DDoS Detection using Machine Learning

被引：0

作者：

Nagah, Nour Ahmed ^{[1
]}

Bahaa, Mariam ^{[1
]}

Elsersy, Wael Farouk ^{[2
]}

机构：

[1] Univ Ain Shams, Fac Engn, Cess Dept, Cairo, Egypt

[2] Modern Sci & Arts Univ, Fac Comp Sci, Giza, Egypt

来源：

2024 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE AND SMART INNOVATION, ICMISI 2024 | 2024年

关键词：

Distributed Denial of service attack; Machine learning; APA DDoS; Unsupervised learning; Botnet; Bengin; Random Forrest; ATTACKS; NETWORK;

D O I：

10.1109/ICMISI61517.2024.10580319

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the cybersecurity domain, Denial of Service (DoS) attacks maliciously disrupt the availability of systems, inundating them with packets or requests. Distributed Denial of Service (DDoS) attacks compound this challenge, utilizing multiple compromised sources. Recognizing and classifying these attacks swiftly is critical for safeguarding online platforms. Our research focuses on DDoS attacks, leveraging Machine Learning (ML) to distinguish between normal and malicious network behavior. Anchored by the apaddos-dataset, our approach aims to empower systems to autonomously identify and respond to threats, enhancing digital security.

引用

页码：94 / 100

页数：7

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