Experimentations with OpenStack System Logs and Support Vector Machine for an Anomaly Detection Model in a Private Cloud Infrastructure

Akanle, Matthew B.; Adetiba, Emmanuel; Akande, Victor; Akinrinmade, Adekunle; Ajala, Sunday; Moninuola, Funmilayo; Badejo, Joke A.; Adebiyi, Ezekiel

doi:10.1109/icabcd49160.2020.9183878

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…Similar to [ 108 ], the article [ 109 ] describes the experiments to identify anomalies in the system logs of OpenStack. For this, a SVM with different cores is used.…”

Section: Systematization Of Sa Stages and ML Solutionsmentioning

confidence: 99%

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Kotenko

Izrailov

Buinevich

2022

Sensors

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Ensuring security for modern IoT systems requires the use of complex methods to analyze their software. One of the most in-demand methods that has repeatedly been proven to be effective is static analysis. However, the progressive complication of the connections in IoT systems, the increase in their scale, and the heterogeneity of elements requires the automation and intellectualization of manual experts’ work. A hypothesis to this end is posed that assumes the applicability of machine-learning solutions for IoT system static analysis. A scheme of this research, which is aimed at confirming the hypothesis and reflecting the ontology of the study, is given. The main contributions to the work are as follows: systematization of static analysis stages for IoT systems and decisions of machine-learning problems in the form of formalized models; review of the entire subject area publications with analysis of the results; confirmation of the machine-learning instrumentaries applicability for each static analysis stage; and the proposal of an intelligent framework concept for the static analysis of IoT systems. The novelty of the results obtained is a consideration of the entire process of static analysis (from the beginning of IoT system research to the final delivery of the results), consideration of each stage from the entirely given set of machine-learning solutions perspective, as well as formalization of the stages and solutions in the form of “Form and Content” data transformations.

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“…Similar to [ 108 ], the article [ 109 ] describes the experiments to identify anomalies in the system logs of OpenStack. For this, a SVM with different cores is used.…”

Section: Systematization Of Sa Stages and ML Solutionsmentioning

confidence: 99%

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Kotenko

Izrailov

Buinevich

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The advancement in image processing, pattern recognition and computer vision has brought about the upsurge in the use of deep neural networks in the field of image classification. Relatively, a lot of shallow neural network classifiers have been reported to pose problems of data overfitting, which greatly affects how objects or images are classified (Janani and Gopal, 2013;Sweetwilliams et al, 2019;Alaba et al, 2020;Akanle et al, 2020), hence, the advent of CNN to mitigate the vanishing gradient problem. A CNN is a supervised deep learning method that employs the use of convolution mathematics, which is the process of implementing a 2-D convolution with a filter on an input image, in at least one of the network layers.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

Ajayi¹,

Kala²,

Ajala³

et al. 2021

Journal of Computer Science

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Experimentations with OpenStack System Logs and Support Vector Machine for an Anomaly Detection Model in a Private Cloud Infrastructure

Cited by 2 publications

References 14 publications

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

Static Analysis of Information Systems for IoT Cyber Security: A Survey of Machine Learning Approaches

LeafsnapNet: An Experimentally Evolved Deep Learning Model for Recognition of Plant Species based on Leafsnap Image Dataset

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