Malware is any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing at a faster rate every year and poses a serious global security threat. Although signature-b...
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Malware is any type of malicious code that has the potential to harm a computer or network. The volume of malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new malware. supervised machine-learning models have been used to address this issue. However, the use of supervisedlearning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect unknown malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.
Although smart-card data secures collective travel information on public transportation users, the reality is that only a few cities are equipped with an automatic fare collection (AFC) system that can provide user in...
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Although smart-card data secures collective travel information on public transportation users, the reality is that only a few cities are equipped with an automatic fare collection (AFC) system that can provide user information for both boarding and alighting locations. Many researchers have delved into forecasting the destinations of smart-card users. Such effort, however, have never been validated with actual data on a large scale. In the present study, a deep-learningmodel was developed to estimate the destinations of bus passengers based on both entry-only smart-card data and land-use characteristics. A supervised machine-learning model was trained using exact information on both boarding and alighting. That information was provided by the AFC system in Seoul, Korea. The model performance was superior to that of the most prevalent schemes developed thus far.
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