Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm Article Swipe
Pravin R. Kshirsagar
,
Hariprasath Manoharan
,
Hassan A. Alterazi
,
Nawaf Alhebaishi
,
Osama Rabie
,
Shitharth Selvarajan
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/9386989
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1155/2022/9386989
Cloud services are a popular concept used to describe how internet-based services are delivered and maintained. The computer technology environment is being restructured with respect to information preservation. Data protection is of critical importance when storing huge volumes of information. In today’s cyber world, an intrusion is a significant security problem. Services, information, and services are all vulnerable to attack in the cloud due to its distributed structure of the cloud. Inappropriate behavior in the connection and in the host is detected using intrusion detection systems (IDS) in the cloud. DDoS attacks are difficult to protect against since they produce massive volumes of harmful information on the network. This assault forces the cloud services to become unavailable to target consumers, which depletes computer resources and leaves the provider exposed to massive financial and reputational losses. Cyber-analyst data mining techniques may assist in intrusion detection. Machine learning techniques are used to create many strategies. Attribute selection techniques are also vital in keeping the dataset’s dimensionality low. In this study, one method is provided, and the dataset is taken from the NSL-KDD dataset. In the first strategy, a filtering method called learning vector quantization (LVQ) is used, and in the second strategy, a dimensionality-simplifying method called PCA. The selected attributes from each technique are used for categorization before being tested against a DoS attack. This recent study shows that an LVQ-based SVM performs better than the competition in detecting threats.
Related Topics
Concepts
Computer science
Cloud computing
Intrusion detection system
Learning vector quantization
Machine learning
Data mining
Backup
Denial-of-service attack
Support vector machine
Artificial intelligence
Computer security
The Internet
Algorithm
Artificial neural network
World Wide Web
Database
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/2022/9386989
- https://downloads.hindawi.com/journals/js/2022/9386989.pdf
- OA Status
- hybrid
- Cited By
- 14
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4292014285
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4292014285Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/2022/9386989Digital Object Identifier
- Title
-
Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning AlgorithmWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-08-16Full publication date if available
- Authors
-
Pravin R. Kshirsagar, Hariprasath Manoharan, Hassan A. Alterazi, Nawaf Alhebaishi, Osama Rabie, Shitharth SelvarajanList of authors in order
- Landing page
-
https://doi.org/10.1155/2022/9386989Publisher landing page
- PDF URL
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https://downloads.hindawi.com/journals/js/2022/9386989.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://downloads.hindawi.com/journals/js/2022/9386989.pdfDirect OA link when available
- Concepts
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Computer science, Cloud computing, Intrusion detection system, Learning vector quantization, Machine learning, Data mining, Backup, Denial-of-service attack, Support vector machine, Artificial intelligence, Computer security, The Internet, Algorithm, Artificial neural network, World Wide Web, Database, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 5, 2023: 4, 2022: 3Per-year citation counts (last 5 years)
- References (count)
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23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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