networks with both high precision and minimal computational requirements. This paper presents SecurityBERT, a novel architecture that leverages the Bidirectional Encoder Representations from Transformers (BERT) model for cyber threat detection in IoT networks. During the training of SecurityBERT, we incorporated a novel privacy-preserving encoding technique called Privacy-Preserving Fixed-Length Encoding (PPFLE). We effectively represented network traffic data in a structured format by combining PPFLE with the Byte-level Byte-Pair Encoder (BBPE) Tokenizer. Our research demonstrates that SecurityBERT outperforms traditional Machine Learning (ML) and Deep Learning (DL) methods, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), in cyber threat detection. Employing the Edge-IIoTset cybersecurity dataset, our experimental analysis shows that SecurityBERT achieved an impressive 98.2% overall accuracy in identifying fourteen distinct attack types, surpassing previous records set by hybrid solutions such as GAN-Transformer-based architectures and CNN-LSTM models. With an inference time of less than 0.15 seconds on an average CPU and a compact model size of just 16.7MB, SecurityBERT is ideally suited for real-life traffic analysis and a suitable choice for deployment on resource-constrained IoT devices.

Fifth-generation and Beyond (5GB) networks are transformational technologies to revolutionize future wireless communications in terms of massive connectivity, higher capacity, lower latency, and ultra-high reliability. To this end, 5GB networks are designed as a coalescence of various schemes and enabling technologies such as unmanned aerial vehicles (UAV)-assisted networks, vehicular networks, heterogeneous cellular networks (HCNs), Internet of Things (IoT), device-to-device (D2D) communication, millimeter-wave (mm-wave), massive multiple-input multiple-output (mMIMO), non-orthogonal multiple access (NOMA), re-configurable intelligent surface (RIS) and Terahertz (THz) communications. Due to the scarcity of licensed bands and the co-existence of multiple technologies in unlicensed bands, interference management is a pivotal factor in enhancing the user experience and quality of service (QoS) in future-generation networks. However, due to the highly complex scenarios, conventional interference mitigation techniques may not be suitable in 5GB networks. To cope with this, researchers have investigated artificial intelligence (AI)-based interference management techniques to tackle complex environments. Existing surveys either focus on conventional interference management methods or AI-based interference management only for a specific scheme or technology. This survey article complements the existing survey literature by providing a detailed review of AI-based intentional-interference management such as jamming detection and mitigation, and AI-enabled unintentional-interference mitigation techniques from the standpoints of UAV-assisted networks, vehicular networks, HCNs, D2D, IoT, mmWave-MIMO, NOMA, and THz communications. While identifying and presenting the AI-based techniques for interference management in 5G and beyond networks, this article also points out the challenges, open issues, and future research directions to adopt AI-enabled techniques to curtail the effects of interference in 5GB and towards 6G networks.

The integration of Large Language Models (LLMs) and Edge Intelligence (EI) introduces a groundbreaking paradigm for intelligent edge devices. With their capacity for human-like language processing and generation, LLMs empower edge computing with a powerful set of tools, paving the way for a new era of decentralized intelligence. Yet, a notable research gap exists in obtaining a thorough comprehension of LLM-based EI architectures, which should incorporate crucial elements such as security, optimization, and responsible development. This survey aims to bridge this gap by providing a comprehensive resource for both researchers and practitioners. We explore LLM-based EI architectures in-depth, carefully analyzing state-of-the-art paradigms and design decisions. To facilitate efficient and scalable edge deployments, we perform a comparative analysis of recent optimization and autonomy techniques specifically designed for resource-constrained edge environments. Additionally, we shed light on the extensive potential of LLM-based EI by demonstrating its varied practical applications across a wide range of domains. Acknowledging the utmost importance of security, our survey thoroughly investigates potential vulnerabilities inherent in LLM-based EI deployments. We explore corresponding defense mechanisms to protect the integrity and confidentiality of data processed at the edge. In conclusion, highlighting the essential aspect of trustworthiness, we outline best practices and guiding principles for the responsible development and deployment of these systems. By conducting a comprehensive review of these key components, our survey aims to support the ethical development and strategic implementation of LLM-driven EI, paving the way for its transformative impact on diverse applications.

Organizations constantly exposed to cyber threats are compelled to comply with cyber security standards and policies for protecting their digital assets. Vulnerability assessment (VA) and penetration testing (PT) are widely adopted methods for security compliance (SC) to identify security gaps and anticipate security breaches. However, these methods for security compliance tend to be highly repetitive and resource-intensive. In this paper, we propose a novel method to tackle the ever-growing problem of efficiency in network security auditing by designing and developing an Expert-System Automated Security Compliance Framework (ESASCF). ESASCF enables industrial and open-source VA and PT tools to extract, process, store and re-use the expertise in similar scenarios or during periodic re-testing. ESASCF was tested on different size networks and proved efficient in terms of time efficiency and testing effectiveness. ESASCF takes over autonomously the SC in re-testing and offloading the human expert by automating repeated segments SC and thus enabling experts to prioritize important tasks in ad-hoc compliance tests. The obtained results show a performance improvement by cutting the time required for an expert to 50% in the context of typical corporate networks’ first security compliance and 20% in re-testing. In addition, the framework allows a long-term impact illustrated in the knowledge extraction, generalization, and re-utilization, which enables better SC confidence independent of the human expert skills, coverage, and wrong decisions resulting in false negatives.

In this paper, we propose a new comprehensive realistic cyber security dataset of IoT and IIoT applications, called Edge-IIoTset, which can be used by machine learning-based intrusion detection systems in two different modes, namely, centralized and federated learning. Specifically, the dataset has been generated using a purpose-built IoT/IIoT testbed with a large representative set of devices, sensors, protocols and cloud/edge configurations. The IoT data are generated from various IoT devices (more than 10 types) such as Low-cost digital sensors for sensing temperature and humidity, Ultrasonic sensor, Water level detection sensor, pH Sensor Meter, Soil Moisture sensor, Heart Rate Sensor, Flame Sensor, etc.). Furthermore, we identify and analyze fourteen attacks related to IoT and IIoT connectivity protocols, which are categorized into five threats, including, DoS/DDoS attacks, Information gathering, Man in the middle attacks, Injection attacks, and Malware attacks. In addition, we extract features obtained from different sources, including alerts, system resources, logs, network traffic, and propose new 61 features with high correlations from 1176 found features. After processing and analyzing the proposed realistic cyber security dataset, we provide a primary exploratory data analysis and evaluate the performance of machine learning approaches (i.e., traditional machine learning as well as deep learning) in both centralized and federated learning modes. The Edge-IIoTset dataset can be publicly accessed from http://ieee-dataport.org/8939.

Resistance to differential cryptanalysis is a fundamental security requirement for symmetric block ciphers, and recently, deep learning has attracted the interest of cryptography experts, particularly in the field of block cipher cryptanalysis, where the bulk of these studies are differential distinguisher based black-box attacks. This paper provides a deep learning-based decryptor for investigating the permutation primitives used in multimedia block cipher encryption algorithms.We aim to investigate how deep learning can be used to improve on previous classical works by employing ciphertext pair aspects to maximize information extraction with low-data constraints by using convolution neural network features to discover the correlation among permutable atoms to extract the plaintext from the ciphered text without any P-box expertise. The evaluation of testing methods has been conceptualized as a regression task in which neural networks are supervised using a variety of parameters such as variations between input and output, number of iterations, and P-box generation patterns. On the other hand, the transfer learning skills demonstrated in this study indicate that discovering suitable testing models from the ground is also achievable using our model with optimum prior cryptographic expertise, where we contribute the results of deep learning in the field of deep learning based differential cryptanalysis development.Various experiments were performed on discrete and continuous chaotic and non-chaotic permutation patterns, and the best-performing model had an MSE of $1.8217{e}^{-04}$ and an $R^{2}$ of 1, demonstrating the practicality of the suggested technique.

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.

Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.