A Decision Tree Classifier for Intrusion Detection Priority Tagging

Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
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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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[15]	Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
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[16]	Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
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[17]	A Case Study on Using Deep Learning for Network Intrusion Detection
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[18]	Hybrid approach to provide situational awareness for information security in computational environments
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[19]	Intrusion Detection Techniques: A Review
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[20]	A Hybrid Architecture to Enrich Context Awareness through Data Correlation
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[21]	Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space
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[22]	Machine Learning and Deep Learning Methods for Cybersecurity
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[23]	Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME
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[24]	A HYBRID INTRUSION DETECTION TECHNIQUE BASED ON IRF & AODE FOR KDD-CUP 99 DATASET
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[26]	Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset
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[5]	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

[6]	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

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

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[9]	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

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

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

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

[15]	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

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

[17]	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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