Cutting Edge Trends in Deception Based Intrusion Detection Systems

Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
www.scirp.net/journal/jis
E-mail: [email protected]

Citations

"Cutting Edge Trends in Deception Based Intrusion Detection Systems—A Survey"
written by Onyekware U. Oluoha, Terungwa S. Yange, George E. Okereke, Francis S. Bakpo,
published by Journal of Information Security, Vol.12 No.4, 2021

has been cited by the following article(s):

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CrossRef

[1]	Domain-independent deception: A new taxonomy and linguistic analysis
	arXiv preprint arXiv …, 2024

[2]	Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graphsage models
	Results in Control and Optimization, 2024

[3]	An appraisal for the identification of a novel approach to IDPS in cloud environment
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[4]	Comparing a Hybrid Multi-layered Machine Learning Intrusion Detection System to Single-layered and Deep Learning Models
	2023

[5]	DDoS ATTACK DETECTION USING SUPERVISED MACHINE LEARNING ALGORITHMS OVER THE CIDDOS2019 DATASET
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[6]	Принцип синтезу мультикомп'ютерних систем з комбінованих антивірусних приманок і пасток та контролеру прийняття рішень для виявлення зловмисного …
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[7]	Development of a Machine-Learning Intrusion Detection System and Testing of Its Performance Using a Generative Adversarial Network
	Sensors, 2023

[8]	INFLUNCES OF GPU CHALLENGES ON CLOUD COMPUTING WITH WEB TECHNOLOGY APPLICATIONS
	The Seybold Report, 2023

[9]	The survey and meta-analysis of the attacks, transgressions, countermeasures and security aspects common to the Cloud, Edge and IoT
	Neurocomputing, 2023

[10]	IT Investment And Islamic Banking Performance In Indonesia: Do Sukuk Issuance And Sariah Governance Matter?
	Banks and Bank …, 2023

[11]	Leveraging computational intelligence techniques for defensive deception: a review, recent advances, open problems and future directions
	Sensors, 2022

[1]	Intrusion detection with HACDT-Net and TRBM-Net using a hybrid deep learning framework with enhanced sampling techniques Scientific Reports, 2026 DOI:10.1038/s41598-026-41422-5

[2]	Detection of computer attacks based on sonification of network traffic CEUR Workshop Proceedings, Vol-3963: Proceedings of the The 6th International Workshop on Intelligent Information Technologies & Systems of Information Security (IntelITSIS 2025), 2025 DOI:10.55056/ceur-ws.org/Vol-3963/paper21.pdf

[3]	Fifth International Conference on Computing and Network Communications Lecture Notes in Electrical Engineering, 2025 DOI:10.1007/978-981-97-4540-1_35

[4]	Domain-independent deception: a new taxonomy and linguistic analysis Frontiers in Big Data, 2025 DOI:10.3389/fdata.2025.1581734

[5]	Cyber security attack recognition on cloud computing networks based on graph convolutional neural network and graphsage models Results in Control and Optimization, 2024 DOI:10.1016/j.rico.2024.100423

[6]	IT investment and Islamic banking performance in Indonesia: Do Sukuk issuance and Sariah governance matter? Banks and Bank Systems, 2023 DOI:10.21511/bbs.18(2).2023.07

[7]	The survey and meta-analysis of the attacks, transgressions, countermeasures and security aspects common to the Cloud, Edge and IoT Neurocomputing, 2023 DOI:10.1016/j.neucom.2023.126533

[8]	An appraisal for the identification of a novel approach to IDPS in cloud environment APPLIED DATA SCIENCE AND SMART SYSTEMS, 2023 DOI:10.1063/5.0177661

[9]	Leveraging Computational Intelligence Techniques for Defensive Deception: A Review, Recent Advances, Open Problems and Future Directions Sensors, 2022 DOI:10.3390/s22062194

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