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Multi-Loss Siamese Neural Network with Batch Normalization Layer for malware detection
journal contribution
posted on 2020-11-19, 01:57 authored by Jinting Zhu, Julian Jang-Jaccard, Paul WattersPaul WattersNo description supplied
Funding
This work was supported by the Cyber Security Research Programme-Artificial Intelligence for Automating Response to Threats from the Ministry of Business, Innovation, and Employment (MBIE) of New Zealand as a part of the Catalyst Strategy Funds under Grant MAUX1912.
History
Publication Date
2020-09-18Journal
IEEE AccessVolume
8Pagination
9p. (p. 171542-171550)Publisher
IEEEISSN
2169-3536Rights Statement
The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.Publisher DOI
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Categories
No categories selectedKeywords
Science & TechnologyTechnologyComputer Science, Information SystemsEngineering, Electrical & ElectronicTelecommunicationsComputer ScienceEngineeringMalwareFeature extractionTrainingTask analysisMachine learningRecurrent neural networksSiamese neural network (SNN)malware detectionvanishing gradient problemfeature embedding spacezero-day attack
