Search Results - (( botnet detection using algorithm ) OR ( evolution optimization method algorithm ))
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ABC: android botnet classification using feature selection and classification algorithms
Published 2017“…The Information Gain algorithm is used to select the most significant permissions, then the classification algorithms Naïve Bayes, Random Forest and J48 used to classify the Android apps as botnet or benign apps. …”
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Systematic analysis on mobile botnet detection techniques using genetic algorithm
Published 2024Conference Paper -
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Systematic Analysis on Mobile Botnet Detection Techniques Using Genetic Algorithm
Published 2024“…Furthermore, this paper also discusses the challenges and the potential research for future work with relate of the genetic algorithm. This research paper can be used as a reference and guidance for further study on mobile botnet detection techniques.…”
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A new mobile botnet classification based on permission and API calls
Published 2024Conference Paper -
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Botnet detection using automated script / Norfathin Rosli
Published 2020“…This paper is about a Botnet Detection using Automated Script, the programming language is and the operating system is Windows 7. …”
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A comprehensive review of mobile botnet detection using genetic algorithm: A systematic review
Published 2024journal::journal article -
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Botnet Detection in IoT Devices Using Random Forest Classifier with Independent Component Analysis
Published 2022“…This paper represents a model that accounts for the detection of botnets through the use of machine learning algorithms. …”
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A New Mobile Botnet Classification based on Permission and API Calls
Published 2024“…This new classification can be used as the input for mobile botnet detection for future work, especially for financial matters.…”
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Layered Botnet Detection Framework Based On Signal Processing And Discrete Time Analysis
Published 2012“…This thesis presents a new approach in the area of botnet detection. It introduces a framework called Layered Botnet Detection Framework (LBDF) that can detect botnet members efficiently. …”
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Botnet Detection Using a Feed-Forward Backpropagation Artificial Neural Network
Published 2019“…The proposed technique aims to detect Botnet zero-day attack in real time. This technique applies a backpropagation algorithm to the CTU-13 dataset to train and evaluate the Botnet detection classifier. …”
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ABC: Android botnet classification using feature selection and classification algorithms
Published 2024journal::journal article -
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A smart framework for mobile botnet detection using static analysis
Published 2020“…This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. …”
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An enhanced android botnet detection approach using feature refinement
Published 2019“…In order to detect botnet attacks which causes immense chaos and problems to smartphones, first the Android botnet need to be analysed. …”
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A Static Approach towards Mobile Botnet Detection
Published 2016“…In this study we propose a static approach towards mobile botnet detection. This technique combines MD5, permissions, broadcast receivers as well as background services and uses machine learning algorithm to detect those applications that have capabilities for mobile botnets. …”
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A behavior based algorithm to detect spam bots
Published 2009“…This is performed by using a suitable algorithm that can identify the abnormal behaviors that related to the spam activity. …”
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Intersection Features For Android Botnet Classification
Published 2019“…The Chi Square was used to select the most significant permissions, then the classification algorithms like Naïve Bayes and Decision Tree were used to classify the Android apps as botnet or benign apps. …”
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Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…The proposed method is tested on 10 multi-objective benchmark problems of CEC 2009 and compared with four metaheuristics: Multi-Objective Grey Wolf Optimizer (MOGWO), Multi-Objective Evolutionary Algorithm Based on Decomposition (MOEA/D), Multi-Objective Differential Evolution (MODE) and MOPSO. …”
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