Titlebook: Deployable Machine Learning for Security Defense; First International Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh Conference proceedings 20

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Deployable Machine Learning for Security Defense978-3-030-59621-7Series ISSN 1865-0929 Series E-ISSN 1865-0937

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978-3-030-59620-0Springer Nature Switzerland AG 2020

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MALOnt: An Ontology for Malware Threat Intelligencen different platforms from scattered threat sources. This collective information can guide decision making in cyber defense applications utilized by security operation centers. In this paper, we introduce an open-source malware ontology, MALOnt that allows the structured extraction of information an

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: Adaptable Fraud Detection in the Real Worlding:.How suspicious is ‘Smith’, trying to buy $500 shoes, on Monday 3am? How to merge the risk scores, from a handful of risk-assessment modules (‘oracles’) in an adversarial environment? More importantly, given historical data (orders, prices, and what-happened afterwards), and business goals/restr

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Domain Generation Algorithm Detection Utilizing Model Hardening Through GAN-Generated Adversarial Exf hardcoding the address of the C&C domain in the malware, DGAs are used to frequently change the address of the C&C server, causing static detection methods, such as blacklists, to be ineffective. In response, DGA detection methods have been proposed which attempt to detect these DGA-produced domai

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