Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.

We examine a general wireless sensor network (WSN) model which incorporates a large number of sensors distributed over a large and complex geographical area. The study proposes solutions for a flexible deployment, low cost and high reliability in a wireless sensor network. To achieve these aims, we propose the application of an unmanned aerial vehicle (UAV) as a flying relay to receive and forward signals that employ nonorthogonal multiple access (NOMA) for a high spectral sharing efficiency. To obtain an optimal number of subclusters and optimal UAV positioning, we apply a sensor clustering method based on K-means unsupervised machine learning in combination with the gap statistic method. The study proposes an algorithm to optimize the trajectory of the UAV, i.e., the centroid-to-next-nearest-centroid (CNNC) path. Because a subcluster containing multiple sensors produces cochannel interference which affects the signal decoding performance at the UAV, we propose a diagonal matrix as a phase-shift framework at the UAV to separate and decode the messages received from the sensors. The study examines the outage probability performance of an individual WSN and provides results based on Monte Carlo simulations and analyses. The investigated results verified the benefits of the K-means algorithm in deploying the WSN.

We present the design for an ultra-low latency and low energy Internet of Things (IoT) network inspired by the emerging cooperative Non-Orthogonal Multiple Access (NOMA) wireless communication technique. The IoT network model consists of a source at the center of the network, a near device inside the network, and a far device outside the network. The far device is in the near proximity of the near device, however. We deploy the near device as a relay to assist the far device. The near device is assumed to be a low energy node. As a result, the near device cannot forward signals to the far device through its own power. We therefore design the IoT network to apply the Simultaneous Wireless Information and Power Transfer (SWIPT) technique so that the near device would be able to harvest energy and use it to forward signals. Two cooperative IoT network scenarios are examined: Half-Duplex (HD) and Full-Duplex (FD) relaying, each with and without eavesdroppers. The design also exploits Power Splitting (PS) factors for fairness in Quality of Service (QoS) for the devices. Novel analysis expressions are obtained accuracy and approximation of closed-forms for Outage Probability (OP), secrecy OP, system throughput and Jain's fairness index. The analysis results are proved and verified by Monte Carlo simulation results.

Non-Orthogonal Multiple Access (NOMA) is the key technology promised to be applied in next-generation networks in the near future. In this study, we propose a multi-points cooperative relay (MPCR) NOMA model instead of just using a relay as in previous studies. Based on the channel state information (CSI), the base station (BS) selects a closest user equipment (UE) and sends a superposed signal to this UE as a first relay node. We have assumed that there are N UEs in the network and the N-th UE, which is farthest from BS, has the poorest quality signal transmitted from the BS compared the other UEs. The N-th UE received a forwarded signal from $N-1$ relaying nodes that are the UEs with better signal quality. At the i-th relaying node, it detects its own symbol by using successive interference cancellation (SIC) and will forward the superimposed signal to the next closest user, namely the $(i+1)$ -th UE, and include an excess power which will use for energy harvesting (EH) intention at the next UE. By these, the farthest UE in network can be significantly improved. In addition, closed-form expressions of outage probability for users over both the Rayleigh and Nakagami-m fading channels are also presented. Analysis and simulation results performed by Matlab software, which are presented accurately and clearly, show that the effectiveness of our proposed model and this model will be consistent with the multi-access wireless network in the future.

This study examined how to improve system performance by equipping multiple antennae at a base station (BS) and all terminal users/mobile devices instead of a single antenna as in previous studies. Experimental investigations based on three NOMA down-link models involved (1) a single-input-single-output (SISO) scenario in which a single antenna was equipped at a BS and for all users, (2) a multi-input-single-output (MISO) scenario in which multiple transmitter antennae were equipped at a BS and a single receiver antenna for all users and (3) a multi-input-multi-output (MIMO) scenario in which multiple transmitter antennae were equipped at a BS and multiple receiver antenna for all users. This study investigated and compared the outage probability (OP) and system throughput assuming all users were over Rayleigh fading channels. The individual scenarios also each had an eavesdropper. Secure system performance of the individual scenarios was therefore also investigated. In order to detect data from superimposed signals, successive interference cancellation (SIC) was deployed for users, taking into account perfect, imperfect and fully imperfect SICs. The results of analysis of users in these three scenarios were obtained in an approximate closed form by using the Gaussian-Chebyshev quadrature method. However, the clearly and accurately presented results obtained using Monte Carlo simulations prove and verify that the MIMO-NOMA scenario equipped with multiple antennae significantly improved system performance.

In long-range wireless communication networks, the fading channels described in channel state information are strongly related to distance and the path loss exponent and represent a major challenge in delivering the performance required to support emerging applications. Conveniently, multiple antennas and cooperative relays are efficient solutions that can combat fading channels, thereby improving networking capacity and transmission reliability. This study investigated the use of multi-antenna unmanned aerial vehicle (UAV)s as aerial Internet of Things (IoT) relays and employed their direct line-of-sight benefits to assist IoT wireless networks. To improve the outage probability, system throughput, and energy efficiency (EE), we first considered a combination of transmit antenna selection at the transmitter and the selection combining technique at the receiver to determine the best channel from the pre-coding channel matrix. Then, based on a practical model in a three-dimensional earth environment and thanks to the K-means algorithm, we investigated optimal UAV placement to obtain optimal channel state information for the non-orthogonal multiple access (NOMA)-IoT device cluster globally, thereby ensuring the quality of service for the IoT devices. We introduced a max-successive interference cancellation-min-rate framework for non-ordered NOMA devices, thus deriving theoretical expressions in novel closed forms for two independent scenarios: ( i ) Rayleigh and ( ii ) Nakagami- m fading channels. By optimizing the UAV placement, the investigated results applied to the UAV scheme delivered a better performance in a NOMA-IoT network than in a terrestrial relay (TR) scheme. Finally, this study examines a variety of models and presents algorithms for Monte Carlo simulations to verify the theoretical results.