Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people’s daily lives. However, IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. This research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories: normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes the following models: shallow neural networks (SNN), decision trees (DT), bagging trees (BT), k-nearest neighbor (kNN), and support vector machine (SVM). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was used to improve the learning models’ accuracy. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4–99.9% recorded for the classification process.

Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using human power to monitor them for special security purposes. Abnormal events deviate from normal patterns and are considered rare. Furthermore, collecting or producing data on these rare events and modeling abnormal data are difficult. Therefore, there is a need to develop an intelligent approach to overcome this challenge. Many research studies have been conducted on detecting abnormal events using machine learning and deep learning techniques. This study focused on abnormal event detection, particularly for video surveillance applications, and included an up-to-date state-of-the-art that extends previous related works. The major objective of this survey was to examine the existing machine learning and deep learning techniques in the literature and the datasets used to detect abnormal events in surveillance videos to show their advantages and disadvantages and summarize the literature studies, highlighting the major challenges.

In wireless personal area networks (WPANs), devices can communicate with each other without relying on a central router or access point. They can improve performance and efficiency by allowing devices to share resources directly; however, managing resource allocation and optimizing communication between devices can be challenging. This paper proposes an intelligent load-based resource optimization model to enhance the performance of device-to-device communication in 5G-WPAN. Intelligent load-based resource optimization in device-to-device communication is a strategy used to maximize the efficiency and effectiveness of resource usage in device-to-device (D2D) communications. This optimization strategy is based on optimizing the network’s resource load by managing resource utilization and ensuring that the network is not overloaded. It is achieved by monitoring the current load on the network and then adjusting the usage of resources, such as bandwidth and power, to optimize the overall performance. This type of optimization is essential in D2D communication since it can help reduce costs and improve the system’s performance. The proposed model has achieved 86.00% network efficiency, 93.74% throughput, 91.94% reduced latency, and 92.85% scalability.

This research focuses on addressing the privacy issues in healthcare advancement monitoring with the rapid establishment of the decentralised communication system in the Internet of Medical Things (IoMT). An integrated blockchain homomorphic encryption standard with an in‐build supervised learning‐based smart contract is designed to improvise personal data prevention. The Internet of Medical Things (IoMT) has advanced in healthcare with the rapid establishment of decentralised communication systems. Distributed ledgers have resource constraints to leverage public, private, and hybrid blockchain transactions to facilitate heterogeneous operations. The authors propose a supervised learning strategy in healthcare to mitigate learning health‐related issues, improvise clinical monitoring, and ensure secure communication. The proposed approach handles the vast IoMT data by adopting blockchain for IoMT as (BIoMT) to preserve sensitive clinical information. It incorporates hybrid encryption techniques to improve patient and health records' privacy protection. BIoMT also maintains secured and sustainable supply chain management with a highly confidential decentralised framework using blockchain‐based smart contracts, which minimises data loss. Moreover, a framework is designed with a hybrid hashing that integrates a homomorphically encrypted algorithm to support a smart contract for decentralised applicability. The BIoMT approach is tested and compared with the relevant prevention mechanisms. The evaluation shows that the effects observed from the result analysis noted that the proposed method outperforms reliable prevention mechanisms compared to the existing approaches.

In the recent past, the level of cyber threats has changed drastically, leading to the current transformation of the cybersecurity landscape. For example, emerging threats like Zero-day and polymorphic malware cannot be detected by conventional detection methods like heuristic and signature-based methods, which have proven useful in the identification of malware. In view of this shift in the cybersecurity paradigm, this study proposes to discuss the utilization of transformer models to improve malware detection effectiveness and the accuracy and efficiency in detecting malicious software. In this regard, this study adopts the application of transformers in identifying different forms of malicious software: ransomware, spyware, and trojans. Transformers are endowed with the ability to handle sequential data and capture intricate patterns. By employing deep learning techniques and conducting thorough contextual analysis, these models enhance the detection process by identifying subtle indications of compromise, which traditional methods may overlook. This research also explains the challenges and limitations related to the application of transformer-based models in real-world cybersecurity settings, which include computing requirements and large-scale labeled datasets’ requirements. By the end, the article suggests potential future research avenues in order to improve and integrate these models into cybersecurity systems.

The Bitcoin cryptocurrency is a worldwide prevalent virtualized digital currency conceptualized in 2008 as a distributed transactions system. Bitcoin transactions make use of peer-to-peer network nodes without a third-party intermediary, and the transactions can be verified by the node. Although Bitcoin networks have exhibited high efficiency in the financial transaction systems, their payment transactions are vulnerable to several ransomware attacks. For that reason, investigators have been working on developing ransomware payment identification techniques for bitcoin transactions’ networks to prevent such harmful cyberattacks. In this paper, we propose a high performance Bitcoin transaction predictive system that investigates the Bitcoin payment transactions to learn data patterns that can recognize and classify ransomware payments for heterogeneous bitcoin networks. Specifically, our system makes use of two supervised machine learning methods to learn the distinguishing patterns in Bitcoin payment transactions, namely, shallow neural networks (SNN) and optimizable decision trees (ODT). To validate the effectiveness of our solution approach, we evaluate our machine learning based predictive models on a recent Bitcoin transactions dataset in terms of classification accuracy as a key performance indicator and other key evaluation metrics such as the confusion matrix, positive predictive value, true positive rate, and the corresponding prediction errors. As a result, our superlative experimental result was registered to the model-based decision trees scoring 99.9% and 99.4% classification detection (two-class classifier) and accuracy (multiclass classifier), respectively. Hence, the obtained model accuracy results are superior as they surpassed many state-of-the-art models developed to identify ransomware payments in bitcoin transactions.