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L(a)ying in (Test)Bed - How Biased Datasets Produce Impractical Results for Actual Malware Families' Classification.

, , , , and . ISC, volume 11723 of Lecture Notes in Computer Science, page 381-401. Springer, (2019)

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L(a)ying in (Test)Bed - How Biased Datasets Produce Impractical Results for Actual Malware Families' Classification., , , , and . ISC, volume 11723 of Lecture Notes in Computer Science, page 381-401. Springer, (2019)AntiViruses under the microscope: A hands-on perspective., , , , , , and . Comput. Secur., (2022)We need to talk about antiviruses: challenges & pitfalls of AV evaluations., , , and . Comput. Secur., (2020)Machine Learning (In) Security: A Stream of Problems., , , , , , and . CoRR, (2020)Taking a Peek: An Evaluation of Anomaly Detection Using System calls for Containers., , , and . ISCC, page 1-6. IEEE, (2021)Understanding uses and misuses of similarity hashing functions for malware detection and family clustering in actual scenarios., , , , and . Digit. Investig., (2021)Computer Users Have Unique Yet Temporally Inconsistent Computer Usage Profiles., , , , , , , , , and 4 other author(s). CoRR, (2021)Fast & Furious: Modelling Malware Detection as Evolving Data Streams., , , , , and . CoRR, (2022)People Still Care About Facts: Twitter Users Engage More with Factual Discourse than Misinformation., , , , , , , , , and . SocialSec, volume 14097 of Lecture Notes in Computer Science, page 3-22. Springer, (2023)Fast & Furious: On the modelling of malware detection as an evolving data stream., , , , , and . Expert Syst. Appl., (2023)