A machine learning approach for detection of android malware based on hybrid analysis (Record no. 6095)

MARC details
000 -LEADER
fixed length control field 01464nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122840.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180530b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Ramakrishnan T (93616029)
9 (RLIN) 14226
245 ## - TITLE STATEMENT
Title A machine learning approach for detection of android malware based on hybrid analysis
300 ## - PHYSICAL DESCRIPTION
Extent MSC CS 2016-2018
500 ## - GENERAL NOTE
General note <br/>Android is an open source Smartphone OS developed by Google. Android is highly targeted<br/>by malware apps in recent years. We suggest a hybrid approach which combines the static<br/>and dynamic methods for malware detection in Android. In the static analysis, we extracted<br/>the information from source code of android application by reverse engineering the<br/>application and in dynamic analysis we have traced out the system calls generated by the<br/>application by strace utility. This information is further used as the features of a machine<br/>learning classifier. We used machine learning classifiers such as Decision Tree, KNN,<br/>Logistic regression, Naive bayes, SVC and random forest. With Decision tree classifier we <br/>got 58.33% accuracy, whereas KNN, Logistic Regression and Support Vector Classifier have <br/>given an accuracy of 66.66%. Random forest and Naive bayes have given a top accuracy of<br/>75%. <br/><br/><br/><br/>
502 ## - DISSERTATION NOTE
Degree type MSC CS
Name of granting institution 2016-2018
Year degree granted INT
-- Dr. Tony Thomas
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ANDROID
9 (RLIN) 14227
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MALWARE DETECTION
9 (RLIN) 14228
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 14229
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
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Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date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   30/05/2018   R-1394 30/05/2018 30/05/2018 Project Reports