A Decision Tree Classifier for Intrusion Detection Priority Tagging

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Computer and Communications

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.org/journal/jcc
E-mail: jcc@scirp.org

Google-based Impact Factor: 1.12
Citations h5-index & Ranking*

Journals Menu

"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):

Google Scholar
CrossRef

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

[2]	Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection
	Computer …, 2022

[3]	Machine Learning Models to Classify Normal and Fibrotic Mouse Liver Model using Dielectric Properties
	… on Bioinformatics and …, 2022

[4]	Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems
	Indonesian Journal of …, 2021

[5]	Intrusion Alert Reduction Based on Unsupervised and Supervised Learning Algorithms
	B, MMD Siraj - International Journal of Innovative …, 2021

[6]	A comparative study of the performance of machine learning algorithms to detect malicious traffic in IoT networks
	Journal of Digital Convergence, 2021

[7]	A consolidated decision tree-based intrusion detection system for binary and multiclass imbalanced datasets
	2021

[8]	AI Approaches for IoT Security Analysis
	2021

[9]	Agrupamento e Classificação de Consumidores de Energia Rural Utilizando Random Forest e K-Nearest Neighbors
	2020

[10]	Cyber Threat Intelligence Using Deep Learning to Detect Abnormal Network Behavior
	2020

[11]	Agrupamento e Classificaçao de Consumidores de Energia Rural Utilizando Random Forest e K-Nearest Neighbors
	… de Automática-CBA, 2020

[12]	Bi-directional Recurrent Neural network for Intrusion Detection System (IDS) in the internet of things (IoT)
	2020

[13]	Detecting Stealth-based Attacks in Large Campus Networks
	2020

[14]	Designing an effective network forensic framework for the investigation of botnets in the Internet of Things
	2020

[15]	Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity
	2020

[16]	Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
	2019

[17]	A Case Study on Using Deep Learning for Network Intrusion Detection
	2019

[18]	Hybrid approach to provide situational awareness for information security in computational environments
	2018

[19]	Intrusion Detection Techniques: A Review
	2018

[20]	A Hybrid Architecture to Enrich Context Awareness through Data Correlation
	2018

[21]	Anomaly-Based Intrusion Detection Using Extreme Learning Machine and Aggregation of Network Traffic Statistics in Probability Space
	Cognitive Computation, 2018

[22]	Machine Learning and Deep Learning Methods for Cybersecurity
	2018

[23]	Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME
	Proceedings of 3rd International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2018

[24]	A HYBRID INTRUSION DETECTION TECHNIQUE BASED ON IRF & AODE FOR KDD-CUP 99 DATASET
	International Research Journal of Engineering and Technology, 2018

[25]	Network Intrusion Detection Using Flow Statistics
	2018

[26]	Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset
	2018

[27]	Anomaly-Based Intrusion Detection by Modeling Probability Distributions of Flow Characteristics
	2017

[28]	2: uma abordagem consciente de situação para segurança em infraestruturas computacionais
	Revista Brasileira de Computação Aplicada, 2016

[29]	HYBRID INTRUSION DETECTION SYSTEM FOR PRIVATE CLOUD: AN INTEGRATED APPROACH
	2016

[30]	Σύγχρονα εργαλεία, τεχνικές και μεθοδολογίες για τον χαρακτηρισμό κυβερνοεπιθέσεων και κακόβουλου λογισμικού
	2016

[1]	Detection of Network Attacks using Machine Learning and Deep Learning Models Procedia Computer Science, 2023 DOI:10.1016/j.procs.2022.12.401

[2]	Detection of Network Attacks using Machine Learning and Deep Learning Models Procedia Computer Science, 2023 DOI:10.1016/j.procs.2022.12.401

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

[4]	Detection of Network Attacks using Machine Learning and Deep Learning Models Procedia Computer Science, 2023 DOI:10.1016/j.procs.2022.12.401

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

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

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

[8]	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

[9]	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

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

[11]	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

[12]	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

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

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

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

[16]	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

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

[18]	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

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

[20]	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

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

[22]	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

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

[24]	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

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Computer and Communications

Journal of Computer and Communications

Journals Menu

Home

About SCIRP

Service

Policies