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- W4385957003 abstract "Criminal investigation (CI) is a vital part of policing, with officers using a variety of traditional approaches to investigate crimes including robbery and assault. However, the strategies should be combined with the use of artificial intelligence to evaluate and determine different types of crimes in order to take real-time action. To combat cybercrime, new cyber security measures are needed. Conventional reputation frameworks perform poorly due to high management costs and also because of some other drawbacks such as false rate is high, more time taking process, and it is working on a limited number of data sources. This study proposes a hybrid approach based on intelligent dynamic malware analysis (IDMA) to address the issues raised above. The proposed approach uses advanced dynamic malware using decision tree classification machine learning (ML) algorithm. The proposed method emphases the risk scores (RS) as well as the confidence score (CS). At first, we compare ML algorithms to determine the good recall, precision and F-measure. As the next step, the comparison of complete reputation system with other reputation systems have been done. Using the confidential and risk scores, which are inversely proportional to each other, we can conclude that our findings are accurate. The risk score will automatically decrease as a result of an increase in confidence, reducing risk levels. The suggested architecture can not only be cross-checked with external sources, but it can also address security concerns that were previously overlooked by obsolete reputation engines." @default.
- W4385957003 created "2023-08-18" @default.
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- W4385957003 date "2023-01-01" @default.
- W4385957003 modified "2023-10-17" @default.
- W4385957003 title "Computational System Based on Machine Learning with Hybrid Security Technique to Classify Crime Offenses" @default.
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- W4385957003 doi "https://doi.org/10.1007/978-981-99-2271-0_20" @default.
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