A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection

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Journal of Computer and Communications

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ISSN Online: 2327-5227
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"A Comparative Study of Machine Learning Algorithms and Their Ensembles for Botnet Detection"
written by Songhui Ryu, Baijian Yang,
published by Journal of Computer and Communications, Vol.6 No.5, 2018

has been cited by the following article(s):

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CrossRef

[1]	Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
	Applied Sciences, 2024

[2]	Performance evaluation of botnet detection using machine learning techniques
	International Journal of Electrical and Computer …, 2023

[3]	BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning
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[4]	Detection of Botnet Traffic using Deep Learning Approach
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[5]	IoT botnet attack detection model based on DBO-catboost
	Applied Sciences, 2023

[6]	Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique.
	Intelligent Automation & …, 2023

[7]	INTERNET OF THINGS BOTNET DETECTION VIA ENSEMBLE DEEP NEURAL NETWORKS
	International Journal of 3D …, 2023

[8]	Outside the Closed World: On Using Machine Learning for Network Intrusion Detection
	… on Information and Communication Technology for …, 2023

[9]	Discriminative features-based trustworthiness prediction in IoT devices using machine learning models
	2023 IEEE Smart World …, 2023

[10]	Guardians of the IoT Galaxy: Using Deep Learning to Secure IoT Networks Against Botnet Attacks
	Masri, H Mokayed - Full Length Article, 2023

[11]	Data driven approach to identify a flow-based Botnet Host using Deep Learning
	2023 International Conference on …, 2023

[12]	Detecting Botnets Using Hidden Markov Model, Profile Hidden Markov Model and Network Flow Analysis
	2023

[13]	Smart Prediction System for Classifying Mirai and Gafgyt Attacks on IoT Devices
	2022 International …, 2022

[14]	Establishing the contaminating effect of metadata feature inclusion in machine-learned network intrusion detection models
	Detection of Intrusions …, 2022

[15]	CUPID: A labeled dataset with Pentesting for evaluation of network intrusion detection
	Journal of Systems …, 2022

[16]	BOTNET ATTACKS DETECTION IN INTERNET OF THINGS USING MACHINE LEARNING
	NEUROQUANTOLOGY, 2022

[17]	Botnets and how to automatic detect them: exploring new ways of dealing with botnet classification: Botnets e como detectá-los automaticamente: explorando novas …
	Brazilian Journal of …, 2022

[18]	MACHINE LEARNING FOR MALWARE BOTNET DETECTION IN IOT DEVICES
	2021

[19]	Improved Botnet Attack Detection Using Principal Component Analysis and Ensemble Voting Algorithm
	2021 International …, 2021

[20]	A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques
	2021

[21]	Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review
	2021

[22]	SISTEM PENCEGAHAN SERANGAN BOTNET DENGAN METODE SUPPORT VECTOR MACHINE
	2021

[23]	Anomaly classification using genetic algorithm-based random forest model for network attack detection.
	Computers, Materials & Continua, 2021

[24]	A Novel Approach for Detecting DGA‐Based Botnets in DNS Queries Using Machine Learning Techniques
	Journal of Computer Networks and …, 2021

[25]	Towards a Universal Features Set for IoT Botnet Attacks Detection
	2020

[26]	The risk of botnets in cyber physical systems
	2020

[27]	Comparative Study of Botnet Detection System using Different Machine-Learning Algorithms
	2020

[28]	Multi-Attributed Heterogeneous Graph Convolutional Network for Bot Detection
	2020

[29]	An Efficient BotNet Detection Scheme Exploiting Word2Vec and Accelerated Hierarchical Density-based Clustering
	2019

[30]	Tracking Temporal Evolution of Network Activity for Botnet Detection
	2019

[31]	Clustering Botnet Behavior Using K-Means with Uncertain Data
	2019

[32]	A Comparative Study of Machine Learning Classifiers for Network Intrusion Detection
	2019

[33]	Network Analytics for Streaming Traffic Analysis
	2019

[34]	Encoding IP Address as a Feature for Network Intrusion Detection
	2019

[35]	Word2Vec 과 가속화 계층적 밀집도 기반 클러스터링을 활용한 효율적 봇넷 탐지 기법
	Journal of Korean Society …, 2019

[36]	Sınıflandırma ve regresyon ağaçları ile rastgele orman algoritması kullanarak botnet tespiti: Van Yüzüncü Yıl Üniversitesi Örneği
	Yüzüncü Yıl Üniversitesi Fen …, 2018

[1]	Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models Applied Sciences, 2024 DOI:10.3390/app14104019

[2]	Botnet Detection by including payload information of packets through machine learning 2024 Control Instrumentation System Conference (CISCON), 2024 DOI:10.1109/CISCON62171.2024.10696386

[3]	Detection and Classification of Network Traffic in Bot Network Using Deep Learning Journal of Information & Knowledge Management, 2024 DOI:10.1142/S0219649224500862

[4]	Detection of Botnet Traffic using Deep Learning Approach 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS), 2023 DOI:10.1109/ICSCDS56580.2023.10104633

[5]	Data driven approach to identify a flow-based Botnet Host using Deep Learning 2023 International Conference on Advancement in Computation & Computer Technologies (InCACCT), 2023 DOI:10.1109/InCACCT57535.2023.10141698

[6]	IoT Botnet Attack Detection Model Based on DBO-Catboost Applied Sciences, 2023 DOI:10.3390/app13127169

[7]	INTERNET OF THINGS BOTNET DETECTION VIA ENSEMBLE DEEP NEURAL NETWORKS International Journal of 3D Printing Technologies and Digital Industry, 2023 DOI:10.46519/ij3dptdi.1293277

[8]	ICT for Intelligent Systems Smart Innovation, Systems and Technologies, 2023 DOI:10.1007/978-981-99-3982-4_23

[9]	BotSward: Centrality Measures for Graph-Based Bot Detection Using Machine Learning Computers, Materials & Continua, 2023 DOI:10.32604/cmc.2023.031641

[10]	Performance Analysis of Intrusion Detection System in the IoT Environment Using Feature Selection Technique Intelligent Automation & Soft Computing, 2023 DOI:10.32604/iasc.2023.036856

[11]	CUPID: A labeled dataset with Pentesting for evaluation of network intrusion detection Journal of Systems Architecture, 2022 DOI:10.1016/j.sysarc.2022.102621

[12]	Detection of Intrusions and Malware, and Vulnerability Assessment Lecture Notes in Computer Science, 2022 DOI:10.1007/978-3-031-09484-2_2

[13]	CUPID: A labeled dataset with Pentesting for evaluation of network intrusion detection Journal of Systems Architecture, 2022 DOI:10.1016/j.sysarc.2022.102621

[14]	Smart Prediction System for Classifying Mirai and Gafgyt Attacks on IoT Devices 2022 International Conference on Computational Science and Computational Intelligence (CSCI), 2022 DOI:10.1109/CSCI58124.2022.00218

[15]	CUPID: A labeled dataset with Pentesting for evaluation of network intrusion detection Journal of Systems Architecture, 2022 DOI:10.1016/j.sysarc.2022.102621

[16]	Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review Symmetry, 2021 DOI:10.3390/sym13050866

[17]	Machine Learning-Based Botnet Detection in Software-Defined Network: A Systematic Review Symmetry, 2021 DOI:10.3390/sym13050866

[18]	A Novel Approach for Detecting DGA-Based Botnets in DNS Queries Using Machine Learning Techniques Journal of Computer Networks and Communications, 2021 DOI:10.1155/2021/4767388

[19]	Improved Botnet Attack Detection Using Principal Component Analysis and Ensemble Voting Algorithm 2021 International Conference on Computing, Computational Modelling and Applications (ICCMA), 2021 DOI:10.1109/ICCMA53594.2021.00014

[20]	Security of Cyber-Physical Systems 2020 DOI:10.1007/978-3-030-45541-5_5

[21]	Towards a Universal Features Set for IoT Botnet Attacks Detection 2020 IEEE 23rd International Multitopic Conference (INMIC), 2020 DOI:10.1109/INMIC50486.2020.9318106

[22]	Anomaly Classification Using Genetic Algorithm-based Random Forest Model for Network Attack Detection Computers, Materials & Continua, 2020 DOI:10.32604/cmc.2020.013813

[23]	Proceedings of International Joint Conference on Computational Intelligence Algorithms for Intelligent Systems, 2020 DOI:10.1007/978-981-13-7564-4_41

[24]	Artificial Intelligence and Security Lecture Notes in Computer Science, 2019 DOI:10.1007/978-3-030-24265-7_7

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