首页|Reports Summarize Machine Learning Research from Umm Al-Qura University (Ransomw are detection based on machine learning using memory features)
Reports Summarize Machine Learning Research from Umm Al-Qura University (Ransomw are detection based on machine learning using memory features)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting originating from Umm Al-Qura Unive rsity by NewsRx correspondents, research stated, “Ransomware attacks have escala ted recently and are affecting essential infrastructure and enterprises across t he globe.” Funders for this research include Imam Abdulrahman Bin Faisal University. The news reporters obtained a quote from the research from Umm Al-Qura Universit y: “Unfortunately, ransomware uses sophisticated encryption techniques to encryp t important files on the targeted machine and then demands payment to decrypt th e data. Artificial intelligent techniques including machine learning have been i ncreasingly applied in the field of cybersecurity and greatly contributed to det ecting and preventing different kinds of attacks However, the number of studies that applied machine learning to detect ransomware are still limited by the obfu scation of malware, the lack of setting up a proper analysis environment, the ac curacy of models, and the high false-positive rate. Thus, it is crucial to devel op effective ransomware detection based on machine learning techniques. This stu dy aims to build a robust machine-learning model that can recognize unknown samp les using memory dumps to detect ransomware with high accuracy and minimal false positives providing an extensive analysis of how memory traces can assist in th e detection of ransomware. This goal was achieved by building a new dataset comp osed of recent ransomware group attack samples like Revil, Lockbit, and BlackCat , as well as a number of benign dynamically analyzed with in an enhanced cuckoo sandbox to ensure the most reliable results. Then, a set of machine learning models were developed, and a comparative performance analysis was conducted.”
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