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The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away fromunauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard. It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security, while maintaining a high-quality reconstruction of images at the receiver side. The proposed framework allows image watermarking based on the Discrete Cosine Transform (DCT) merged with the Singular Value Decomposition (SVD) in the so-called DCT-SVD watermarking. In addition, image encryption is implemented based on chaos and DNA encoding. The encrypted watermarked images are then transmitted through the wireless SC-FDMA system. The linearMinimumMean Square Error (MMSE) equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images. Two subcarrier mapping schemes, namely localized and interleaved schemes, are compared in this paper. The study depends on different channelmodels, namely PedestrianAandVehicularA, with amodulation technique namedQuadratureAmplitude Modulation (QAM). Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images. In addition, different variants of SC-FDMA based on the Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Fast Fourier Transform (FFT) are considered and compared for the image communication task. The simulation results and comparison demonstrate clearly that DWT-SC-FDMAis better suited to the transmission of the digital images in the case of PedestrianAchannels, while the DCT-SC-FDMA is better suited to the transmission of the digital images in the case of Vehicular A channels. [ABSTRACT FROM AUTHOR]

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.

• Designing an orchestrated testbed architecture that comprises IoT and IIoT systems and devices of edge, fog, and cloud layers. • Creating new datasets, the so-called TON_IoT, for evaluating new Artificial Intelligence-based cybersecurity applications. • Generating recent normal and attack events, as well as data features in the new datasets. • Evaluating the network ToN_IoT dataset using various machine learning models to assess its credibility. While there has been a significant interest in understanding the cyber threat landscape of Internet of Things (IoT) networks, and the design of Artificial Intelligence (AI)-based security approaches, there is a lack of distributed architecture led to generating heterogeneous datasets that contain the actual behaviors of real-world IoT networks and complex cyber threat scenarios to evaluate the credibility of the new systems. This paper presents a novel testbed architecture of IoT network which can be used to evaluate Artificial Intelligence (AI)-based security applications. The platform NSX vCloud NFV was employed to facilitate the execution of Software-Defined Network (SDN), Network Function Virtualization (NFV) and Service Orchestration (SO) to offer dynamic testbed networks, which allow the interaction of edge, fog and cloud tiers. While deploying the architecture, real-world normal and attack scenarios are executed to collect labeled datasets. The generated datasets are named 'TON_IoT', as they comprise heterogeneous data sources collected from telemetry datasets of IoT services, Windows and Linux-based datasets, and datasets of network traffic. The TON_IoT network dataset is validated using four machine learning-based intrusion detection algorithms of Gradient Boosting Machine, Random Forest, Naive Bayes, and Deep Neural Networks, revealing a high performance of detection accuracy using the set of training and testing. A comparative summary of the TON_IoT network dataset and other competing network datasets demonstrates its diverse legitimate and anomalous patterns that can be used to better validate new AI-based security solutions. The architecture and datasets can be publicly accessed from TON_IOT Datasets (2020). [ABSTRACT FROM AUTHOR]