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

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书目名称Deployable Machine Learning for Security Defense影响因子(影响力)




书目名称Deployable Machine Learning for Security Defense影响因子(影响力)学科排名




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书目名称Deployable Machine Learning for Security Defense网络公开度学科排名




书目名称Deployable Machine Learning for Security Defense被引频次




书目名称Deployable Machine Learning for Security Defense被引频次学科排名




书目名称Deployable Machine Learning for Security Defense年度引用




书目名称Deployable Machine Learning for Security Defense年度引用学科排名




书目名称Deployable Machine Learning for Security Defense读者反馈




书目名称Deployable Machine Learning for Security Defense读者反馈学科排名

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: Adaptable Fraud Detection in the Real Worldally and continuously update our weights for each ‘oracle’. For the second problem, we show how to derive an optimal decision surface, and how to compute the Pareto optimal set, to allow what-if questions. An important consideration is adaptation: Fraudsters will change their behavior, according to

高射炮

Domain Generation Algorithm Detection Utilizing Model Hardening Through GAN-Generated Adversarial Exentiate from real domains. The resulting set of domains have characteristics, such as character distribution, that more closely resemble real domains than sets produced in previous research. We then use these GAN-produced domains as additional examples of DGA domains and use them to augment the trai

Legion

Toward Explainable and Adaptable Detection and Classification of Distributed Denial-of-Service Attac approaches, along with the detection results this method further generates risk profiles that provides users with interpretability for filtering DDoS traffic. Additionally, this method does not need to retrain the detection model in order to make it fit in a new network environment. Users can lever

多节

DAPT 2020 - Constructing a Benchmark Dataset for Advanced Persistent Threatsintrusion datasets have three key limitations - (1) They capture attack traffic at the external endpoints, limiting their usefulness in the context of APTs which comprise of attack vectors within the internal network as well (2) The difference between normal and anomalous behavior is quiet distingui

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Glossy

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Billy Tsouvalas,Nick Nikiforakis approaches, along with the detection results this method further generates risk profiles that provides users with interpretability for filtering DDoS traffic. Additionally, this method does not need to retrain the detection model in order to make it fit in a new network environment. Users can lever

Ingratiate

Lecture Notes in Computer Scienceintrusion datasets have three key limitations - (1) They capture attack traffic at the external endpoints, limiting their usefulness in the context of APTs which comprise of attack vectors within the internal network as well (2) The difference between normal and anomalous behavior is quiet distingui