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標(biāo)題: Titlebook: Machine Learning for Cybersecurity; Innovative Deep Lear Marwan Omar Book 2022 The Author(s), under exclusive license to Springer Nature Sw [打印本頁]

作者: 離開浮于空中 時(shí)間: 2025-3-21 19:29
書目名稱Machine Learning for Cybersecurity影響因子(影響力)

書目名稱Machine Learning for Cybersecurity影響因子(影響力)學(xué)科排名

書目名稱Machine Learning for Cybersecurity網(wǎng)絡(luò)公開度

書目名稱Machine Learning for Cybersecurity網(wǎng)絡(luò)公開度學(xué)科排名

書目名稱Machine Learning for Cybersecurity被引頻次

書目名稱Machine Learning for Cybersecurity被引頻次學(xué)科排名

書目名稱Machine Learning for Cybersecurity年度引用

書目名稱Machine Learning for Cybersecurity年度引用學(xué)科排名

書目名稱Machine Learning for Cybersecurity讀者反饋

書目名稱Machine Learning for Cybersecurity讀者反饋學(xué)科排名

作者: 微粒 時(shí)間: 2025-3-21 23:29
New Approach to Malware Detection Using Optimized Convolutional Neural Network, and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and impleme

作者: SOBER 時(shí)間: 2025-3-22 01:28

作者: Sciatica 時(shí)間: 2025-3-22 08:11
Book 2022olve certain challenges facing the cybersecurity industry..By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior.?.The knowledge and tools introduc

作者: fringe 時(shí)間: 2025-3-22 11:18
Malware Anomaly Detection Using Local Outlier Factor Technique,ectiveness of our technique on real-world datasets. This is an efficient technique for malware detection as the model trained for this purpose is based on unsupervised learning. The model trains on the anomalies, that is, the unusual behavior in a process, making it significantly effective.

作者: 時(shí)代 時(shí)間: 2025-3-22 15:33
Application of Machine Learning (ML) to Address Cybersecurity Threats,various problem domains in cybersecurity. To achieve this objective, a rapid evidence assessment (REA) of existing scholarly literature on the subject matter is adopted. The aim is to present a snapshot of the various ways ML is being applied to help address cybersecurity threat challenges.

作者: lipoatrophy 時(shí)間: 2025-3-22 19:22

作者: Distribution 時(shí)間: 2025-3-23 00:00
Application of Machine Learning (ML) to Address Cybersecurity Threats,s has prompted the use of machine learning (hereafter, ML) to help address the problem. But as organizations increasingly use intelligent cybersecurity techniques, the overall efficacy and benefit analysis of these ML-based digital security systems remain a subject of increasing scholarly inquiry. T

作者: carbohydrate 時(shí)間: 2025-3-23 05:12