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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書目名稱Cyber Security and Digital Forensics影響因子(影響力)




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刺穿

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

脫毛

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

Outmoded

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%.