scholarly journals Trust Management in the World of Cloud Computing. Past Trends and Some New Directions

2021 ◽  
Vol 22 (4) ◽  
pp. 425-444
Author(s):  
Mahreen Saleem ◽  
M.R Warsi ◽  
Saiful Islam ◽  
Areesha Anjum ◽  
Nadia Siddiquii
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Current Knowledge ◽  
Economic Benefits ◽  
Cloud Services ◽  
Machine Learning Techniques ◽  
Trust Evaluation ◽  
Trust Models ◽  
Cloud Environments ◽  
Insight Into

Over the past years, Cloud computing has become one of the most influential information technologies to combat computing needs because of its unprecedented advantages. In spite of all the social and economic benefits it provides, it has its own fair share of issues. These include privacy, security, virtualization, storage, and trust. The underlying issues of privacy, security, and trust are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. There is a lack of review on trust models in this research domain. This paper focuses on getting insight into the nomenclature of trust, its classifications, trust dimensions and throws an insight into various trust models that exist in the current knowledge stack. Also, various trust evaluation measures are highlighted in this work. We also draw a comparative analysis of various trust evaluation models and metrics to better understand the notion of trust in cloud environments. Furthermore, this work brings into light some of the gaps and areas that need to be tackled toward solving the trust issues in cloud environments so as to provide a trustworthy cloud ecosystem. Lastly, we proposed a Machine Learning backed Rich model based solution for trust verification in Cloud Computing. We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. The proposed scheme can be utilized for verifying the cloud trust in delivering services as expected that can be perceived as an initiative towards trust evaluation in cloud services employing Machine learning techniques. The experimental results prove that the proposed method verifies the service utilized with an accuracy of 99%.

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.

scholarly journals Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure

AI Magazine ◽  
2012 ◽  
Vol 33 (2) ◽  
pp. 55 ◽  
Author(s):  
Nisarg Vyas ◽  
Jonathan Farringdon ◽  
David Andre ◽  
John Ivo Stivoric
Keyword(s):  
Machine Learning ◽  
Energy Expenditure ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Sensor Technology ◽  
Health Goals ◽  
System A ◽  
Multi Sensor Data Fusion ◽  
Learning Techniques ◽  
Insight Into

In this article we provide insight into the BodyMedia FIT armband system — a wearable multi-sensor technology that continuously monitors physiological events related to energy expenditure for weight management using machine learning and data modeling methods. Since becoming commercially available in 2001, more than half a million users have used the system to track their physiological parameters and to achieve their individual health goals including weight-loss. We describe several challenges that arise in applying machine learning techniques to the health care domain and present various solutions utilized in the armband system. We demonstrate how machine learning and multi-sensor data fusion techniques are critical to the system’s success.

scholarly journals Research on threat detection in cyber security based on machine learning

2021 ◽  
Vol 2113 (1) ◽  
pp. 012074
Author(s):  
Qiwei Ke
Keyword(s):  
Machine Learning ◽  
Cyber Security ◽  
Information Privacy ◽  
Machine Learning Techniques ◽  
Threat Detection ◽  
Vulnerability Detection ◽  
New Information ◽  
Learning Techniques ◽  
Potential Problems ◽  
Insight Into

Abstract The volume of the data has been rocketed since the new information era arrives. How to protect information privacy and detect the threat whenever the intrusion happens has become a hot topic. In this essay, we are going to look into the latest machine learning techniques (including deep learning) which are applicable in intrusion detection, malware detection, and vulnerability detection. And the comparison between the traditional methods and novel methods will be demonstrated in detail. Specially, we would examine the whole experiment process of representative examples from recent research projects to give a better insight into how the models function and cooperate. In addition, some potential problems and improvements would be illustrated at the end of each section.

A Structured Method for Security Requirements Elicitation concerning the Cloud Computing Domain

2014 ◽  
Vol 5 (2) ◽  
pp. 20-43 ◽  
Author(s):  
Kristian Beckers ◽  
Isabelle Côté ◽  
Ludger Goeke ◽  
Selim Güler ◽  
Maritta Heisel
Keyword(s):  
Cloud Computing ◽  
System Analysis ◽  
Economic Benefits ◽  
Cloud Services ◽  
Security Requirements ◽  
Cloud System ◽  
Security Requirement ◽  
Analysis Pattern ◽  
It Resources ◽  
Requirements Patterns

Cloud computing systems offer an attractive alternative to traditional IT-systems, because of economic benefits that arise from the cloud's scalable and flexible IT-resources. The benefits are of particular interest for SME's. The reason is that using Cloud Resources allows an SME to focus on its core business rather than on IT-resources. However, numerous concerns about the security of cloud computing services exist. Potential cloud customers have to be confident that the cloud services they acquire are secure for them to use. Therefore, they have to have a clear set of security requirements covering their security needs. Eliciting these requirements is a difficult task, because of the amount of stakeholders and technical components to consider in a cloud environment. Therefore, the authors propose a structured, pattern-based method supporting eliciting security requirements and selecting security measures. The method guides potential cloud customers to model the application of their business case in a cloud computing context using a pattern-based approach. Thus, a potential cloud customer can instantiate our so-called Cloud System Analysis Pattern. Then, the information of the instantiated pattern can be used to fill-out our textual security requirements patterns and individual defined security requirement patterns, as well. The presented method is tool-supported. Our tool supports the instantiation of the cloud system analysis pattern and automatically transfers the information from the instance to the security requirements patterns. In addition, they have validation conditions that check e.g., if a security requirement refers to at least one element in the cloud. The authors illustrate their method using an online-banking system as running example.

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.

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

scholarly journals Personalized Medicine Using Neuroimmunological Biomarkers in Depressive Disorders

2021 ◽  
Vol 11 (2) ◽  
pp. 114
Author(s):  
Suhyuk Chi ◽  
Moon-Soo Lee
Keyword(s):  
Machine Learning ◽  
Nervous System ◽  
Depressive Disorders ◽  
Current Knowledge ◽  
Antidepressant Treatment ◽  
Learning Approach ◽  
Suicidal Risk ◽  
Major Depressive ◽  
Machine Learning Approach ◽  
Insight Into

Major depressive disorder (MDD) is associated with increased suicidal risk and reduced productivity at work. Neuroimmunology, the study of the immune system and nervous system, provides further insight into the pathogenesis and outcome of MDD. Cytokines are the main modulators of neuroimmunology, and their levels are somewhat entangled in depressive disorders as they affect depressive symptoms and are affected by antidepressant treatment. The use of cytokine-derived medication as a treatment option for MDD is currently a topic of interest. Although not very promising, cytokines are also considered as possible prognostic or diagnostic markers for depression. The machine learning approach is a powerful tool for pattern recognition and has been used in psychiatry for finding useful patterns in data that have translational meaning and can be incorporated in daily clinical practice. This review focuses on the current knowledge of neuroimmunology and depression and the possible use of machine learning to widen our understanding of the topic.

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