Systematic analysis on peer-to-peer botnet attack detection
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
| Hlavní autoři: | , , , , |
|---|---|
| Další autoři: | |
| Médium: | Diplomová práce |
| Jazyk: | English |
| Vydáno: |
Brac University
2024
|
| Témata: | |
| On-line přístup: | http://hdl.handle.net/10361/23221 |
| id |
10361-23221 |
|---|---|
| record_format |
dspace |
| spelling |
10361-232212024-06-06T21:00:47Z Systematic analysis on peer-to-peer botnet attack detection Binte istiaq, Faiza E Mohammad, Rubaiyat Tasnia, Moriom Hassan, Kazi Moinul Tabassum, Tanjim Chakrabarty, Dr. Amitabha Department of Computer Science and Engineering, Brac University Botnet Peer to peer Honeypots AutoBotCatcher SDN PeerGrep Computer security. Computer networks--Security measures. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 46-50). "Botnet” refers to a network of compromised machines that the bot master remotely controls to prosecute innumerable malicious activities through a CC server and mis cellaneous slave machines. It is possible to categorize botnets as centralized (CC) or decentralized (P2P). According to their distributed functionality,recently P2P botnets is the most significant risks to network security . In this paper, we sys tematically analyze and compare some very recent peer-to-peer botnet algorithms and methods such as Honeypots, AutoBotCatcher, SDN, and PeerGrep to ascertain the most appropriate one for real-world applications. To perform this comparison, we examine AutuBotCatcher, an algorithm that utilizes the community detection method, Honeypot system, where we focus on the Nepethesis honeypot method. Additionally, the PeerGrep system integrates the PeerGrep algorithm, CART algo rithm, and P2P traffic in SDN to automate and flexibly manage flow entries through machine learning. Faiza Binte istiaq Rubaiyat E Mohammad Moriom Tasnia Kazi Moinul Hassan Tanjim Tabassum B.Sc in Computer Science 2024-06-06T10:14:54Z 2024-06-06T10:14:54Z 2022 2022-09 Thesis ID: 18301227 ID: 18301103 ID: 18301058 ID: 18301290 ID: 20101629 http://hdl.handle.net/10361/23221 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 50 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Botnet Peer to peer Honeypots AutoBotCatcher SDN PeerGrep Computer security. Computer networks--Security measures. |
| spellingShingle |
Botnet Peer to peer Honeypots AutoBotCatcher SDN PeerGrep Computer security. Computer networks--Security measures. Binte istiaq, Faiza E Mohammad, Rubaiyat Tasnia, Moriom Hassan, Kazi Moinul Tabassum, Tanjim Systematic analysis on peer-to-peer botnet attack detection |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Chakrabarty, Dr. Amitabha |
| author_facet |
Chakrabarty, Dr. Amitabha Binte istiaq, Faiza E Mohammad, Rubaiyat Tasnia, Moriom Hassan, Kazi Moinul Tabassum, Tanjim |
| format |
Thesis |
| author |
Binte istiaq, Faiza E Mohammad, Rubaiyat Tasnia, Moriom Hassan, Kazi Moinul Tabassum, Tanjim |
| author_sort |
Binte istiaq, Faiza |
| title |
Systematic analysis on peer-to-peer botnet attack detection |
| title_short |
Systematic analysis on peer-to-peer botnet attack detection |
| title_full |
Systematic analysis on peer-to-peer botnet attack detection |
| title_fullStr |
Systematic analysis on peer-to-peer botnet attack detection |
| title_full_unstemmed |
Systematic analysis on peer-to-peer botnet attack detection |
| title_sort |
systematic analysis on peer-to-peer botnet attack detection |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/23221 |
| work_keys_str_mv |
AT binteistiaqfaiza systematicanalysisonpeertopeerbotnetattackdetection AT emohammadrubaiyat systematicanalysisonpeertopeerbotnetattackdetection AT tasniamoriom systematicanalysisonpeertopeerbotnetattackdetection AT hassankazimoinul systematicanalysisonpeertopeerbotnetattackdetection AT tabassumtanjim systematicanalysisonpeertopeerbotnetattackdetection |
| _version_ |
1814307087952379904 |