A Framework for Profiling and Detecting Title Android ...
[Pages:1]CIC
A Framework for ProTiftilleing and Detecting Android FinAaunthcoiarsl Malware
CanadiaAnnIdnisFtiitturitaehfoAr. CKyabdeirrs, eNcautraitliya(SCtICak),hUanniovvears,iAtylioAf .nGehworBbraunniswick (UNB)
Canadian Institute for Cybersecurity (CIC), University of new Brunswick (UNB)
OVERVIEW
Problem: Android financial malware (AFM) exist because information about users and their activities has value and naturally money is a motivating factor. However, what constitutes AFM is still ambiguous
Significance: A comprehensive understanding of the existing AFM attacks supported by a unified terminology is necessarily required for the deployment of reliable defence mechanisms against these attacks
Goal: We focus on three aspects of AFM: analyzing the characteristics, profiling the behavior, and detecting the malware .
WHAT IS ANDROID FINANCIAL MALWARE?
WHAT IS OUR SOLUTION?
Current Issues
Table: Example of Banking Malware Zitmo detected by AVs
Proposed Framework
This nonstandardization leads to confusion and inaccuracy.
Definition
A specialized malware which is designed to: direct financial profit or money exchange to the fraudsters financial transaction includes any reselling or direct transactions without the user's knowledge or consent
Data
32 families 1758 unique samples (2010-2015) 5 categories: banking malware, ransomware, scareware, SMS malware, and adware
None of the labels indicate
banking nature
WHAT DOES ANALYSIS TELL US?
Taxonomy Classification Comparison
Unique Phone number used by SMS Fraud
HOW TO DETECT ANDROID FINANCIAL MALWARE?
Industry Solution
Company AVG Mobile
Apps Name
AntiVirus FREE Security Scan
Bitdefender
Mobile Security & Antivirus
Lookout Mobile Security & Antivirus Lookout
Norton Mobile Norton Security and Antivirus
Webroot Inc
Security - Free
Academic Research
Financial Charge Example SMS premium-rate number relationships
By devising the AFM taxonomy:
one can gain a deep understanding of the complex characteristics and the unknown behavior of AFM.
It can help in detecting the future malware threats.
SUMMARY FUTURE WORK
In the future, we plan to: develop the framework of AFM detection based on profiles. build a prototype for evaluating the AFM detection system.
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