With the continuous increase in Internet of Things (IoT) device usage, more interest has been shown in internet security, specifically focusing on protecting these vulnerable devices from malicious traffic. Such threats are difficult to distinguish, so an advanced intrusion detection system (IDS) is becoming necessary. Machine learning (ML) is one of the promising techniques as a smart IDS in different areas, including IoT. However, the input to ML models should be extracted from the IoT environment by feature extraction models, which play a significant role in the detection rate and accuracy. Therefore, this research aims to introduce a study on ML-based IDS in IoT, considering different feature extraction algorithms with several ML models. This study evaluated several feature extractors, including image filters and transfer learning models, such as VGG-16 and DenseNet. Additionally, several machine learning algorithms, including random forest, K-nearest neighbors, SVM, and different stacked models were assessed considering all the explored feature extraction algorithms. The study presented a detailed evaluation of all combined models using the IEEE Dataport dataset. Results showed that VGG-16 combined with stacking resulted in the highest accuracy of 98.3%.

The COVID-19 epidemic has caused a large number of human losses and havoc in the economic, social, societal, and health systems around the world. Controlling such epidemic requires understanding its characteristics and behavior, which can be identified by collecting and analyzing the related big data. Big data analytics tools play a vital role in building knowledge required in making decisions and precautionary measures. However, due to the vast amount of data available on COVID-19 from various sources, there is a need to review the roles of big data analysis in controlling the spread of COVID-19, presenting the main challenges and directions of COVID-19 data analysis, as well as providing a framework on the related existing applications and studies to facilitate future research on COVID-19 analysis. Therefore, in this paper, we conduct a literature review to highlight the contributions of several studies in the domain of COVID-19-based big data analysis. The study presents as a taxonomy several applications used to manage and control the pandemic. Moreover, this study discusses several challenges encountered when analyzing COVID-19 data. The findings of this paper suggest valuable future directions to be considered for further research and applications.

In recent years, the digital world has advanced significantly, particularly on the Internet, which is critical given that many of our activities are now conducted online. As a result of attackers’ inventive techniques, the risk of a cyberattack is rising rapidly. One of the most critical attacks is the malicious URL intended to extract unsolicited information by mainly tricking inexperienced end users, resulting in compromising the user’s system and causing losses of billions of dollars each year. As a result, securing websites is becoming more critical. In this paper, we provide an extensive literature review highlighting the main techniques used to detect malicious URLs that are based on machine learning models, taking into consideration the limitations in the literature, detection technologies, feature types, and the datasets used. Moreover, due to the lack of studies related to malicious Arabic website detection, we highlight the directions of studies in this context. Finally, as a result of the analysis that we conducted on the selected studies, we present challenges that might degrade the quality of malicious URL detectors, along with possible solutions.

With the emergence of technology, the usage of IoT (Internet of Things) devices is said to be increasing in people’s lives. Such devices can benefit the average individual, who does not necessarily have to have technical knowledge. The IoT can be found in home security and alarm systems, smart fridges, smart televisions, and more. Although small Internet-connected devices have numerous benefits and can help enhance people’s efficiency, they also can pose a security threat. Malicious actors often attempt to find new ways to exploit and utilize certain resources, and IoT devices are a perfect candidate for such exploitation due to the huge volume of active devices. This is particularly true for Distributed Denial of Service (DDoS) attacks, which involve the exploitation of a massive number of devices, such as IoT devices, to act as bots and send fraudulent requests to services, thus obstructing them. To identify and detect whether such attacks have occurred or not in a network, there must be a reliable mechanism of detection based on adequate techniques. The most common technique for this purpose is artificial intelligence, which involves the use of Machine Learning (ML) and Deep Learning (DL) to help identify cyberattacks. ML models involve algorithms that use structured data to learn from, predict outcomes from, and identify patterns. The goal of this paper is to review selected studies and publications relevant to the topic of DDoS detection in IoT-based networks using machine-learning-relevant publications. It offers a wealth of references for academics looking to define or expand the scope of their research in this area.

The Round Robin algorithm is considered as one of the most common scheduling algorithms due to its simplicity and fairness. It is applied in many fields to improve system performance. In this paper, we conducted a review on how the researchers applied the RR algorithm in the CPU scheduling as well as in the cloud computing environment. Many researchers proposed various techniques in order to improve the RR algorithm and selecting the optimal time quantum which plays a vital role to enhance this algorithm. This paper presents a review and new classification on these techniques. Studies have been classified into two categories: RR based on static quantum, and RR based on dynamic quantum. Furthermore, the second category was divided into dynamic quantum for each round and dynamic quantum for each process. Moreover, an analytical comparison has been provided. This review can be a strong starting point for researchers who seek to enhance or optimize RR algorithm or either apply it in other fields.

Recently, there has been significant growth in the popularity of cloud computing systems. One of the main issues in building cloud computing systems is task scheduling. It plays a critical role in achieving high-level performance and outstanding throughput by having the greatest benefit from the resources. Therefore, enhancing task scheduling algorithms will enhance the QoS, thus leading to more sustainability of cloud computing systems. This paper introduces a novel technique called the dynamic round-robin heuristic algorithm (DRRHA) by utilizing the round-robin algorithm and tuning its time quantum in a dynamic manner based on the mean of the time quantum. Moreover, we applied the remaining burst time of the task as a factor to decide the continuity of executing the task during the current round. The experimental results obtained using the CloudSim Plus tool showed that the DRRHA significantly outperformed the competition in terms of the average waiting time, turnaround time, and response time compared with several studied algorithms, including IRRVQ, dynamic time slice round-robin, improved RR, and SRDQ algorithms.