A static analysis based tool to detect vulnerabilities in android automotive applications (Record no. 6487)

MARC details
000 -LEADER
fixed length control field 02012nam a22002057a 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 Ladli Sultana (93617022)
9 (RLIN) 15817
245 ## - TITLE STATEMENT
Title A static analysis based tool to detect vulnerabilities in android automotive applications
300 ## - PHYSICAL DESCRIPTION
Extent MSC CS 2017-2019
500 ## - GENERAL NOTE
General note With millions of Android device activation per day and billions of application<br/>installations from Google Play, Android is becoming one of the most widely<br/>used operating systems, even in automotives. Based on the Linux kernel,<br/>Android operating system is open and <br/>exible enough to run on dierent devices<br/>having varied hardware conguration. Modern smart car infotainment<br/>systems allow users to connect an Android device to the vehicle. The device<br/>then interacts with the hardware of the car, hence providing new interaction<br/>mechanisms to the driver. However this can be misused and become a major<br/>security breach into the car with subsequent security concerns : the Android<br/>device can both read sensitive data (speed, model, airbag status) and send<br/>dangerous commands (brake, lock, airbag explosion). Moreover, this scenario<br/>is unsettling since Android devices connect to the cloud, opening the door<br/>to remote attacks by malicious users of the cyberspace. The applications are<br/>bringing potential vulnerability when they access private information from<br/>the resources of the devices. In this project, I have developed a static analysis<br/>based tool to detect vulnerabilities in Android Automotive Applications.<br/>It mainly focuses on the presence of malicious content in the application by<br/>analysing permissions, API calls, etc. The acquired data is then classied<br/>using machine learning techniques.
502 ## - DISSERTATION NOTE
Degree type MSC CS
Name of granting institution 2017-2019
Year degree granted INT
-- Dr Tony Thomas
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element ANDROID OPERATING SYSTEM
9 (RLIN) 15818
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element STATIC ANALYSIS
9 (RLIN) 15819
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element VULNERABILITY
9 (RLIN) 15820
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element MACHINE LEARNING
9 (RLIN) 15821
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-1491 18/06/2019 18/06/2019 Project Reports