Real-time DDoS detection in software-defined networks using machine learning

Real-time DDoS detection in software-defined networks using machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.

Xehetasun bibliografikoak
Egile Nagusiak:	Hasan, Kadir, Hossain, Kaji Sajjad, Apurbo, GM Mohaiminuzzaman, Islam, MD Zubairul, Alam, Md Shakibul
Beste egile batzuk:	Hossain, Muhammad Iqbal
Formatua:	Thesis
Hizkuntza:	English
Argitaratua:	Brac University 2024
Gaiak:	Distributed denial of service DDoS attacks SDN Machine learning CICIoT2023 Cyber threats Intrusion detection systems (Computer security). Real-time data processing. Denial of service attacks. Computer networks > Security measures.
Sarrera elektronikoa:	http://hdl.handle.net/10361/24344

Antzeko izenburuak

Mitigating DDoS attacks using a resource sharing network
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Argitaratua: (2022)

Real time performance analysis on DDoS attack detection using machine learning
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Analysis on IOT Botnet and DDOS attack
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A machine learning based approach for DDos attack detection
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Cyber threats and scams in FinTech organizations: a brief overview of financial fraud cases, future challenges, and recommended solutions in Bangladesh
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Analysis of cybersecurity threats to financial and accounting data: implications for organizational risk management
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Intrusion of Malware (DDoS) detection in IoT devices using Machine Learning on Cyberspace
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Intrusion detection system for various IoT attacks on MQTT protocol
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Role of cyber security in ensuring safety and security of data in financial institutions
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Health threats as nontraditional security challenges for Bangladesh
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Systematic analysis on peer-to-peer botnet attack detection
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Service marketing analysis of MTB Securities Ltd
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Argitaratua: (2012)

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Demystifying machine learning models for IOT attack detection with explainable AI
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