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Titlebook: Applications and Techniques in Information Security; 13th International C Srikanth Prabhu,Shiva Raj Pokhrel,Gang Li Conference proceedings

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樓主: Iodine
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發(fā)表于 2025-3-23 13:16:37 | 只看該作者
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發(fā)表于 2025-3-23 14:53:41 | 只看該作者
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發(fā)表于 2025-3-23 21:47:30 | 只看該作者
Continuously Non-malleable Codes from?Authenticated Encryptions in?2-Split-State Model where an attacker can tamper the message for polynomial number of times. In this work, we propose a construction of continuously non-malleable code from authenticated encryption in 2-split-state model. Our construction provides security against polynomial number of tampering attacks and non-malleab
14#
發(fā)表于 2025-3-23 22:42:59 | 只看該作者
Variants of Crypto-Jacking Attacks and Their Detection Techniquesith its unique design. In order to increase the profitability of crypto-jacking, attackers are expanding their reach to browsers, network devices, and even Internet of Things (IoT) devices. Browsers, for example, are a particularly enticing target for attackers looking to obtain sensitive data from
15#
發(fā)表于 2025-3-24 03:07:24 | 只看該作者
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發(fā)表于 2025-3-24 09:09:02 | 只看該作者
Analysis of?Classification Algorithms for?Predicting Parkinson’s Disease and?Applications in?the?Fieient Boosting, XGBoost, Random Forest, and Extra Trees Classification are used to estimate whether the individual is normal or affected by Parkinson’s disease. According to this study, the ensemble method Gradient Boosting classification algorithm outperformed other classification algorithms in term
17#
發(fā)表于 2025-3-24 14:37:17 | 只看該作者
Optimization of?Secured Cluster Based Charging Dynamics and?Scheduling of?EV Using Deep RNN, at CS, vehicle time scheduling is done using the DRNN approach, considering delay-based distance computation. When compared to the stochastic Particle Swarm Optimization (PSO) algorithm for routing, the proposed DRNN-SSD routing algorithm optimizes delay and traffic congestion significantly achiev
18#
發(fā)表于 2025-3-24 18:31:01 | 只看該作者
19#
發(fā)表于 2025-3-24 19:12:20 | 只看該作者
Detection of Android Ransomware Using Machine Learning Approachictim’s data and demands for a ransom amount for the decryption key. The present Android ransomware research is deficient in key components and relies on supervised machine-learning techniques. However, these techniques have several drawbacks and early detection and recognition of ransomware in andr
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發(fā)表于 2025-3-25 00:43:12 | 只看該作者
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