Denial of service detection and prevention in set - top box using machine learning (Record no. 6488)

MARC details
000 -LEADER
fixed length control field 01876nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122850.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 190618b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Lekha P H (93617023)
9 (RLIN) 15822
245 ## - TITLE STATEMENT
Title Denial of service detection and prevention in set - top box using machine learning
300 ## - PHYSICAL DESCRIPTION
Extent MSC CS 2017-2019
500 ## - GENERAL NOTE
General note Denial of Service (DoS) is one of the most occurring and the most dangerous<br/>attacks in the Security world. DoS is an attack meant to shut down a machine<br/>or network, making it inaccessible to its intended users. DoS attacks<br/>accomplish this by <br/>ooding the target with trac, or sending it information<br/>that triggers a crash. DoS attacks do not typically result in the theft or loss<br/>of signicant information or other assets, they can cost the victim a great<br/>deal of time and money to handle.<br/>Set Top box is an endpoint of entire broadcasting unit. It is deployed in<br/>the user's side & it contains some information about the customer. Nowadays<br/>Set Top Box (STB) came with internet connection facility & we know that<br/>every device that connected to the internet has a chance to be vulnerable to<br/>the security threats. So we need a system to provide security to the STB.<br/>In this project I have developed a Tool for real time DoS detection using<br/>supervised Machine Learning Algorithm. Here I used Decision tree classier<br/>as my machine learning model for detecting DoS attack in Set Top Box. I<br/>have considered a multi class detection for DoS attack including ICMP Flood,<br/>UDP Flood, TCP Flood and Ping Death. So my proposed system is aims to<br/>detect these four types of DoS attack in Set-Top Box live network trac.
502 ## - DISSERTATION NOTE
Degree type MSC CS
Name of granting institution 2017-2019
Year degree granted INT
-- Dr. Elizabeth Sherly
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element DENIAL OF SERVICE
9 (RLIN) 15823
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element SET TOP BOX
9 (RLIN) 15824
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING ALGORITHM
9 (RLIN) 15825
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   18/06/2019   R-1492 18/06/2019 18/06/2019 Project Reports