Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation

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Journal of Information Security

ISSN Print: 2153-1234
ISSN Online: 2153-1242
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"Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation"
written by Sabah Alzahrani, Liang Hong,
published by Journal of Information Security, Vol.9 No.4, 2018

has been cited by the following article(s):

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[1]	Explainable AI-Based DDOS Attack Identification Method for IoT Networks
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[2]	Traffic data extraction and labeling for machine learning based attack detection in IoT networks
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[3]	Adaptive machine learning based distributed denial-of-services attacks detection and mitigation system for SDN-enabled iot
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[4]	Two-stage intelligent model for detecting malicious DDoS behavior
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[5]	A survey of machine learning methods for DDoS threats detection against SDN
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[6]	All-Packets-Based Multi-Rate DDoS Attack Detection Method in ISP Layer
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[7]	Distributed Denial of Service Attack Alleviated and Detected by Using Mininet and Software Defined Network
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[8]	Predictive machine learning-based integrated approach for DDoS detection and prevention
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[9]	Automation of network anomaly detection and mitigation with the use of IBN: A deployment case on KOREN
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[11]	Security Analysis Using Network Authentication on the University Campus
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[12]	Comparison of Software Simulation and Network Testbed of DDoS Attacks for IPv4 and IPv6 Networks
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[13]	Evaluation of Data Center Network Security based on Next-Generation Firewall
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[14]	DDOS ATTACK DETECTION IN DISTRIBUTED SDN SYSTEM USING DEEP LEARNING ALGORITHM
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[15]	Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier
	Machine Learning Applications in …, 2021

[16]	Feature Selection Intrusion Detection System for The Attack Classification with Data Summarization
	Machine Learning Applications in Engineering …, 2021

[17]	IoT Güvenliği için Kullanılan Makine Öğrenimi ve Derin Öğrenme Modelleri Üzerine Bir Derleme
	Bilişim Teknolojileri Dergisi, 2021

[18]	Method for assessing the operating quality of a switching node in a technological transmission network data in the context of DDoS attacks by an intruder
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[19]	Machine learning approaches to IoT security: A systematic literature review
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[20]	A simulation work for generating a novel dataset to detect distributed denial of service attacks on Vehicular Ad hoc NETwork systems
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[21]	Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
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[22]	A fast all-packets-based DDoS attack detection approach based on network graph and graph kernel
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[23]	Securing of Software-Defined Networking (SDN) from DDoS Attack Using a Blockchain
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[24]	Testbeds, Attacks, and Dataset Generation for Big Data Cluster: A System Application for Big Data Platform Security Analysis
	2020

[25]	A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks
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[26]	Session-level Adversary Intent-Driven Cyberattack Simulator
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[27]	Low-rate DDoS attacks detection method using data compression and behavior divergence measurement
	2020

[28]	ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ
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[29]	An Improved adhoc on demand distance vector protocol for mobile adhoc networks
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[30]	ANCERT: APLICACIÓN DE TÉCNICAS DE MACHINE LEARNING A LA SEGURIDAD
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[31]	Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)
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[32]	Detection of Unknown Insider Attack on Components of Big Data System: A Smart System Application for Big Data Cluster


[33]	ODVOZOVÁNÍ VZORU PRO MITIGACI DDOS ÚTOKŮ


[34]	Implementation of Network Intrusion Detection System

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[2]	Cyber Security Attacks Detecting Thread in the Virtual World of Corporate Sectors International Journal of Case Studies in Business, IT, and Education, 2023 DOI:10.47992/IJCSBE.2581.6942.0261

[3]	Privacy Preservation and Secured Data Storage in Cloud Computing Advances in Information Security, Privacy, and Ethics, 2023 DOI:10.4018/979-8-3693-0593-5.ch017

[4]	CBTCset: A Reference Dataset for Detecting Misbehavior Attacks in CBTC Networks 2023 IEEE 34th International Symposium on Software Reliability Engineering Workshops (ISSREW), 2023 DOI:10.1109/ISSREW60843.2023.00047

[5]	Detection of DDoS attacks in D2D communications using machine learning approach Computer Communications, 2023 DOI:10.1016/j.comcom.2022.11.013

[6]	Security Analysis Using Network Authentication on the University Campus 2022 2nd International Conference on Computation, Communication and Engineering (ICCCE), 2022 DOI:10.1109/ICCCE55785.2022.10036197

[7]	Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT Sensors, 2022 DOI:10.3390/s22072697

[8]	Distributed Computing for Emerging Smart Networks Communications in Computer and Information Science, 2022 DOI:10.1007/978-3-030-99004-6_6

[9]	All-Packets-Based Multi-Rate DDoS Attack Detection Method in ISP Layer Security and Communication Networks, 2022 DOI:10.1155/2022/7551107

[10]	Automation of network anomaly detection and mitigation with the use of IBN: A deployment case on KOREN 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022 DOI:10.1109/WoWMoM54355.2022.00056

[11]	Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments IEEE Access, 2021 DOI:10.1109/ACCESS.2021.3062909

[12]	A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks International Journal of Communication Systems, 2020 DOI:10.1002/dac.4401

[13]	A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks International Journal of Communication Systems, 2020 DOI:10.1002/dac.4401

[14]	Session-level Adversary Intent-Driven Cyberattack Simulator 2020 IEEE/ACM 24th International Symposium on Distributed Simulation and Real Time Applications (DS-RT), 2020 DOI:10.1109/DS-RT50469.2020.9213690

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