Feature Selection for Intrusion Detection Using Random Forest

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Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
www.scirp.org/journal/jis
E-mail: jis@scirp.org

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"Feature Selection for Intrusion Detection Using Random Forest"
written by Md. Al Mehedi Hasan, Mohammed Nasser, Shamim Ahmad, Khademul Islam Molla,
published by Journal of Information Security, Vol.7 No.3, 2016

has been cited by the following article(s):

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[8]	Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges IET Networks, 2024 DOI:10.1049/ntw2.12128

[9]	Machine learning-based centralized link coding attack detection in software-defined network Wireless Networks, 2024 DOI:10.1007/s11276-023-03483-6

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[15]	Weather temperature prediction based on LSTM and transformer International Conference on Electronics, Electrical and Information Engineering (ICEEIE 2024), 2024 DOI:10.1117/12.3052168

[16]	Leveraging CNNs, Quantization, and Random Forest for Edge Deployable Intrusion Detection Efficiency 2024 5th International Conference for Emerging Technology (INCET), 2024 DOI:10.1109/INCET61516.2024.10593457

[17]	Enhancing Short-Term Solar Photovoltaic Power Forecasting Using a Hybrid Deep Learning Approach IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3440035

[18]	An efficient intrusion detection system for IoT security using CNN decision forest PeerJ Computer Science, 2024 DOI:10.7717/peerj-cs.2290

[19]	Deep Attention Knowledge Tracking Incorporating Multiple Features and TCN-Transformer 2024 International Joint Conference on Neural Networks (IJCNN), 2024 DOI:10.1109/IJCNN60899.2024.10650848

[20]	Seleção de Características na Predição de Ataques DDoS com Transformação em Padrões Ordinais Anais do XXIX Workshop de Gerência e Operação de Redes e Serviços (WGRS 2024), 2024 DOI:10.5753/wgrs.2024.3259

[21]	Machine Learning for DoS Attack Detection in IoT Systems Procedia Computer Science, 2024 DOI:10.1016/j.procs.2024.08.027

[22]	Machine Learning Predicts Biochar Aging Effects on Nitrous Oxide Emissions from Agricultural Soils ACS Agricultural Science & Technology, 2024 DOI:10.1021/acsagscitech.4c00114

[23]	A hybrid deep learning model for Bitcoin price prediction: data decomposition and feature selection Applied Economics, 2024 DOI:10.1080/00036846.2023.2276093

[24]	Dynamics of Digital Pen-Tablet: Handwriting Analysis for Person Identification Using Machine and Deep Learning Techniques IEEE Access, 2024 DOI:10.1109/ACCESS.2024.3352070

[25]	A crash occurrence risk prediction model based on variational autoencoder and generative adversarial network Transportmetrica B: Transport Dynamics, 2024 DOI:10.1080/21680566.2024.2358211

[26]	Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges IET Networks, 2024 DOI:10.1049/ntw2.12128

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[31]	IoT Network Intrusion Detection Using Ensemble Learning Approach 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023 DOI:10.1109/ICCCNT56998.2023.10307253

[32]	An AdamW-Based Deep Neural Network Using Feature Selection and Data Oversampling for Intrusion Detection 2023 8th International Conference on Data Science in Cyberspace (DSC), 2023 DOI:10.1109/DSC59305.2023.00039

[33]	BI-TBL: A Network Intrusion Detection Method Based on Payload Analysis 2023 20th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2023 DOI:10.1109/ICCWAMTIP60502.2023.10387094

[34]	Detecting the Type of Attack in Healthcare Cloud Computing System Using Termite Deep Belief Neural Network (TDBNN) for Future Smart Cities Journal of Testing and Evaluation, 2023 DOI:10.1520/JTE20220041

[35]	Signal Anomaly Detection of Bridge SHM System Based on Two-Stage Deep Convolutional Neural Networks Structural Engineering International, 2023 DOI:10.1080/10168664.2021.1983914

[36]	A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks Intelligent Automation & Soft Computing, 2023 DOI:10.32604/iasc.2023.031876

[37]	DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning Computers, Materials & Continua, 2023 DOI:10.32604/cmc.2023.037386

[38]	Detecting the Type of Attack in Healthcare Cloud Computing System Using Termite Deep Belief Neural Network (TDBNN) for Future Smart Cities Journal of Testing and Evaluation, 2023 DOI:10.1520/JTE20220041

[39]	Machine Learning Predicts Five-Year Cancer Survival Rates Advances in Applied Mathematics, 2023 DOI:10.12677/AAM.2023.125254

[40]	HYBRID FEATURE SELECTION AND SUPPORT VECTOR MACHINE FRAMEWORK FOR PREDICTING MAINTENANCE FAILURES Applied Computer Science, 2023 DOI:10.35784/acs-2023-18

[41]	Deep Learning-Based Child Handwritten Arabic Character Recognition and Handwriting Discrimination Sensors, 2023 DOI:10.3390/s23156774

[42]	Machine learning-based centralized link coding attack detection in software-defined network Wireless Networks, 2023 DOI:10.1007/s11276-023-03483-6

[43]	Futuristic Approach to Cholesterol Detection by Utilizing Non-invasive Techniques Current Biotechnology, 2023 DOI:10.2174/2211550112666230419110914

[44]	Towards In-vehicle Driver Fainting Detection Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2023 DOI:10.1145/3580585.3607172

[45]	A hybrid deep learning model for Bitcoin price prediction: data decomposition and feature selection Applied Economics, 2023 DOI:10.1080/00036846.2023.2276093

[46]	Machine learning as a tool to identify inpatients who are not at risk of adverse drug events in a large dataset of a tertiary care hospital in the USA British Journal of Clinical Pharmacology, 2023 DOI:10.1111/bcp.15846

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[50]	Empirical Analysis of Machine Learning Models towards Adaptive Network Intrusion Detection Systems 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), 2022 DOI:10.1109/ICSSIT53264.2022.9716526

[51]	A feature selection strategy for improving software maintainability prediction Intelligent Data Analysis, 2022 DOI:10.3233/IDA-215825

[52]	A hybrid machine learning model for intrusion detection in VANET Computing, 2022 DOI:10.1007/s00607-021-01001-0

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[56]	Application o f Advanced Machine Learning and Artificial Neural Network Methods in Wireless Sensor Networks Based Applications International Journal of Engineering and Advanced Technology, 2022 DOI:10.35940/ijeat.C3394.0211322

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[58]	Effective Attribute Selection for Multi-dimensional Root Cause Analysis 2022 IEEE 33rd International Symposium on Software Reliability Engineering (ISSRE), 2022 DOI:10.1109/ISSRE55969.2022.00039

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[61]	Computational Intelligence in Data Mining Smart Innovation, Systems and Technologies, 2022 DOI:10.1007/978-981-16-9447-9_5

[62]	Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods Applied Sciences, 2022 DOI:10.3390/app12105256

[63]	Enhancing Detection of R2L Attacks by Multistage Clustering Based Outlier Detection Wireless Personal Communications, 2022 DOI:10.1007/s11277-022-09482-8

[64]	Risk Factors Identification and Prediction of Anemia among Women in Bangladesh using Machine Learning Techniques Current Women s Health Reviews, 2022 DOI:10.2174/1573404817666210215161108

[65]	Low-Latency Intrusion Detection Using a Deep Neural Network IT Professional, 2022 DOI:10.1109/MITP.2022.3154234

[66]	Detecting Zero-Day Intrusion Attacks Using Semi-Supervised Machine Learning Approaches IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3187116

[67]	Customer Lifetime Value Analysis Based on Machine Learning 2022 the 6th International Conference on Information System and Data Mining, 2022 DOI:10.1145/3546157.3546160

[68]	A Survey on Threat Hunting: Approaches and Applications 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC), 2022 DOI:10.1109/DSC55868.2022.00053

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[71]	Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3056614

[72]	Feature Selection and Analysis in Air Quality Data 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2021 DOI:10.1109/Confluence51648.2021.9376882

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[75]	Detection System for the Network Data Security with a profound Deep learning approach 2021 6th International Conference on Communication and Electronics Systems (ICCES), 2021 DOI:10.1109/ICCES51350.2021.9488967

[76]	A New Feature Selection Approach and Classification Technique for Current Intrusion Detection System 2021 6th International Conference on Computer Science and Engineering (UBMK), 2021 DOI:10.1109/UBMK52708.2021.9559011

[77]	Signal Anomaly Detection of Bridge SHM System Based on Two-Stage Deep Convolutional Neural Networks Structural Engineering International, 2021 DOI:10.1080/10168664.2021.1983914

[78]	Is it possible developing reliable prediction models considering only the pipe’s age for decision-making in sewer asset management? Journal of Modelling in Management, 2021 DOI:10.1108/JM2-11-2019-0258

[79]	A Powerful Ensemble Learning Approach for Improving Network Intrusion Detection System (NIDS) 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), 2021 DOI:10.1109/ICDS53782.2021.9626727

[80]	SABADT: Hybrid Intrusion Detection Approach for Cyber Attacks Identification in WLAN IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3129600

[81]	Network Anomaly Detection Technology Based on Deep Learning 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), 2021 DOI:10.1109/ICFTIC54370.2021.9647222

[82]	Quotidian Sales Forecasting using Machine Learning 2021 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2021 DOI:10.1109/ICSES52305.2021.9633975

[83]	Detection of Phising Websites using Machine Learning Approaches 2021 International Conference on Data Science and Its Applications (ICoDSA), 2021 DOI:10.1109/ICoDSA53588.2021.9617482

[84]	An Efficient Mixed Attribute Outlier Detection Method for Identifying Network Intrusions International Journal of Information Security and Privacy, 2020 DOI:10.4018/IJISP.2020070107

[85]	APT datasets and attack modeling for automated detection methods: A review Computers & Security, 2020 DOI:10.1016/j.cose.2020.101734

[86]	A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2020 DOI:10.1109/WIECON-ECE52138.2020.9397945

[87]	Dos attack forecasting: A comparative study on wrapper feature selection 2020 International Conference on Intelligent Systems and Computer Vision (ISCV), 2020 DOI:10.1109/ISCV49265.2020.9204323

[88]	Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things Symmetry, 2020 DOI:10.3390/sym12030454

[89]	Investigating and Suggesting the Evaluation Dataset for Image Classification Model IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3024575

[90]	A Survey on Machine Learning Techniques for Cyber Security in the Last Decade IEEE Access, 2020 DOI:10.1109/ACCESS.2020.3041951

[91]	A Deep Learning Approach for Network Intrusion Detection Based on NSL-KDD Dataset 2019 IEEE 13th International Conference on Anti-counterfeiting, Security, and Identification (ASID), 2019 DOI:10.1109/ICASID.2019.8925239

[92]	Autocalibration of Outlier Threshold with Autoencoder Mean Probability Score Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, 2019 DOI:10.1145/3375959.3375978

[93]	Ensembles for feature selection: A review and future trends Information Fusion, 2019 DOI:10.1016/j.inffus.2018.11.008

[94]	Wireless Sensor Networks Intrusion Detection Based on SMOTE and the Random Forest Algorithm Sensors, 2019 DOI:10.3390/s19010203

[95]	COSMOS: Collaborative, Seamless and Adaptive Sentinel for the Internet of Things Sensors, 2019 DOI:10.3390/s19071492

[96]	Random Forest Based Optimal Feature Selection for Partial Discharge Pattern Recognition in HV Cables IEEE Transactions on Power Delivery, 2019 DOI:10.1109/TPWRD.2019.2918316

[97]	Autocalibration of Outlier Threshold with Autoencoder Mean Probability Score Proceedings of the 2019 2nd Artificial Intelligence and Cloud Computing Conference, 2019 DOI:10.1145/3375959.3375978

[98]	Analysis of Feature Selection and Ensemble Classifier Methods for Intrusion Detection International Journal of Natural Computing Research, 2018 DOI:10.4018/IJNCR.2018010104

[99]	Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis 2018 3rd International Conference on Computer and Communication Systems (ICCCS), 2018 DOI:10.1109/CCOMS.2018.8463300

[100]	Survey on SDN based network intrusion detection system using machine learning approaches Peer-to-Peer Networking and Applications, 2018 DOI:10.1007/s12083-017-0630-0

[101]	Comparison of Recurrent Neural Network Algorithms for Intrusion Detection Based on Predicting Packet Sequences 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE), 2018 DOI:10.1109/CCECE.2018.8447793

[102]	A Hybrid Feature Selection Method for Improved Detection of Wired/Wireless Network Intrusions Wireless Personal Communications, 2017 DOI:10.1007/s11277-017-4949-x

[103]	A feature selection algorithm for IDS 2017 International Conference on Computer Science and Engineering (UBMK), 2017 DOI:10.1109/UBMK.2017.8093538

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