Data is becoming a new resource with high value, so many parties are interested in owning it. There are many ways to take it, one of which is planting and spreading malware known as stealer malware. Over time, malware...
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Data is becoming a new resource with high value, so many parties are interested in owning it. There are many ways to take it, one of which is planting and spreading malware known as stealer malware. Over time, malware...
Data is becoming a new resource with high value, so many parties are interested in owning it. There are many ways to take it, one of which is planting and spreading malware known as stealer malware. Over time, malware has become more sophisticated, targeted, complex, commercialized, and scalable for a wider range of attacks. This makes malware analysis an important job requiring a lot of time, expertise, and extensive knowledge, both by individuals and teams of analysts. This study will analyze stealer malware using three analytical methods: surface, runtime, and static code. In malware analysis using the surface method, malware is tested by scanning by antivirus, hashing malware, and package/obfuscated detection, followed by Portable Executable analysis and malware sandbox analysis. In the runtime method, the malware is run for further observations of registry changes, DNS activity observations, and network data communication activities. In research using the static code analysis method, tests were carried out to find the relationship between the use of linked libraries and functions, string search as a guide for working steps of malware, and debugging malware to explore deeper into malware behaviour. The results obtained are information about the characteristics of the malware stealer and its impact on the test environment.
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and...
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This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both *** BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke *** findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes.
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