| Title | Malware Family Classification Using Active Learning by Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Chen, Chin-Wei, Su, Ching-Hung, Lee, Kun-Wei, Bair, Ping-Hao |
| Conference Name | 2020 22nd International Conference on Advanced Communication Technology (ICACT) |
| Date Published | feb |
| Keywords | active learning, computer security, cybersecurity, Data models, feature extraction, Human Behavior, machine learning, Malicious Traffic Detection, Malware, malware classication, malware classification, Metrics, privacy, pubcrawl, resilience, Resiliency, Support vector machines, Uncertainty |
| Abstract | In the past few years, the malware industry has been thriving. Malware variants among the same malware family shared similar behavioural patterns or signatures reflecting their purpose. We propose an approach that combines support vector machine (SVM) classifiers and active learning by learning (ALBL) techniques to deal with insufficient labeled data in terms of the malware classification tasks. The proposed approach is evaluated with the malware family dataset from Microsoft Malware Classification Challenge (BIG 2015) on Kaggle. The results show that ALBL techniques can effectively boost the performance of our machine learning models and improve the quality of labeled samples. |
| DOI | 10.23919/ICACT48636.2020.9061419 |
| Citation Key | chen_malware_2020 |
Malware Family Classification Using Active Learning by Learning