Visible to the public One-Shot Malware Outbreak Detection Using Spatio-Temporal Isomorphic Dynamic Features

TitleOne-Shot Malware Outbreak Detection Using Spatio-Temporal Isomorphic Dynamic Features
Publication TypeConference Paper
Year of Publication2019
AuthorsPark, Sean, Gondal, Iqbal, Kamruzzaman, Joarder, Zhang, Leo
Conference Name2019 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)
Date Publishedaug
KeywordsBanking, Behavioural detection, behavioural signature, Deep Learning, dynamic detection, dynamic execution patterns, dynamic malware detection, Dynamic Malware outbreak detection, feature extraction, Generative Adversarial Autoencoder, invasive software, learning (artificial intelligence), machine learning algorithm, Malware, Mathematical model, one-shot malware outbreak detection, pubcrawl, resilience, Resiliency, Scalability, scarce number, signature based defense, spatio-temporal isomorphic dynamic features, Training

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.

Citation Keypark_one-shot_2019