Detection of Malware by Static Analysis Using Machine Learning Methods
Vuran, Nisa | Acı, Mehmet
The increase in cyber-attacks has also started to threaten the use of internet and information technologies. This situation emphasizes the importance of detecting malicious software that is responsible for cyber-attacks. Nowadays, there are studies on the development of machine learning methods for malicious software detection. Malicious software detectors are the primary tools in defense against malicious software. The quality of such a detector is determined by the techniques it uses. Malware analysis methods such as machine learning, deep learning, and static and dynamic analysis are among these techniques. This study presents malware analysis and classification techniques. For malware detection, well-known algorithms for machine learning including such K-Nearest Neighbors, Naive Bayes,...
An integrated SHAP-MCDM approach for slope stability prediction based on machine learning algorithms
Demir, Alparslan Serhat | Dağdeviren, Uğur | Kurnaz, Talas Fikret | Erden, Caner | Kökçam, Abdullah Hulusi
In machine learning (ML)-based slope stability prediction studies, feature importance results often vary across different algorithms, leading to inconsistent interpretations. This issue arises because the importance of features differs depending on the algorithm applied within the same study. To address this challenge, this study proposes a novel methodology for obtaining a final, unified ranking of features by combining the feature importance rankings of various ML algorithms using a Multi-Criteria Decision-Making (MCDM) technique. This approach ensures a consistent and reliable feature ranking derived from the results of successful ML models. Furthermore, the study demonstrates how performance indicators of ML algorithms can be translated into criterion weights within the MCDM f...