Fairness and Transparency of Machine Learning for Trustworthy Cloud Services

2018 ◽  
Author(s):  
Nuno Antunes ◽  
Leandro Balby ◽  
Flavio Figueiredo ◽  
Nuno Lourenco ◽  
Wagner Meira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Services

Machine-learning approach to analyze on-the-job data via cloud services

2021 ◽  
Author(s):  
Suresh Kumar Billakurthi ◽  
B. Rajani ◽  
A. Kumari Shalini ◽  
Suvarna Lakshmi
Keyword(s):  
Machine Learning ◽  
Cloud Services ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine Learning for Design Optimization of Electromagnetic Devices: Recent Developments and Future Directions

2021 ◽  
Vol 11 (4) ◽  
pp. 1627
Author(s):  
Yanbin Li ◽  
Gang Lei ◽  
Gerd Bramerdorfer ◽  
Sheng Peng ◽  
Xiaodong Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Optimization Methods ◽  
Machine Learning Algorithms ◽  
Cloud Services ◽  
Robust Design Optimization ◽  
Support Vector ◽  
Future Directions ◽  
Electromagnetic Devices ◽  
Recent Developments

This paper reviews the recent developments of design optimization methods for electromagnetic devices, with a focus on machine learning methods. First, the recent advances in multi-objective, multidisciplinary, multilevel, topology, fuzzy, and robust design optimization of electromagnetic devices are overviewed. Second, a review is presented to the performance prediction and design optimization of electromagnetic devices based on the machine learning algorithms, including artificial neural network, support vector machine, extreme learning machine, random forest, and deep learning. Last, to meet modern requirements of high manufacturing/production quality and lifetime reliability, several promising topics, including the application of cloud services and digital twin, are discussed as future directions for design optimization of electromagnetic devices.

scholarly journals MACHINE LEARNING APPROACH FOR CRYPTOSYSTEM SUGGEST IN EDUCARE OVER CLOUDS

2021 ◽  
Vol 9 (6) ◽  
pp. 98-103
Author(s):  
Ravish G K ◽  
Thippeswamy K
Keyword(s):  
Machine Learning ◽  
Research Work ◽  
Cloud Services ◽  
Learning System ◽  
Security And Privacy ◽  
Great Promise ◽  
Sensitive Information ◽  
Online Systems ◽  
Machine Learning Approach ◽  
Time Requirements

In the current situation of the pandemic, global organizations are turning to online functionality to ensure survival and sustainability. The future, even though uncertain, holds great promise for the education system being online. Cloud services for education are the center of this research work as they require security and privacy. The sensitive information about the users and the institutions need to be protected from all interested third parties. since the data delivery on any of the online systems is always time sensitive, the have to be fast. In previous works some of the algorithms were explored and statistical inference based decision was presented. In this work a machine learning system is designed to make that decision based on data type and time requirements.

Kernel-Based Machine Learning Models to Predict Mitigation Time During Cloud Security Attacks

2021 ◽  
Vol 17 (4) ◽  
pp. 75-88
Author(s):  
Padmaja Kadiri ◽  
Seshadri Ravala
Keyword(s):  
Machine Learning ◽  
Service Provider ◽  
Predictive Models ◽  
Cloud Service ◽  
Cloud Services ◽  
Security Threats ◽  
Learning Models ◽  
Security Attacks ◽  
Cloud Service Provider ◽  
Machine Learning Models

Security threats are unforeseen attacks to the services provided by the cloud service provider. Depending on the type of attack, the cloud service and its associated features will be unavailable. The mitigation time is an integral part of attack recovery. This research paper explores the different parameters that will aid in predicting the mitigation time after an attack on cloud services. Further, the paper presents machine learning models that can predict the mitigation time. The paper presents the kernel-based machine learning models that can predict the average mitigation time during security attacks. The analysis of the results shows that the kernel-based models show 87% accuracy in predicting the mitigation time. Furthermore, the paper explores the performance of the kernel-based machine learning models based on the regression-based predictive models. The regression model is used as a benchmark model to analyze the performance of the machine learning-based predictive models in the prediction of mitigation time in the wake of an attack.

scholarly journals cHybriDroid: A Machine Learning-Based Hybrid Technique for Securing the Edge Computing

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Afifa Maryam ◽  
Usman Ahmed ◽  
Muhammad Aleem ◽  
Jerry Chun-Wei Lin ◽  
Muhammad Arshad Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hybrid Approach ◽  
Malware Detection ◽  
Edge Computing ◽  
Cloud Services ◽  
Hybrid Technique ◽  
Dynamic Features ◽  
Hybrid Features ◽  
Android Malware ◽  
Detection Techniques

Smart phones are an integral component of the mobile edge computing (MEC) framework. Securing the data stored on mobile devices is very crucial for ensuring the smooth operations of cloud services. A growing number of malicious Android applications demand an in-depth investigation to dissect their malicious intent to design effective malware detection techniques. The contemporary state-of-the-art model suggests that hybrid features based on machine learning (ML) techniques could play a significant role in android malware detection. The selection of application’s features plays a very crucial role to capture the appropriate behavioural patterns of malware instances for a useful classification of mobile applications. In this study, we propose a novel hybrid approach to detect android malware, wherein static features in conjunction with dynamic features of smart phone applications are employed. We collect these hybrid features using permissions, intents, and run-time features (such as information leakage, cryptography’s exploitation, and network manipulations) to analyse the effectiveness of the employed techniques for malware detection. We conduct experiments using over 5,000 real-world applications. The outcomes of the study reveal that the proposed set of features has successfully detected malware threats with 97% F-measure results.

scholarly journals Redefining Safety in Light of Human-Robot Interaction: A Critical Review of Current Standards and Regulations

2021 ◽  
Vol 3 ◽  
Author(s):  
Alberto Martinetti ◽  
Peter K. Chemweno ◽  
Kostas Nizamis ◽  
Eduard Fosch-Villaronga
Keyword(s):  
Machine Learning ◽  
Human Robot Interaction ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Physical Interaction ◽  
Robot Interaction ◽  
Learning Techniques ◽  
Standards And Regulations ◽  
Physical Impact ◽  
Definition Of

Policymakers need to consider the impacts that robots and artificial intelligence (AI) technologies have on humans beyond physical safety. Traditionally, the definition of safety has been interpreted to exclusively apply to risks that have a physical impact on persons’ safety, such as, among others, mechanical or chemical risks. However, the current understanding is that the integration of AI in cyber-physical systems such as robots, thus increasing interconnectivity with several devices and cloud services, and influencing the growing human-robot interaction challenges how safety is currently conceptualised rather narrowly. Thus, to address safety comprehensively, AI demands a broader understanding of safety, extending beyond physical interaction, but covering aspects such as cybersecurity, and mental health. Moreover, the expanding use of machine learning techniques will more frequently demand evolving safety mechanisms to safeguard the substantial modifications taking place over time as robots embed more AI features. In this sense, our contribution brings forward the different dimensions of the concept of safety, including interaction (physical and social), psychosocial, cybersecurity, temporal, and societal. These dimensions aim to help policy and standard makers redefine the concept of safety in light of robots and AI’s increasing capabilities, including human-robot interactions, cybersecurity, and machine learning.

scholarly journals Securing Machine Learning in the Cloud: A Systematic Review of Cloud Machine Learning Security

2020 ◽  
Vol 3 ◽  
Author(s):  
Adnan Qayyum ◽  
Aneeqa Ijaz ◽  
Muhammad Usama ◽  
Waleed Iqbal ◽  
Junaid Qadir ◽  
...  
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Graphics Processing Units ◽  
High Performance ◽  
Cloud Services ◽  
Third Party ◽  
Systematic Evaluation ◽  
Research Issues ◽  
Wide Range ◽  
Attack And Defense

With the advances in machine learning (ML) and deep learning (DL) techniques, and the potency of cloud computing in offering services efficiently and cost-effectively, Machine Learning as a Service (MLaaS) cloud platforms have become popular. In addition, there is increasing adoption of third-party cloud services for outsourcing training of DL models, which requires substantial costly computational resources (e.g., high-performance graphics processing units (GPUs)). Such widespread usage of cloud-hosted ML/DL services opens a wide range of attack surfaces for adversaries to exploit the ML/DL system to achieve malicious goals. In this article, we conduct a systematic evaluation of literature of cloud-hosted ML/DL models along both the important dimensions—attacks and defenses—related to their security. Our systematic review identified a total of 31 related articles out of which 19 focused on attack, six focused on defense, and six focused on both attack and defense. Our evaluation reveals that there is an increasing interest from the research community on the perspective of attacking and defending different attacks on Machine Learning as a Service platforms. In addition, we identify the limitations and pitfalls of the analyzed articles and highlight open research issues that require further investigation.

scholarly journals Empirical Insights on Cloud Services for Machine Learning Applications

2020 ◽  
Vol 26 (2) ◽  
pp. 85-90
Author(s):  
Adrian MICU ◽  
Marius GERU ◽  
Angela-Eliza MICU ◽  
Alexandru CAPATINA ◽  
Constantin AVRAM ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cloud Services ◽  
Machine Learning Applications

scholarly journals Cloud computing survey on services, enhancements and challenges in the era of machine learning and data science

2020 ◽  
Vol 9 (2) ◽  
pp. 117
Author(s):  
Wajid Hassan ◽  
Te-Shun Chou ◽  
Omar Tamer ◽  
John Pickard ◽  
Patrick Appiah-Kubi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Cloud Computing ◽  
Data Science ◽  
Service Level Agreement ◽  
Resource Scarcity ◽  
Cloud Services ◽  
Future Research ◽  
Intelligent Decision Making ◽  
Intelligent Decision

<p>Cloud computing has sweeping impact on the human productivity. Today it’s used for Computing, Storage, Predictions and Intelligent Decision Making, among others. Intelligent Decision Making using Machine Learning has pushed for the Cloud Services to be even more fast, robust and accurate. Security remains one of the major concerns which affect the cloud computing growth however there exist various research challenges in cloud computing adoption such as lack of well managed service level agreement (SLA), frequent disconnections, resource scarcity, interoperability, privacy, and reliability. Tremendous amount of work still needs to be done to explore the security challenges arising due to widespread usage of cloud deployment using Containers. We also discuss Impact of Cloud Computing and Cloud Standards. Hence in this research paper, a detailed survey of cloud computing, concepts, architectural principles, key services, and implementation, design and deployment challenges of cloud computing are discussed in detail and important future research directions in the era of Machine Learning and Data Science have been identified.</p>

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