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

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發(fā)表于 2025-3-21 19:53:21 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Deployable Machine Learning for Security Defense
副標(biāo)題First International
編輯Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh
視頻videohttp://file.papertrans.cn/266/265763/265763.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Deployable Machine Learning for Security Defense; First International  Gang Wang,Arridhana Ciptadi,Ali Ahmadzadeh Conference proceedings 20
描述This book constitutes selected papers from the First International Workshop on?Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online.?.The 8 full papers were thoroughly reviewed and selected from 13 qualified submissions. The papers are organized in the following topical sections: understanding the adversaries; adversarial ML for better security; threats on networks..
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; computer crime; computer security; computer systems; cryptography; cyber-attacks
版次1
doihttps://doi.org/10.1007/978-3-030-59621-7
isbn_softcover978-3-030-59620-0
isbn_ebook978-3-030-59621-7Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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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
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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
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發(fā)表于 2025-3-22 05:46:23 | 只看該作者
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
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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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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
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發(fā)表于 2025-3-23 08:52:29 | 只看該作者
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
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