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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CrossRef

[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]	DDoS Attack Detection Using Predictive Machine Learning (ML) Algorithms in Wireless Body Area Network Environments
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[4]	Reassembler-Towards a Global DDoS Attack Analysis Using Attack Fingerprints
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[5]	CBTCset: A Reference Dataset for Detecting Misbehavior Attacks in CBTC Networks
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[6]	Feature Selection of Distributed Denial of Service (DDos) IoT Bot Attack Detection Using Machine Learning Techniques
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[7]	Cyber Security Attacks Detecting Thread in the Virtual World of Corporate Sectors
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[8]	Detection of DDoS Attacks Using Variational Autoencoder-Based Deep Neural Network
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[9]	Simulator of Distributed Datasets for Pulse-wave DDoS Attacks
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[10]	Emulator for Distributed DDoS Datasets (EDDD)
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[11]	Generation of Distributed Denial of Service Network Data with Phyton and Scapy
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[12]	Automation of network anomaly detection and mitigation with the use of IBN: A deployment case on KOREN
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[13]	Network Congestion Solution for FTP Services Based on Distributed Firewall and Snort
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[14]	Security Analysis Using Network Authentication on the University Campus
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[15]	Adaptive machine learning based distributed denial-of-services attacks detection and mitigation system for SDN-enabled iot
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[16]	Two-stage intelligent model for detecting malicious DDoS behavior
	Sensors, 2022

[17]	A survey of machine learning methods for DDoS threats detection against SDN
	… Computing for Emerging Smart Networks: Third …, 2022

[18]	All-Packets-Based Multi-Rate DDoS Attack Detection Method in ISP Layer
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[19]	Distributed Denial of Service Attack Alleviated and Detected by Using Mininet and Software Defined Network
	Mashadani, M Ilyas - Webology, 2022

[20]	Predictive machine learning-based integrated approach for DDoS detection and prevention
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[21]	Detection of Unknown Insider Attack on Components of Big Data System: A Smart System Application for Big Data Cluster
	International Journal of Computer Network and Information Security, 2022

[22]	Исследование методов обнаружения и устранения киберугроз для корпоративных сетей
	… В ЭЛЕКТРОННОМ УПРАВЛЕНИИ ОРВСЭУ-2022) В …, 2022

[23]	All‐Packets‐Based Multi‐Rate DDoS Attack Detection Method in ISP Layer
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[24]	Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier
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[25]	Feature Selection Intrusion Detection System for The Attack Classification with Data Summarization
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[26]	IoT Güvenliği için Kullanılan Makine Öğrenimi ve Derin Öğrenme Modelleri Üzerine Bir Derleme
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[27]	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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[28]	Machine learning approaches to IoT security: A systematic literature review
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[29]	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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[30]	Machine Learning Approaches for Combating Distributed Denial of Service Attacks in Modern Networking Environments
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[31]	A fast all-packets-based DDoS attack detection approach based on network graph and graph kernel
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[32]	Securing of Software-Defined Networking (SDN) from DDoS Attack Using a Blockchain
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[33]	Comparison of Software Simulation and Network Testbed of DDoS Attacks for IPv4 and IPv6 Networks
	… Application in Informatics: Proceedings of 5th …, 2021

[34]	Evaluation of Data Center Network Security based on Next-Generation Firewall
	International Journal of Advanced …, 2021

[35]	DDOS ATTACK DETECTION IN DISTRIBUTED SDN SYSTEM USING DEEP LEARNING ALGORITHM
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[36]	Low-rate DDoS attacks detection method using data compression and behavior divergence measurement
	2020

[37]	Testbeds, Attacks, and Dataset Generation for Big Data Cluster: A System Application for Big Data Platform Security Analysis
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[38]	A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks
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[39]	Session-level Adversary Intent-Driven Cyberattack Simulator
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[40]	An Improved adhoc on demand distance vector protocol for mobile adhoc networks
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[41]	ΔΙΠΛΩΜΑΤΙΚΗ ΕΡΓΑΣΙΑ
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[42]	Aplicación de técnicas de Machine Learning a la seguridad
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[43]	ANCERT: APLICACIÓN DE TÉCNICAS DE MACHINE LEARNING A LA SEGURIDAD
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[44]	Implementation of Network Intrusion Detection System Using Soft Computing Algorithms (Self Organizing Feature Map and Genetic Algorithm)
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[45]	Implementation of Network Intrusion Detection System
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[46]	A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security


[47]	ODVOZOVÁNÍ VZORU PRO MITIGACI DDOS ÚTOKŮ


[48]	of the master graduate qualification work

[1]	A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security MANAS Journal of Engineering, 2024 DOI:10.51354/mjen.1197753

[2]	A Systematic Survey of Machine Learning and Deep Learning Models Used in Industrial Internet of Things Security MANAS Journal of Engineering, 2024 DOI:10.51354/mjen.1197753

[3]	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

[4]	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

[5]	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

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

[7]	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

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

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

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

[11]	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

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

[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]	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

[15]	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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