標(biāo)題: Titlebook: Machine Learning Approaches in Cyber Security Analytics; Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel Book 2020 Springer Nature Sing [打印本頁] 作者: 撒謊 時間: 2025-3-21 19:28
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書目名稱Machine Learning Approaches in Cyber Security Analytics讀者反饋學(xué)科排名
作者: 眼界 時間: 2025-3-22 00:04
Machine Learning Approaches in Cyber Security Analytics作者: Condescending 時間: 2025-3-22 00:27 作者: hurricane 時間: 2025-3-22 07:03 作者: Charlatan 時間: 2025-3-22 11:40
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: Champion 時間: 2025-3-22 12:55 作者: 寵愛 時間: 2025-3-22 17:34
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: 陶瓷 時間: 2025-3-22 23:14 作者: FLOAT 時間: 2025-3-23 05:15
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: Blazon 時間: 2025-3-23 06:25
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: 取之不竭 時間: 2025-3-23 09:59
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: Collar 時間: 2025-3-23 16:34
Tony Thomas,Athira P. Vijayaraghavan,Sabu Emmanuel作者: 男生戴手銬 時間: 2025-3-23 19:11 作者: Inflammation 時間: 2025-3-24 00:40 作者: 熱情贊揚(yáng) 時間: 2025-3-24 03:28
Introduction to Machine Learning, For example, predicting whether an android application is a malware or goodware during a malware detection process is a classification task, whereas estimating the threat level of a system is a regression task.作者: MARS 時間: 2025-3-24 07:07 作者: 失望未來 時間: 2025-3-24 13:10 作者: 假裝是我 時間: 2025-3-24 15:04
Book 2020ed over various networks, monitoring everything that is exchanged and identifying potential cyber threats and attacks poses a serious challenge for cyber experts. Further, as cyber attacks become more frequent and sophisticated, there is a requirement for machines to predict, detect, and identify th作者: 歡樂中國 時間: 2025-3-24 19:03 作者: 加劇 時間: 2025-3-24 23:32
Support Vector Machines and Malware Detection,re used in predicting continuous values, and classification models are used in predicting which class a data point is part of. SVMs are mostly used for solving classification problems. At the end of this chapter, we also demonstrate the classification of malware from benign ones.作者: Cholagogue 時間: 2025-3-25 03:56 作者: 推延 時間: 2025-3-25 10:04
plement machine learning research.Provides the latest resear.This book introduces various machine learning methods for cyber security analytics. With an overwhelming amount of data being generated and transferred over various networks, monitoring everything that is exchanged and identifying potentia作者: Canary 時間: 2025-3-25 12:46
iehungswissenschaftlichen Forschung. Film wird dazu als kulturelles Ged?chtnis und als Ort der Konstruktion und Aufführung von popul?rem Wissen über Lehr-, Lern- und Bildungsprozesse befragt..Der Inhalt.? Fu?tritte:?.The Miracle Worker.?und die Gewalt des Spracherwerbs.? Einblicke in die Erziehung e作者: Genistein 時間: 2025-3-25 18:47
Introduction,and alert the user whenever we install or run a malicious application. In this situation, a rule-based detection mechanism may not work as malware continuously evolve and a set of rules may not be sufficient to characterize an application as malware or goodware. Instead, we may have a detection mech作者: 闖入 時間: 2025-3-25 23:01
Introduction to Machine Learning, from a training dataset to make predictions. Predictive models can be built for classification or regression problems. Regression models explore relationships between variables and make predictions about continuous variables. Classification involves predicting discrete class labels for data points.作者: 知識分子 時間: 2025-3-26 01:43
Machine Learning and Cybersecurity,erdata, ML algorithms can build models of behaviors and use those models as a basis for making predictions on newly input data. ML techniques can analyze threats and respond to attacks and security incidents quickly in an automated way.作者: 倫理學(xué) 時間: 2025-3-26 06:37 作者: 悠然 時間: 2025-3-26 12:17 作者: Insufficient 時間: 2025-3-26 15:38
Nearest Neighbor and Fingerprint Classification, data point in the test dataset and then assign it to a class that is most represented by the neighbors. NN classifier works by taking into consideration the maximum number of nearest neighbors belonging to the similar class.作者: V切開 時間: 2025-3-26 19:38 作者: 苦惱 時間: 2025-3-27 00:08
Applications of Decision Trees, data points in a dataset by constructing tree structures. These tree-like structures are used to make accurate predictions about unseen data. The dataset is split into multiple subsets, thereby resulting in each decision node branching to more decision nodes. The very first decision node from which作者: 使熄滅 時間: 2025-3-27 01:24
Adversarial Machine Learning in Cybersecurity, machine learning model. For instance, attributes of a goodware can be added to a malware executable to make the classifier identify a malicious sample as benign. As the name suggests, “adversary” means opponent or enemy. If you are thinking what an enemy has got to do in machine learning, this chap作者: 殺死 時間: 2025-3-27 05:24
https://doi.org/10.1007/978-981-15-1706-8Malware; Anomaly Detection; Biometrics; Machine Intelligence; Cyber Security; data structures作者: Expurgate 時間: 2025-3-27 09:35
978-981-15-1708-2Springer Nature Singapore Pte Ltd. 2020作者: lethal 時間: 2025-3-27 14:42 作者: 悠然 時間: 2025-3-27 19:59
Clustering and Malware Classification,alling malware without the user’s knowledge or authorization. In such a situation where the user’s data and privacy are always at threat, it is necessary to build a resilient system so as to curb such attacks. The system should undergo a learning–decision-making process to early detect and defend malware attacks.作者: 傳染 時間: 2025-3-27 22:34
Nearest Neighbor and Fingerprint Classification, data point in the test dataset and then assign it to a class that is most represented by the neighbors. NN classifier works by taking into consideration the maximum number of nearest neighbors belonging to the similar class.作者: Veneer 時間: 2025-3-28 03:54 作者: NUL 時間: 2025-3-28 07:02
Tony Thomas,Athira P. Vijayaraghavan,Sabu EmmanuelIncludes applications of the latest machine learning algorithms in cyber security.Discusses how applications in cyber security analytics complement machine learning research.Provides the latest resear作者: 吞噬 時間: 2025-3-28 13:49
http://image.papertrans.cn/m/image/620387.jpg作者: 哀求 時間: 2025-3-28 17:24
Dimensionality Reduction and Face Recognition,Dimensionality reduction is used to reduce the number of features under consideration, where each feature is a dimension that partly represents the data objects. Dimensionality reduction methods make sure that all the relevant information remains intact while mapping data from a higher dimension to lower dimension.作者: 不透明性 時間: 2025-3-28 18:47
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