Ransomware detection using machine learning (Record no. 6501)

MARC details
000 -LEADER
fixed length control field 01546nam 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 Vidhya K V (93617036)
9 (RLIN) 15873
245 ## - TITLE STATEMENT
Title Ransomware detection using machine learning
300 ## - PHYSICAL DESCRIPTION
Extent MSC CS 2017-2019
500 ## - GENERAL NOTE
General note Ransomware is a kind of malware that installs covertly on a victim's computer<br/>or smartphone, executes a cryptovirology attack and demands a ransom<br/>payment to restore it. Ransomwares have been the most serious threat<br/>in 2016, and this situation continues to worsen. Because of high reward for<br/>Ransomwares, more and more Ransomware families appear, and it makes<br/>us more dicult to detect them. There are dierent signatures or behaviors<br/>among dierent families (i.e. Locky, Cerber, Cryptowall to name a few)<br/>or versions (i.e. CryptXXX2.0, CryptXXX3.0) of Ransomwares. It will be<br/>wonderful if there is a way that can detect potential Ransomware threats.<br/>This paper is based on machine learning technique to detect Ransomwares.<br/>The rst part introduces how to label the data with dierent behaviors and<br/>what features that have chosen. Afterward, present the model for detecting<br/>various Ransomware and prevent them from encrypting victim's data.<br/>Experimental evaluation demonstrates that this model can detect the latest<br/>Ransomware.
502 ## - DISSERTATION NOTE
Degree type MSC CS
Name of granting institution 2017-2019
Year degree granted INT
-- Md. Meraj Uddin
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element RANSOMWARE
9 (RLIN) 15874
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MALWARE DETECTION
9 (RLIN) 15875
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 15876
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date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-1505 18/06/2019 18/06/2019 Project Reports