In light of the COVID-19 outbreak caused by the novel coronavirus, companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion. Employees, however, have been exposed to different security risks because of working from home. Moreover, the rapid global spread of COVID-19 has increased the volume of data generated from various sources. Working from home depends mainly on cloud computing (CC) applications that help employees to efficiently accomplish their tasks. The cloud computing environment (CCE) is an unsung hero in the COVID-19 pandemic crisis. It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data. Despite the increase in the use of CC applications, there is an ongoing research challenge in the domains of CCE concerning data, guaranteeing security, and the availability of CC applications. This paper, to the best of our knowledge, is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE. Additionally, this paper also highlights the security risks of working from home during the COVID-19 pandemic.

The IETF Routing Over Low power and Lossy network (ROLL) working group defined IPv6 Routing Protocol for Low Power and Lossy Network (RPL) to facilitate efficient routing in IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN). Limited resources of 6LoWPAN nodes make it challenging to secure the environment, leaving it vulnerable to threats and security attacks. Machine Learning (ML) and Deep Learning (DL) approaches have shown promise as effective and efficient mechanisms for detecting anomalous behaviors in RPL-based 6LoWPAN. Therefore, this paper systematically reviews and critically analyzes the research landscape on ML, DL, and combined ML-DL approaches applied to detect attacks in RPL networks. In addition, this study examined existing datasets designed explicitly for the RPL network. This work collects relevant studies from five major databases: Google Scholar, Springer Link, Scopus, Science Direct, and IEEE Xplore® digital library. Furthermore, 15,543 studies, retrieved from January 2016 to mid-2021, were refined according to the assigned inclusion criteria and designed research questions resulting in 49 studies. Finally, a conclusive discussion highlights the issues and challenges in the existing studies and proposes several future research directions.

Cloud computing (CC) plays a significant role in revolutionizing the information and communication technology (ICT) industry, allowing flexible delivery of new services and computing resources at a fraction of the costs for end-users than traditional computing. Unfortunately, many potential cyber threats impact CC-deployed services due to the exploitation of CC’s characteristics, such as resource sharing, elasticity, and multi-tenancy. This survey provides a comprehensive discussion on security issues and challenges facing CC for cloud service providers and their users. Furthermore, this survey proposes a new taxonomy for classifying CC attacks, distributed denial of service (DDoS) attacks, and DDoS attack detection approaches on CC. It also provides a qualitative comparison with the existing surveys. Finally, this survey aims to serve as a guide and reference for other researchers working on new DDoS attack detection approaches within the CC environment.

Internet Protocol version six (IPv6) is equipped with new protocols, such as the Neighbor Discovery Protocol (NDP). NDP is a stateless protocol without authentication that makes it vulnerable to many types of attacks, such as Router Advertisement (RA) and Neighbour Solicitation (NS) DoS flooding attacks. In these types of attacks, attackers send an enormous volume of abnormal NDP traffic, which causes congestion that degrades network performance. The expected behavior among these attacks is the existence of NDP traffic abnormalities. Thus, this research aims to propose a flow-based approach to detect abnormal NDP traffic behavior, which is considered an indicator of the presence of NDP-based attacks, such as RA and NS DoS flooding attacks. Also, the proposed approach relies on flow-based network traffic representation and adoption of the Entropy algorithm to detect the randomness in the network traffic. The proposed approach is evaluated in terms of detection accuracy, precision, recall, and F1-Score using a simulated dataset. The experimental result shows that the proposed approach obtained 98.1%, 55%, 100%, and 70.96% for average accuracy, precision, recall, and F1-Score, respectively, in detecting abnormal NDP traffic behavior caused by the RA DoS flooding attack. Meanwhile, the proposed approach obtained 99%, 91.3%, 100%, and 95.45% for average accuracy, precision, recall, and F1-Score, respectively, in detecting the abnormal NDP traffic behavior caused by the NS DoS flooding attack. Also, the proposed approach shows better results compared to other existing approaches.

The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks.