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Titlebook: Cyber Security and Digital Forensics; Proceedings of ICCSD Kavita Khanna,Vania Vieira Estrela,Joel José Puga Conference proceedings 2022 T

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書(shū)目名稱(chēng)Cyber Security and Digital Forensics
副標(biāo)題Proceedings of ICCSD
編輯Kavita Khanna,Vania Vieira Estrela,Joel José Puga
視頻videohttp://file.papertrans.cn/242/241759/241759.mp4
概述Presents research works in the field of cyber security and digital forensics.Provides original works presented at ICCSDF 2021 held in Haryana, India.Serves as a reference for researchers and practitio
叢書(shū)名稱(chēng)Lecture Notes on Data Engineering and Communications Technologies
圖書(shū)封面Titlebook: Cyber Security and Digital Forensics; Proceedings of ICCSD Kavita Khanna,Vania Vieira Estrela,Joel José Puga  Conference proceedings 2022 T
描述.This book features high-quality research papers presented at the International Conference on Applications and Techniques in Cyber Security and Digital Forensics (ICCSDF 2021), held at The NorthCap University, Gurugram, Haryana, India, during April 3–4, 2021. This book discusses the topics ranging from information security to cryptography, mobile application attacks to digital forensics, and from cyber security to blockchain. The goal of the book is to provide 360-degree view of cybersecurity to the readers which include cyber security issues, threats, vulnerabilities, novel idea, latest technique and technology, and mitigation of threats and attacks along with demonstration of practical applications. This book also highlights the latest development, challenges, methodologies as well as other emerging areas in this field. It brings current understanding of common Web vulnerabilities while maintaining awareness and knowledge of contemporary standards, practices, procedures, and methods of Open Web Application Security Project. It also expounds how to recover information after a cybercrime..
出版日期Conference proceedings 2022
關(guān)鍵詞Information Security; Cryptography; Cyber Security; Digital Forensics; Network Security; Web and Applicat
版次1
doihttps://doi.org/10.1007/978-981-16-3961-6
isbn_softcover978-981-16-3960-9
isbn_ebook978-981-16-3961-6Series ISSN 2367-4512 Series E-ISSN 2367-4520
issn_series 2367-4512
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Android Malware Detection Using Extreme Learning Machine Optimized with Swarm Intelligencethe classifier’s accuracy. The dataset consists of 1700 benign and 418 malicious Android applications from which over 15,000 features of different types were extracted, and feature selection techniques were applied. The aforementioned dataset was used to experiment our proposed model, and significan
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Passive Video Forgery Detection Techniques to Detect Copy Move Tampering Through Feature Comparison ith the assistance of extricated highlights to check the measure of changes in the tempered frames all through the video stream. To check the uprightness of a video, include correlation and RANSAC strategies are utilized and it shows huge outcomes distinctive when checked with the video which altere
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https://doi.org/10.1057/9780230607132ribe how fog can be used in e-Healthcare systems. In this research paper, we discuss fog computing in the context of Uttarakhand. Our main concerns are security issues and challenges faced by fog computing while using the E-Healthcare system in hilly areas of Uttarakhand. In studying those challenge
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Seaweeds, seagrasses, and emergent plantsrotect network against blackhole attack. These set of rules are created using three AODV critical attribute which are rate of ., . and . value. The proposed FIDS, thereafter, evaluated using ns2 simulator and are found efficient to detect and isolate the attacker node from the network. The deploymen
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https://doi.org/10.1007/978-3-0348-7531-8gging, C4.5 (J48) and random forest classifiers are tested on the phishing dataset. The dataset is taken from UCI repository which has 1353 instances. The C4.5 classification model has the highest accuracy rate of 90.8%.
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