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  1. Article ; Online: A Systematic Literature Review on Machine and Deep Learning Approaches for Detecting Attacks in RPL-Based 6LoWPAN of Internet of Things.

    Al-Amiedy, Taief Alaa / Anbar, Mohammed / Belaton, Bahari / Kabla, Arkan Hammoodi Hasan / Hasbullah, Iznan H / Alashhab, Ziyad R

    Sensors (Basel, Switzerland)

    2022  Volume 22, Issue 9

    Abstract: 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 ...

    Abstract 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
    MeSH term(s) Deep Learning ; Internet of Things ; Publications
    Language English
    Publishing date 2022-04-29
    Publishing country Switzerland
    Document type Journal Article ; Review ; Systematic Review
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22093400
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Distributed Denial of Service Attacks against Cloud Computing Environment

    Ziyad R. Alashhab / Mohammed Anbar / Manmeet Mahinderjit Singh / Iznan H. Hasbullah / Prateek Jain / Taief Alaa Al-Amiedy

    Applied Sciences, Vol 12, Iss 12441, p

    Survey, Issues, Challenges and Coherent Taxonomy

    2022  Volume 12441

    Abstract: 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 ... ...

    Abstract 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.
    Keywords cyber-threats ; cloud computing (CC) ; cloud security ; distributed denial of service (DDoS) attacks ; cybersecurity ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
    Subject code 303
    Language English
    Publishing date 2022-12-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Impact of Coronavirus Pandemic Crisis on Technologies and Cloud Computing Applications

    Alashhab, Ziyad R. / Anbar, Mohammed / Singh, Manmeet Mahinderjit / Leau, Yu-Beng / Al-Sai, Zaher Ali / Alhayja’a, Sami Abu

    Abstract: 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 ...

    Abstract 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.
    Keywords covid19
    Publisher Elsevier; PMC
    Document type Article ; Online
    DOI 10.1016/j.jnlest.2020.100059
    Database COVID19

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