scholarly journals Analysis of Machine Learning Techniques used in Malware Classification in Cloud Computing Environment

2016 ◽  
Vol 133 (15) ◽  
pp. 15-18
Author(s):  
Ajeet Kumar ◽  
Naman Sharma ◽  
Abhishek Khanna ◽  
Saurav Gandhi
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Machine Learning Techniques ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Malware Classification ◽  
Learning Techniques

scholarly journals Survey and Performance Analysis of Machine Learning Based Security Threats Detection Approaches in Cloud Computing

2021 ◽  
pp. 49-58
Author(s):  
Rajesh Keshavrao Sadavarte ◽  
Dr. G. D. Kurundkar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Threat Detection ◽  
Security Threats ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

Cloud computing is gaining a lot of attention, however, security is a major obstacle to its widespread adoption. Users of cloud services are always afraid of data loss, security threats and availability problems. Recently, machine learning-based methods of threat detection are gaining popularity in the literature with the advent of machine learning techniques. Therefore, the study and analysis of threat detection and prevention strategies are a necessity for cloud protection. With the help of the detection of threats, we can determine and inform the normal and inappropriate activities of users. Therefore, there is a need to develop an effective threat detection system using machine learning techniques in the cloud computing environment. In this paper, we present the survey and comparative analysis of the effectiveness of machine learning-based methods for detecting the threat in a cloud computing environment. The performance assessment of these methods is performed using tests performed on the UNSW-NB15 dataset. In this work, we analyse machine learning models that include Support Vector Machine (SVM), Decision Tree (DT), Naive Bayes (NB), Random Forests (RF) and the K-Nearest neighbour (KNN). Additionally, we have used the most important performance indicators, namely, accuracy, precision, recall and F1 score to test the effectiveness of several methods.

Sharing VM Resources With Using Prediction of Future User Requests for an Efficient Load Balancing in Cloud Computing Environment

2021 ◽  
Vol 13 (2) ◽  
pp. 37-64
Author(s):  
Mousa Elrotub ◽  
Ahmed Bali ◽  
Abdelouahed Gherbi
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Performance Metrics ◽  
Virtual Machines ◽  
Machine Learning Techniques ◽  
Data Repository ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Learning Techniques

The problem of balancing user requests in cloud computing is becoming more serious due to the variation of workloads. Load balancing and allocation processes still need more optimizing methodologies and models to improve performance and increase the quality of service. This article describes a solution to balance user workload efficiently by proposing a model that allows each virtual machine (VM) to maximize the serving number of requests based on its capacity. The model measures VMs' capacity as a percentage and maps groups of user requests to appropriate active virtual machines. Finding the expected patterns from a big data repository, such as log data, and using some machine learning techniques can make the prediction more efficiently. The work is implemented and evaluated using some performance metrics, and the results are compared with other research. The evaluation shows the efficiency of the proposed approach in distributing user workload and improving results.

Security Issues in Fog Computing and ML-Based Solutions

2021 ◽  
pp. 209-234
Author(s):  
Himanshu Sahu ◽  
Gaytri
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Data Processing ◽  
Fog Computing ◽  
Machine Learning Techniques ◽  
Special Focus ◽  
Cloud Data Center ◽  
Cloud Data ◽  
Security Issues ◽  
Learning Techniques

IoT requires data processing, which is provided by the cloud and fog computing. Fog computing shifts centralized data processing from the cloud data center to the edge, thereby supporting faster response due to reduced communication latencies. Its distributed architecture raises security and privacy issues; some are inherited from the cloud, IoT, and network whereas others are unique. Securing fog computing is equally important as securing cloud computing and IoT infrastructure. Security solutions used for cloud computing and IoT are similar but are not directly applicable in fog scenarios. Machine learning techniques are useful in security such as anomaly detection, intrusion detection, etc. So, to provide a systematic study, the chapter will cover fog computing architecture, parallel technologies, security requirements attacks, and security solutions with a special focus on machine learning techniques.

A New Framework for Classification of Distributed Denial of Service (DDOS) Attack in Cloud Computing by Machine Learning Techniques

2014 ◽  
Vol 20 (1) ◽  
pp. 175-178
Author(s):  
Mostafa Behzadi ◽  
Ramlan Mahmod ◽  
Mehdi Barati ◽  
Azizol Bin Hj Abdullah ◽  
Mahda Noura
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Denial Of Service ◽  
Machine Learning Techniques ◽  
Distributed Denial Of Service ◽  
Ddos Attack ◽  
Learning Techniques ◽  
New Framework
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