Please use this identifier to cite or link to this item: https://dspace.ctu.edu.vn/jspui/handle/123456789/110731
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorThái, Minh Tuấn-
dc.contributor.authorLâm, Hoàng Khang-
dc.date.accessioned2025-02-04T01:58:10Z-
dc.date.available2025-02-04T01:58:10Z-
dc.date.issued2024-
dc.identifier.otherB2014748-
dc.identifier.urihttps://dspace.ctu.edu.vn/jspui/handle/123456789/110731-
dc.description53 Trvi_VN
dc.description.abstractCybersecurity is a critical concern in today's interconnected world, with networks facing constant threats. Intrusion Detection Systems (IDS) are essential tools for safeguarding networks by identifying and responding to unauthorized access and malicious activities. However, traditional IDS often struggle to adapt to the evolving threat landscape. This thesis proposes a novel approach to enhance IDS capabilities by leveraging advanced machine learning techniques. The primary objective is to develop a robust and intelligent IDS capable of accurately detecting and classifying a wide range of network intrusion patterns. We investigated and compared multiple machine learning algorithms on the UNSW-NB15 dataset, which comprises both normal and malicious network traffic. Our experimental results demonstrate the effectiveness of our proposed machine learning-based IDS, with Decision Tree, Random Forest, XGBoost, and an ensemble model achieving impressive accuracy rates of 96.84%, 97.78%, 97.45%, and 97.52%, respectively, in multi-label classification on the UNSW-NB15 dataset.vi_VN
dc.language.isoenvi_VN
dc.publisherTrường Đại Học Cần Thơvi_VN
dc.subjectCÔNG NGHỆ THÔNG TIN - CHẤT LƯỢNG CAOvi_VN
dc.titleINTRUSION DETECTION USING MACHINE LEARNINGvi_VN
dc.title.alternativePHÁT HIỆN VÀ PHÂN LOẠI TẤN CÔNG MẠNG SỬ DỤNG MÁY HỌCvi_VN
dc.typeThesisvi_VN
Appears in Collections:Trường Công nghệ Thông tin & Truyền thông

Files in This Item:
File Description SizeFormat 
_file_
  Restricted Access
1.66 MBAdobe PDF
Your IP: 216.73.216.121


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.