2018 |
Ceschin, Fabrício; Pinage, Felipe; Castilho, Marcos; Menotti, David; Oliveira, Luis S; Gregio, André The Need for Speed: An Analysis of Brazilian Malware Classifiers Journal Article IEEE Security Privacy, 16 (6), pp. 31-41, 2018, ISSN: 1540-7993. Abstract | Links | BibTeX | Tags: Brazilian malware classifers, Feature extraction, invasive software, learning (artificial intelligence), Machine learning, machine-learning systems, malware, malware classification, pattern classification, security, Security of data, Support vector machines @article{8636415, title = {The Need for Speed: An Analysis of Brazilian Malware Classifiers}, author = {Fabrício Ceschin and Felipe Pinage and Marcos Castilho and David Menotti and Luis S Oliveira and André Gregio}, url = {https://secret.inf.ufpr.br/papers/fabricio_needforspeed.pdf}, doi = {10.1109/MSEC.2018.2875369}, issn = {1540-7993}, year = {2018}, date = {2018-11-01}, journal = {IEEE Security Privacy}, volume = {16}, number = {6}, pages = {31-41}, abstract = {Using a dataset containing about 50,000 samples from Brazilian cyberspace, we show that relying solely on conventional machine-learning systems without taking into account the change of the subject's concept decreases the performance of classification, emphasizing the need to update the decision model immediately after concept drift occurs.}, keywords = {Brazilian malware classifers, Feature extraction, invasive software, learning (artificial intelligence), Machine learning, machine-learning systems, malware, malware classification, pattern classification, security, Security of data, Support vector machines}, pubstate = {published}, tppubtype = {article} } Using a dataset containing about 50,000 samples from Brazilian cyberspace, we show that relying solely on conventional machine-learning systems without taking into account the change of the subject's concept decreases the performance of classification, emphasizing the need to update the decision model immediately after concept drift occurs. |
2018 |
The Need for Speed: An Analysis of Brazilian Malware Classifiers Journal Article IEEE Security Privacy, 16 (6), pp. 31-41, 2018, ISSN: 1540-7993. |