A Review of Machine Learning Algorithms for Cloud Computing Security

Butt, Umer Ahmed; Mehmood, Muhammad Tahir; Shah, Syed Bilal Hussain; Amin, Rashid; Shaukat, Mansoor; Raza, Syed Mohsan; Suh, Doug Young; Piran, Md. Jalil

doi:10.3390/electronics9091379

Cited by 112 publications

(61 citation statements)

References 74 publications

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“…The logical regression is used by YOLOv3 where it foresees the objectivity score. It is projected over the selection frame initially on the object that has been established to be the fundamental truth in the picture by pre-training models [35]. This gives a singular bounding box and any kind of fallacy in this part would cause mistakes in both allocation of these boxes and their accuracy and also in the detection arrear.…”

Section: Feature Extraction and Analysis [34]mentioning

confidence: 99%

Comparative analysis of deep learning image detection algorithms

et al. 2021

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A computer views all kinds of visual media as an array of numerical values. As a consequence of this approach, they require image processing algorithms to inspect contents of images. This project compares 3 major image processing algorithms: Single Shot Detection (SSD), Faster Region based Convolutional Neural Networks (Faster R-CNN), and You Only Look Once (YOLO) to find the fastest and most efficient of three. In this comparative analysis, using the Microsoft COCO (Common Object in Context) dataset, the performance of these three algorithms is evaluated and their strengths and limitations are analysed based on parameters such as accuracy, precision and F1 score. From the results of the analysis, it can be concluded that the suitability of any of the algorithms over the other two is dictated to a great extent by the use cases they are applied in. In an identical testing environment, YOLO-v3 outperforms SSD and Faster R-CNN, making it the best of the three algorithms.

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Section: Feature Extraction and Analysis [34]mentioning

confidence: 99%

Comparative analysis of deep learning image detection algorithms

et al. 2021

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“…Instead of focusing only on detecting confidential data trends, ML solutions should use a comprehensive approach to protecting organizational information across all cloud applications of an organization. ML focuses on advancing computer programs which can find the right rate for their own learning [51].…”

Section: B Rq2: Machine Learning Algorithmsmentioning

confidence: 99%

Machine Learning for Cloud Security: A Systematic Review

et al. 2021

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The popularity and usage of Cloud computing is increasing rapidly. Several companies are investing in this field either for their own use or to provide it as a service for others. One of the results of Cloud development is the emergence of various security problems for both industry and consumer. One of the ways to secure Cloud is by using Machine Learning (ML). ML techniques have been used in various ways to prevent or detect attacks and security gaps on the Cloud. In this paper, we provide a Systematic Literature Review (SLR) of ML and Cloud security methodologies and techniques. We analyzed 63 relevant studies and the results of the SLR are categorized into three main research areas: (i) the different types of Cloud security threats, (ii) ML techniques used, and (iii) the performance outcomes. We have defined 11 Cloud security areas. Moreover, distributed denial-of-service (DDoS) and data privacy are the most common Cloud security areas, with a 16% level of use and 14%respectively. On the other hand, we found 30 ML techniques used, some used hybrid and others as standalone. The most popular ML used is SVM in both hybrid and standalone models. Furthermore, 60% of the papers compared their models with other models to prove the efficiency of their proposed model. Moreover, 13 different evaluation metrics were enumerated. The most applied metric is true positive rate and least used is training time. Lastly, from 20 datasets found, KDD and KDD CUP'99 are the most used among relevant studies.

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“…The conventional optimization techniques and cost function with constraints have high computational overhead and algorithm complexity [ 43 ]. Therefore, we need to formulate a comprehensive cost function, considering all important parameters, for generating a training dataset only.…”

Section: System Modelmentioning

confidence: 99%

Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach

Abbas

Ali

Abbas

et al. 2021

Sensors

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In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy consumption of UEs. We use deep learning to find, simultaneously, the best partitioning of a single task with the best offloading policy. The deep neural network (DNN) is trained through a comprehensive dataset, generated from our mathematical model, which reduces the time delay and energy consumption of the overall process. Due to the complexity and computation of the mathematical model in the algorithm being high, due to trained DNN the complexity and computation are minimized in the proposed work. We propose a comprehensive cost function, which depends on various delays, energy consumption, radio resources, and computation resources. Furthermore, the cost function also depends on energy consumption and delay due to the task-division-process in partial offloading. None of the literature work considers the partitioning along with the computational offloading policy, and hence, the time and energy consumption due to task-division-process are ignored in the cost function. The proposed work considers all the important parameters in the cost function and generates a comprehensive training dataset with high computation and complexity. Once we get the training dataset, then the complexity is minimized through trained DNN which gives faster decision making with low energy consumptions. Simulation results demonstrate the superior performance of the proposed technique with high accuracy of the DNN in deciding offloading policy and partitioning of a task with minimum delay and energy consumption for UE. More than 70% accuracy of the trained DNN is achieved through a comprehensive training dataset. The simulation results also show the constant accuracy of the DNN when the UEs are moving which means the decision making of the offloading policy and partitioning are not affected by the mobility of UEs.

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A Review of Machine Learning Algorithms for Cloud Computing Security

Cited by 112 publications

References 74 publications

Comparative analysis of deep learning image detection algorithms

Comparative analysis of deep learning image detection algorithms

Machine Learning for Cloud Security: A Systematic Review

Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach

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