A Stateful Firewall Packet Analysis Framework for Mitigating Session Fixation Attacks/ (Record no. 93775)

MARC details
000 -LEADER
fixed length control field 02771nam a22002177a 4500
003 - CONTROL NUMBER IDENTIFIER
control field KE-MeUCS
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20260610173106.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 260610b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER
Source
040 ## - CATALOGING SOURCE
Transcribing agency KE-MeUCS
Modifying agency KE-MeUCS
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number TK5105.5.K3 2025
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Kailanya, Eunice
245 ## - TITLE STATEMENT
Title A Stateful Firewall Packet Analysis Framework for Mitigating Session Fixation Attacks/
Statement of responsibility, etc Eunice Kailanya
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Meru:
Name of publisher, distributor, etc Meru University of Science and Technology,
Date of publication, distribution, etc 2025.
300 ## - PHYSICAL DESCRIPTION
Extent xiv,153p.
500 ## - GENERAL NOTE
General note Includes Reference
520 ## - SUMMARY, ETC.
Summary, etc Protecting the networks against web attacks has become increasingly critical. As network<br/>attacks continue to evolve in complexity and sophistication, stateful firewall solutions have<br/>proven to be insufficient in defending against session fixation attacks. Session fixation<br/>attacks pose a significant threat to web security by exploiting vulnerabilities in session<br/>management to hijack authenticated user sessions. Existing stateful firewall models can<br/>filter attacks such as denial of service, distributed denial of service, man-in-the-middle,<br/>malware, ransomware and spamming. However, they are unable to filter session fixation<br/>attacks due to their filtering mechanisms. The aim of this study was to develop a stateful<br/>firewall packet analysis model that operates in network layer to detect and filter session<br/>fixation attack. By maintaining state information across network sessions, the model<br/>analyzed packet sequences and patterns to identify anomalies indicative of session fixation<br/>attempts. Gradient booster classifier algorithm was incorporated into the model to enhance<br/>accuracy in analyzing the packet. Virtual machine simulation experiment was performed to<br/>evaluate the accuracy of the model using Cross-Site Scripting (XSS) datasets vulnerable to<br/>session fixation attacks alongside normal user traffic. The model detection rate, false<br/>positive and false negative metrics was measured to assess the accuracy of the model. The<br/>experimental results demonstrated that the model effectively detected and mitigated session<br/>fixation attacks by analyzing session parameters and maintaining session state consistency.<br/>Experimental evaluation validated the high model detection accuracy level of 98.5 % with<br/>minimal false positives. By tracking the state of each session and analyzing packet-level<br/>data the model is capable of detecting suspicious patterns associated with session fixation<br/>attempts. The adoption and integration of the model into the network security framework not<br/>only strengthens protection at the application layer but also reduces the risk of session<br/>hijacking
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://repository.must.ac.ke/handle/123456789/1596
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Thesis
Cataloguer Mercy Musungu
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Cataloger Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
    Library of Congress Classification     Meru University Meru University Periodical Section 10/06/2026 Meru University of science and Technology (MUST) 0.00 Mercy Musungu   TK5105.5.K3 2025 26-39380 10/06/2026 10/06/2026 Thesis

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