000			01876nam a22001937a 4500
003			OSt
005			20220107122850.0
008			190618b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aLekha P H (93617023) _915822
245			_aDenial of service detection and prevention in set - top box using machine learning
300			_aMSC CS 2017-2019
500			_aDenial of Service (DoS) is one of the most occurring and the most dangerous attacks in the Security world. DoS is an attack meant to shut down a machine or network, making it inaccessible to its intended users. DoS attacks accomplish this by ooding the target with trac, or sending it information that triggers a crash. DoS attacks do not typically result in the theft or loss of signicant information or other assets, they can cost the victim a great deal of time and money to handle. Set Top box is an endpoint of entire broadcasting unit. It is deployed in the user's side & it contains some information about the customer. Nowadays Set Top Box (STB) came with internet connection facility & we know that every device that connected to the internet has a chance to be vulnerable to the security threats. So we need a system to provide security to the STB. In this project I have developed a Tool for real time DoS detection using supervised Machine Learning Algorithm. Here I used Decision tree classier as my machine learning model for detecting DoS attack in Set Top Box. I have considered a multi class detection for DoS attack including ICMP Flood, UDP Flood, TCP Flood and Ping Death. So my proposed system is aims to detect these four types of DoS attack in Set-Top Box live network trac.
502			_bMSC CS _c2017-2019 _dINT _eDr. Elizabeth Sherly
650			_aDENIAL OF SERVICE _915823
650			_aSET TOP BOX _915824
650			_aMACHINE LEARNING ALGORITHM _915825
942			_2ddc _cPR
999			_c6488 _d6488