Identification of IT Incidents for Improved Risk Analysis by Using Machine Learning

2015 ◽  
Author(s):  
Sardar Muhammad Sulaman ◽  
Kim Weyns ◽  
Martin Host
Keyword(s):  
Machine Learning ◽  
Risk Analysis

Risk assessment for health insurance using equation modeling and machine learning

2021 ◽  
Vol 25 (2) ◽  
pp. 201-225
Author(s):  
Amrik Singh ◽  
K.R. Ramkumar
Keyword(s):  
Machine Learning ◽  
Health Insurance ◽  
Risk Analysis ◽  
Real Life ◽  
Equation Model ◽  
Least Square ◽  
Parameter Analysis ◽  
Risk Indicators ◽  
Equation Modeling ◽  
Pairwise Correlation

Due to the advancement of medical sensor technologies new vectors can be added to the health insurance packages. Such medical sensors can help the health as well as the insurance sector to construct mathematical risk equation models with parameters that can map the real-life risk conditions. In this paper parameter analysis in terms of medical relevancy as well in terms of correlation has been done. Considering it as ‘inverse problem’ the mathematical relationship has been found and are tested against the ground truth between the risk indicators. The pairwise correlation analysis gives a stable mathematical equation model can be used for health risk analysis. The equation gives coefficient values from which classification regarding health insurance risk can be derived and quantified. The Logistic Regression equation model gives the maximum accuracy (86.32%) among the Ridge Bayesian and Ordinary Least Square algorithms. Machine learning algorithm based risk analysis approach was formulated and the series of experiments show that K-Nearest Neighbor classifier has the highest accuracy of 93.21% to do risk classification.

scholarly journals Risk analysis: Survival data analysis vs. machine learning. Application to Alzheimer prediction

2019 ◽  
Vol 347 (11) ◽  
pp. 817-830
Author(s):  
Catherine Huber-Carol ◽  
Shulamith Gross ◽  
Filia Vonta
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Risk Analysis ◽  
Survival Data ◽  
Analysis Survival

Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

2019 ◽  
Vol 11 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Kavita Sharma ◽  
B. B. Gupta
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
High Accuracy ◽  
Learning Approaches ◽  
Privacy Risk ◽  
Test Set ◽  
Extensive Test ◽  
Android Applications ◽  
Android Market ◽  
F Measure

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

scholarly journals A Survey on Risk Analysis in Information Technology Infrastructure Library (ITIL) Change Management Using Supervised Machine Learning

2020 ◽  
pp. 298-303
Author(s):  
Srushti Gajjar ◽  
Mrugendrasinh Rahevar
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Risk Analysis ◽  
Change Management ◽  
New Technologies ◽  
Primary Component ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Technology Infrastructure ◽  
Managing Risk

Innovation in IT and technology leads to new developments within the organization. It is important for companies to respond more quickly to the changing trends in order to stay competitive. ITIL change management allows companies to introduce new technologies without interruption or downtime. It follows a standard practice to avoid any unwanted interruptions and involves evaluation, planning and approval of changes. Change Management is all about managing risk for the company and it is linked to the perception of risk that the company has. Risk Analysis is primary component when it comes to any software changes; organizations are concerned about risk management. For better performance by identifying and assessing risk in systematic manner is the aim of the risk management. In ITIL change management risk assessment is a manual process. Automation of risk analysis would have enormous benefits, like reducing the downtime, maximize the productivity and so on. So this paper is mainly on the survey of different supervised machine algorithms of machine learning, like support vector machine, Naive Bayes, logistic regressions.

Towards Privacy Risk Analysis in Android Applications Using Machine Learning Approaches

2021 ◽  
pp. 645-666
Author(s):  
Kavita Sharma ◽  
B. B. Gupta
Keyword(s):  
Machine Learning ◽  
Risk Analysis ◽  
High Accuracy ◽  
Learning Approaches ◽  
Privacy Risk ◽  
Test Set ◽  
Extensive Test ◽  
Android Applications ◽  
Android Market ◽  
F Measure

Android-based devices easily fall prey to an attack due to its free availability in the android market. These Android applications are not certified by the legitimate organization. If the user cannot distinguish between the set of permissions requested by an application and its risk, then an attacker can easily exploit the permissions to propagate malware. In this article, the authors present an approach for privacy risk analysis in Android applications using machine learning. The proposed approach can analyse and identify the malware application permissions. Here, the authors achieved high accuracy and improved F-measure through analyzing the proposed method on the M0Droid dataset and completed testing on an extensive test set with malware from the Androzoo dataset and benign applications from the Drebin dataset.

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