標題: Titlebook: Machine Intelligence and Big Data Analytics for Cybersecurity Applications; Yassine Maleh,Mohammad Shojafar,Youssef Baddi Book 2021 The Ed [打印本頁] 作者: dabble 時間: 2025-3-21 18:42
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書目名稱Machine Intelligence and Big Data Analytics for Cybersecurity Applications被引頻次學科排名
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書目名稱Machine Intelligence and Big Data Analytics for Cybersecurity Applications讀者反饋
書目名稱Machine Intelligence and Big Data Analytics for Cybersecurity Applications讀者反饋學科排名
作者: 不可侵犯 時間: 2025-3-21 22:26 作者: overhaul 時間: 2025-3-22 00:36 作者: 北極熊 時間: 2025-3-22 04:49 作者: 絕種 時間: 2025-3-22 10:08
Improving Cyber-Threat Detection by?Moving the Boundary Around the?Normal Samplesdetection models in various scenarios. However, it often suffers from training data over-fitting. In this paper, we propose a supervised machine learning method for cyber-threat detection, which modifies the training set to reduce data over-fitting when training a deep neural network. This is done b作者: Externalize 時間: 2025-3-22 13:38 作者: NOVA 時間: 2025-3-22 17:27 作者: 嬰兒 時間: 2025-3-22 22:48 作者: Expediency 時間: 2025-3-23 02:08
IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network the field of cybersecurity. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has be作者: prostatitis 時間: 2025-3-23 07:18 作者: Ige326 時間: 2025-3-23 09:48 作者: Frisky 時間: 2025-3-23 17:29
Ameliorated Face and Iris Recognition Using Deep Convolutional Networkseed to respond with superior accuracy for proof of identity and concurrently ensure ease of access. In this chapter we propose approaches using deep convolutional networks which give extremely accurate results with substantially smaller processing time for face and iris recognition. Two approaches b作者: caldron 時間: 2025-3-23 21:13
Presentation Attack Detection Framework biometrics (e.g., face, eye, fingerprint), iris-based authentication is commonly used in every day applications. In iris-based authentication systems, iris images from legitimate users are captured and certain features are extracted to be used for matching during the authentication process. Literat作者: 織布機 時間: 2025-3-23 23:33
Classifying Common Vulnerabilities and Exposures Database Using Text Mining and Graph Theoretical Anat increase its usability. This results in focusing on some well-known vulnerabilities and leaving others during the security tests. Better classification of this dataset would result in finding solutions to a larger set of vulnerabilities/exposures. In this research, vulnerability and exposure data作者: bourgeois 時間: 2025-3-24 04:38
A Novel Deep Learning Model to Secure Internet of Things in Healthcarerolling, and optimization. In this paper, an artificial neural network (ANN), a structure of deep learning model, is proposed to efficiently work with small datasets. The contribution of this paper is two-fold. First, we proposed a novel approach to build ANN architecture. Our proposed ANN structure作者: HOWL 時間: 2025-3-24 07:30
Secure Data Sharing Framework Based on Supervised Machine Learning Detection System for Future SDN-Brs may steal or perturb flows in SDN by performing several types of attacks such as address resolution protocol poisoning, main in the middle and rogue nodes attacks. These attacks are very harm full to SDN networks as they can be performed easily and passively at all SDN layers. Furthermore, data-s作者: 考博 時間: 2025-3-24 11:14 作者: Instantaneous 時間: 2025-3-24 16:52
Anjum Nazir,Rizwan Ahmed Khanngen (mit Ausnahme der physiologischen Wirkungsweise) kennen zu lehren und ihre Ergebnisse unter allgemeinen Gesichtspunkten miteinander zu verknüpfen hat. Nach dieser neuen, gegenüber früheren erheblich erweiterten Definition ist es die Aufgabe des pharmakognostischen Forschers, nicht nur die zu se作者: Cryptic 時間: 2025-3-24 21:46
Youssef Gahi,Imane El Alaouingen (mit Ausnahme der physiologischen Wirkungsweise) kennen zu lehren und ihre Ergebnisse unter allgemeinen Gesichtspunkten miteinander zu verknüpfen hat. Nach dieser neuen, gegenüber früheren erheblich erweiterten Definition ist es die Aufgabe des pharmakognostischen Forschers, nicht nur die zu se作者: Arthr- 時間: 2025-3-25 00:59 作者: Accede 時間: 2025-3-25 04:28
Mustapha El Hamzaoui,Faycal Bensalahd den Abwehrkr?ften des infizierten Wirtsorganismus andrerseits, sollte eine Bek?mpfung auch durch eine Stimulierung der k?rpereigenen Abwehr m?glich sein. Der Organismus verfügt über vielf?ltige M?glichkeiten, das Eindringen und die Pr?senz für ihn potentiell sch?dlicher Substanzen abzuwehren: Man 作者: notion 時間: 2025-3-25 09:55 作者: 可轉變 時間: 2025-3-25 13:19 作者: 心神不寧 時間: 2025-3-25 19:26 作者: 連鎖,連串 時間: 2025-3-25 20:46 作者: 注射器 時間: 2025-3-26 02:11 作者: 銼屑 時間: 2025-3-26 06:10
n der besten Geister ein sjcheres Fundament besitzen. 1. Allgemeines. a) Einteilung und Auswahl des Stoffes. Die Grundeigenschaften des Lebens sind notwendigerweise hineingewoben in alle krankhaften ?u?erungen der Zelle oder der Gewebe; sie k?nnen eine ?tiologische oder symptomatische Bedeutung besi作者: macabre 時間: 2025-3-26 11:38 作者: 鴿子 時間: 2025-3-26 16:34 作者: 語言學 時間: 2025-3-26 17:33
IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Networkror rate of 0.07 which measurements lead this study to generate an optimized model for phishing detection. The detailed process of feature extraction and optimized model generation along with the detection of unknown URLs are considered and proposed during the development of IntAnti-Phish (An Intelligent Anti-Phishing Framework).作者: 使絕緣 時間: 2025-3-26 22:03
A Novel Deep Learning Model to Secure Internet of Things in Healthcarept. A comprehensive evaluation of experiments of proposed solution and other classical deep learning models are shown on three small scale publicly available benchmark datasets. Our proposed model leverages the accuracy of textual data, and our research results validate and confirm the effectiveness of our ANN model.作者: 不愿 時間: 2025-3-27 03:48
Book 2021trusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s作者: Outshine 時間: 2025-3-27 07:53 作者: 好開玩笑 時間: 2025-3-27 10:07
1860-949X .Proposes many case studies and applications of machine inte.This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and 作者: 桉樹 時間: 2025-3-27 17:09 作者: Dealing 時間: 2025-3-27 21:14 作者: Limousine 時間: 2025-3-28 00:16 作者: nitroglycerin 時間: 2025-3-28 04:52 作者: fatuity 時間: 2025-3-28 09:00
Yassine Maleh,Mohammad Shojafar,Youssef BaddiPresents the latest discoveries in terms of machine intelligence and Big data analytics techniques and methods for cybersecurity and privacy.Proposes many case studies and applications of machine inte作者: CLASH 時間: 2025-3-28 11:00
Studies in Computational Intelligencehttp://image.papertrans.cn/m/image/620350.jpg作者: Lethargic 時間: 2025-3-28 14:38
Machine Learning and Deep Learning Models for Big Data Issuesng. We also regroup the most sensitive security aspects that should be addressed to protect valuable data. All the contributions and dimensions were addressed through a set of security use cases, namely, malware detection, intrusion, anomaly, access control, and data ingestion controls. Furthermore,作者: 施魔法 時間: 2025-3-28 22:42 作者: exclamation 時間: 2025-3-29 00:43
Toward a Knowledge-Based Model to Fight Against Cybercrime Within Big Data Environments: A Set of Keopose a knowledge-based approach to support the fight against cybercrime in the big data context. But, we will answer, at the beginning, a large number of comprehension questions to facilitate as best as possible, to those interested in the subject of “big data and cybercrime”, the understanding of 作者: Optic-Disk 時間: 2025-3-29 04:58
Spam Emails Detection Based on?Distributed Word Embedding with?Deep?Learningre utilized to transform emails into email word vectors, as an essential step for machine learning algorithms. Moreover, optimal parameters are identified for many deep learning architectures and email representation by following the hyper-parameter tuning approach. The performance of many classical作者: Bph773 時間: 2025-3-29 08:16
AndroShow: A Large Scale Investigation to Identify the Pattern of Obfuscated Android Malwareaset named Android PRAGuard Dataset. Finally, the patterns in a matrix form have been found and stored in a Comma Separated Values (CSV) file which will be the base of detecting the obfuscated malware in future.作者: 有節(jié)制 時間: 2025-3-29 12:16 作者: Polydipsia 時間: 2025-3-29 18:13 作者: DAMN 時間: 2025-3-29 21:17
Presentation Attack Detection Frameworkhis type of attack. Existing approaches relying on static features of the iris are not suitable to prevent presentation attacks. Feature from live Iris (or liveness detection) is a promising approach. Further, additional layer of security from iris feature can enable hardening the security of authen作者: cleaver 時間: 2025-3-30 00:22
Classifying Common Vulnerabilities and Exposures Database Using Text Mining and Graph Theoretical An possible to discover groups of contents, thus, the CVE items which have similarities. Moreover, lacking some concepts pointed out the problems related to CVE such as delays in the review CVE process or not being preferred by some user groups.作者: Offstage 時間: 2025-3-30 07:02
Secure Data Sharing Framework Based on Supervised Machine Learning Detection System for Future SDN-Bonstructed data set dedicated to SDN context. The simulation results show that our framework can effectively and efficiently address sniffing attacks that can be detected and stopped quickly. Finally, we observe high accuracy with a low false-positive for attack detection.作者: Aerate 時間: 2025-3-30 09:37 作者: 公共汽車 時間: 2025-3-30 13:23 作者: 光亮 時間: 2025-3-30 20:14 作者: 無能力 時間: 2025-3-30 23:35 作者: 摘要記錄 時間: 2025-3-31 00:55
