Classification of ddos attack using artificial neural network (Record no. 65460)

MARC details
000 -LEADER
fixed length control field 01363nam a22001337a 4500
100 ## - MAIN ENTRY--AUTHOR NAME
Personal name Roheen Qamar
-- 17MSCS18
-- Supervisor Dr. Fareed Ahmed Jokhio
245 ## - TITLE STATEMENT
Title Classification of ddos attack using artificial neural network
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Nawabshah:
Name of publisher QUEST,
Year of publication 2019.
300 ## - PHYSICAL DESCRIPTION
Number of Pages 49p
500 ## - GENERAL NOTE
General note ABSTRACT<br/><br/><br/>Distributed Denial of Service (DDoS) attack is one of the well-known threats to any computer or Internet Service. DDoS attack is an attempt to make a network resource or internet unavailable to its intended users. Internet is running on a large numbers servers kept in data centers. When a server or its network is overloaded with users request then the server or networks stop functioning properly and deny service to the genuine requests. As the internet growing we face large amount of cyber-attack. This research work presents the DDoS attack through various angles including we first recognize different kinds of DDoS attacks and then propose a new methodology to develop a Model for detecting the DDoS attacks using Neural Networks. We used three ANN networks and compare them to detect the DDoS attack.<br/>Keywords: DDoS Attack, Feed forward Neural Network, Case Case Forward Neural Network, Fitting Neural Network, KDD Cup dataset.<br/>
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Department of Information Technology
856 ## - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://tinyurl.com/mr2wpepb
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type Thesis and Dissertation
Holdings
Withdrawn status Lost status Home library Current library Date acquired Accession Number Koha item type
    Research Section Research Section 27/09/2019 MP/46-526 Thesis and Dissertation
    Research Section Research Section 18/12/2023 MP/55-697 Thesis and Dissertation