A Decision Tree Classifier for Intrusion Detection Priority Tagging

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Journal of Computer and Communications

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"A Decision Tree Classifier for Intrusion Detection Priority Tagging"
written by Adel Ammar,
published by Journal of Computer and Communications, Vol.3 No.4, 2015

has been cited by the following article(s):

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[2]	Comparative analysis of intrusion detection systems and machine learning based model analysis through decision tree
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[3]	Enhanced Quantum-Secure Ensemble Intrusion Detection Techniques for Cloud Based on Deep Learning
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[18]	Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)
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[19]	Detecting Stealth-based Attacks in Large Campus Networks
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[21]	Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
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[22]	Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
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[23]	A Case Study on Using Deep Learning for Network Intrusion Detection
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[24]	Deep learning approaches for network intrusion detection
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[26]	Intrusion Detection Techniques: A Review
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[27]	A Hybrid Architecture to Enrich Context Awareness through Data Correlation
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[28]	Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space
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[30]	Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME
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[31]	A HYBRID INTRUSION DETECTION TECHNIQUE BASED ON IRF & AODE FOR KDD-CUP 99 DATASET
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[32]	Network Intrusion Detection Using Flow Statistics
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[33]	Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset
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[38]	INTRUSION DETECTION SYSTEM (IDS) DEVELOPMENT USING TREE-BASED MACHINE LEARNING ALGORITHMS

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[9]	Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3296444

[10]	Enhanced Quantum-Secure Ensemble Intrusion Detection Techniques for Cloud Based on Deep Learning Cognitive Computation, 2023 DOI:10.1007/s12559-023-10139-2

[11]	Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3296444

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[16]	Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection Computer Communications, 2022 DOI:10.1016/j.comcom.2022.03.009

[17]	Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection Computer Communications, 2022 DOI:10.1016/j.comcom.2022.03.009

[18]	Machine Learning Models to Classify Normal and Fibrotic Mouse Liver Model using Dielectric Properties 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022 DOI:10.1109/BIBM55620.2022.9995397

[19]	Intrusion Detection System Based on Machine Learning Techniques: A Survey 2022 2nd International Conference on Advances in Engineering Science and Technology (AEST), 2022 DOI:10.1109/AEST55805.2022.10413072

[20]	A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets Mathematics, 2021 DOI:10.3390/math9070751

[21]	Intelligent Systems, Technologies and Applications Advances in Intelligent Systems and Computing, 2021 DOI:10.1007/978-981-16-0730-1_4

[22]	A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets Mathematics, 2021 DOI:10.3390/math9070751

[23]	Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity Energies, 2020 DOI:10.3390/en13102509

[24]	Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity Energies, 2020 DOI:10.3390/en13102509

[25]	Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey Applied Sciences, 2019 DOI:10.3390/app9204396

[26]	Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset Future Generation Computer Systems, 2019 DOI:10.1016/j.future.2019.05.041

[27]	Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey Applied Sciences, 2019 DOI:10.3390/app9204396

[28]	A Case Study on using Deep Learning for Network Intrusion Detection MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM), 2019 DOI:10.1109/MILCOM47813.2019.9020824

[29]	Network Intrusion Detection Using Flow Statistics 2018 IEEE Statistical Signal Processing Workshop (SSP), 2018 DOI:10.1109/SSP.2018.8450709

[30]	A hybrid architecture to enrich context awareness through data correlation Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018 DOI:10.1145/3167132.3167405

[31]	Machine Learning and Deep Learning Methods for Cybersecurity IEEE Access, 2018 DOI:10.1109/ACCESS.2018.2836950

[32]	Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space Cognitive Computation, 2018 DOI:10.1007/s12559-018-9564-y

[33]	Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME SSRN Electronic Journal , 2018 DOI:10.2139/ssrn.3170295

[34]	A hybrid architecture to enrich context awareness through data correlation Proceedings of the 33rd Annual ACM Symposium on Applied Computing, 2018 DOI:10.1145/3167132.3167405

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