Search

Ransomware is a type of malicious software that encrypts a victim's files and demands payment in exchange for the decryption key. It is a rapidly growing and evolving threat that has caused significant damage and disruption to individuals and organizations around the world. In this paper, we propose a comprehensive ransomware classification approach based on the comparison of similarity matrices derived from static, dynamic analysis, and visualization. Our approach involves the use of multiple analysis techniques to extract features from ransomware samples and to generate similarity matrices based on these features. These matrices are then compared using a variety of comparison algorithms to identify similarities and differences between the samples. The resulting similarity scores are then used to classify the samples into different categories, such as families, variants, and versions. We evaluate our approach using a dataset of ransomware samples and demonstrate that it can accurately classify the samples with a high degree of accuracy. One advantage of our approach is the use of visualization, which allows us to classify and cluster large datasets of ransomware in a more intuitive and effective way. In addition, static analysis has the advantage of being fast and accurate, while dynamic analysis allows us to classify and cluster packed ransomware samples. We also compare our approach to other classification approaches based on single analysis techniques and show that our approach outperforms these approaches in terms of classification accuracy. Overall, our study demonstrates the potential of using a comprehensive approach based on the comparison of multiple analysis techniques, including static analysis, dynamic analysis, and visualization, for the accurate and efficient classification of ransomware. It also highlights the importance of considering multiple analysis techniques in the development of effective ransomware classification methods, especially when dealing with large datasets and packed samples. [ABSTRACT FROM AUTHOR]

Social determining factors such as the adverse influence of globalization, supermarket growth, fast unplanned urbanization, sedentary lifestyle, economy, and social position slowly develop behavioral risk factors in humans. Behavioral risk factors such as unhealthy habits, improper diet, and physical inactivity lead to physiological risks, and "obesity/overweight" is one of the consequences. "Obesity and overweight" are one of the major lifestyle diseases that leads to other health conditions, such as cardiovascular diseases (CVDs), chronic obstructive pulmonary disease (COPD), cancer, diabetes type II, hypertension, and depression. It is not restricted within the age and socio-economic background of human beings. The "World Health Organization" (WHO) has anticipated that 30% of global death will be caused by lifestyle diseases by 2030 and it can be prevented with the appropriate identification of associated risk factors and behavioral intervention plans. Health behavior change should be given priority to avoid life-threatening damages. The primary purpose of this study is not to present a risk prediction model but to provide a review of various machine learning (ML) methods and their execution using available sample health data in a public repository related to lifestyle diseases, such as obesity, CVDs, and diabetes type II. In this study, we targeted people, both male and female, in the age group of >20 and <60, excluding pregnancy and genetic factors. This paper qualifies as a tutorial article on how to use different ML methods to identify potential risk factors of obesity/overweight. Although institutions such as "Center for Disease Control and Prevention (CDC)" and "National Institute for Clinical Excellence (NICE)" guidelines work to understand the cause and consequences of overweight/obesity, we aimed to utilize the potential of data science to assess the correlated risk factors of obesity/overweight after analyzing the existing datasets available in "Kaggle" and "University of California, Irvine (UCI) database", and to check how the potential risk factors are changing with the change in body-energy imbalance with data-visualization techniques and regression analysis. Analyzing existing obesity/overweight related data using machine learning algorithms did not produce any brand-new risk factors, but it helped us to understand: (a) how are identified risk factors related to weight change and how do we visualize it? (b) what will be the nature of the data (potential monitorable risk factors) to be collected over time to develop our intended eCoach system for the promotion of a healthy lifestyle targeting "obesity and overweight" as a study case in the future? (c) why have we used the existing "Kaggle" and "UCI" datasets for our preliminary study? (d) which classification and regression models are performing better with a corresponding limited volume of the dataset following performance metrics? [ABSTRACT FROM AUTHOR]