首页|New Findings from City University of London in the Area of Machine Learning Described (Fesad Ransomware Detection Framework With Machine Learning Using Adaption To Concept Drift)
New Findings from City University of London in the Area of Machine Learning Described (Fesad Ransomware Detection Framework With Machine Learning Using Adaption To Concept Drift)
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Investigators publish new report on Machine Learning. According to news reporting from London, United Kingdom, by NewsRx journalists, research stated, “This paper proposes FeSAD, a framework that will allow a machine learning classifier to detect evolutionary ransomware. Ransomware is a critical player in the malware space that causes hundreds of millions of dollars of damage globally and evolves quickly.” The news correspondents obtained a quote from the research from the City University of London, “The evolution of ransomware in machine learning classifiers is often calculated as concept drift. Concept drift is dangerous as changes in the behavior of ransomware can easily lead to misclassifications, and misclassification can harm individuals and businesses. Our proposed framework consists of a feature selection layer, drift calibration layer and drift decision layer that allows a machine learning classifier to detect and classify concept drift samples reliably. We evaluate the FeSAD framework in various concept drift scenarios and observe its ability to detect drifting samples effectively. The FeSAD framework also evaluated on its ability to extend the lifespan of a classifier.”
LondonUnited KingdomEuropeCybersecurityCyborgsEmerging TechnologiesMachine LearningCity University of London
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