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RISS 인기검색어
- 검색키워드
- 저자 : Areej Alotaibi (검색결과 2 건)
- 무료
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- 유료
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Role of Machine Learning in Intrusion Detection System: A Systematic Review
Alhasani, Areej,Al omrani, Faten,Alzahrani, Taghreed,alFahhad, Rehab,Alotaibi, Mohamed International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.3
Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.
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Performance Evaluation of SDN Controllers: RYU and POX for WBAN-based Healthcare Applications
Lama Alfaify,Nujud Alnajem,Haya Alanzi,Rawan Almutiri,Areej Alotaibi,Nourah Alhazri,Awatif Alqahtani International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7
Wireless Body Area Networks (WBANs) have made it easier for healthcare workers and patients to monitor patients' status continuously in real time. WBANs have complex and diverse network structures; thus, management and control can be challenging. Therefore, considering emerging Software-defined networks (SDN) with WBANs is a promising technology since SDN implements a new network management and design approach. The SDN concept is used in this study to create more adaptable and dynamic network architectures for WBANs. The study focuses on comparing the performance of two SDN controllers, POX and Ryu, using Mininet, an open-source simulation tool, to construct network topologies. The performance of the controllers is evaluated based on bandwidth, throughput, and round-trip time metrics for networks using an OpenFlow switch with sixteen nodes and a controller for each topology. The study finds that the choice of network controller can significantly impact network performance and suggests that monitoring network performance indicators is crucial for optimizing network performance. The project provides valuable insights into the performance of SDN-based WBANs using POX and Ryu controllers and highlights the importance of selecting the appropriate network controller for a given network architecture.
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