- Title
- One-shot malware outbreak detection using spatio-temporal isomorphic dynamic features
- Creator
- Park, Sean; Gondal, Iqbal; Kamruzzaman, Joarder; Zhang, Leo
- Date
- 2019
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171947
- Identifier
- vital:14454
- Identifier
-
https://doi.org/10.1109/TrustCom/BigDataSE.2019.00108
- Identifier
- ISBN:9781728127767 (ISBN)
- Abstract
- Fingerprinting the malware by its behavioural signature has been an attractive approach for malware detection due to the homogeneity of dynamic execution patterns across different variants of similar families. Although previous researches show reasonably good performance in dynamic detection using machine learning techniques on a large corpus of training set, decisions must be undertaken based upon a scarce number of observable samples in many practical defence scenarios. This paper demonstrates the effectiveness of generative adversarial autoencoder for dynamic malware detection under outbreak situations where in most cases a single sample is available for training the machine learning algorithm to detect similar samples that are in the wild. © 2019 IEEE.; E1
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 p. 751-756
- Rights
- Copyright @ 2019 IEEE
- Rights
- This metadata is freely available under a CCO license
- Subject
- Behavioural detection; Deep learning; Dynamic Malware outbreak detection; Generative Adversarial Autoencoder
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