ANDROID MALWARE https://www.cnet.com/android-update/ Rafael Estrada - PowerPoint PPT Presentation

Oct 10, 2022 •230 likes •347 views

DATA ANALYSIS OF ANDROID MALWARE https://www.cnet.com/android-update/ Rafael Estrada Department of Mathematics New Mexico Tech Mentor: Dr. Golden G. Richard III Postdoctoral Researcher: Aisha Ali-Gombe July 26 th 2017 CCT REU 2017 ANDROID

DATA ANALYSIS OF ANDROID MALWARE https://www.cnet.com/android-update/ Rafael Estrada Department of Mathematics New Mexico Tech Mentor: Dr. Golden G. Richard III Postdoctoral Researcher: Aisha Ali-Gombe July 26 th 2017 CCT REU 2017
ANDROID MALWARE ➢ What is it? • “Malicious software”, that attacks cellular devices, more specifically the Android OS. ➢ What can this mobile malware do? • Capable of sending SMS/MMS messages, memory deletion (SD card), contacts possession, and privacy leakage. ➢ Infection mechanism? • Malware in Play store. • Repackaged apps in alternate app market.
METHODS  Static Analysis • Aims to find weaknesses in code that will cause problems • Runs before actual code execution (debugging) • Displays where code may have errors/flaws (unused variables, dead code, infinite loops)  Tools • FlowDroid & Androguard • Examine apk files (Android Package Kit).
ANDROGUARD  Mines data such as activities, permission rmissions, and methods.  Powerful tool for: • Reverse engineering • Decompiling apk files • Reading Android xml files within the apk http://blog.k3170makan.com/2014/11/automated-dex-decompilation-using.html
PERMISSIONS  Listed in the Manifest file • Benign or dangerous  Android 6.0 (API level 23 and on) • User grants permissions at runtime • Increased user control (i.e. permission removal)  Android 5.1 (API level 22 and before) • User grants permission at install time
EXAMPLES Figure 1: Android 5.1 Figure 2: Android Figure 3: Android 6.0.1 Permission at install 6.0.1 Settings Screen App asking for time permission
ANDROGUARD (CONTINUED) • Normal Permissions are Of the 17,801 permiss ssion ions s analyzed: yzed: automatically granted 6,710 – Normal Permissions (38%) 8,072 – Dangerous Permissions (45%) Dangerous Permissions need • 3,019 – Other (17%) user approval Dangerous Permissions https://inthecheesefactory.com/uploads/source/blog/mpermission/per mgroup.png
ANDROGUARD (CONTINUED) v
OVERALL  Permissions display what Android applications are able to access • Benign or threatening? • Ambiguity exists as to what the application will undertake  Other features for other tools • Sinks & Sources • Methods • Data Flow Analysis
QUESTIONS?

Recommend

Android Malware Analysis on Attacks and Defense Android malware Android malware With the

Android Malware Analysis on Attacks and Defense Android malware Android malware With the explosive growth of mobile device market and usage, there is an increasing number of malicious mobile applications targeting these devices and

819 views • 44 slides

Android Malware Adventures Mert Can Cokuner Krat Ouzhan Aknc Android Malware

DeepSec IDSC Android Malware Adventures Mert Can Cokuner Krat Ouzhan Aknc Android Malware Adventures Agenda INTRODUCTION ANDROID MALWARE COMMAND & CONTROL QUESTIONS & ANSWERS 2 1 2 3 4 Android Malware

1.01k views • 64 slides

StormDroid: A streaminglized Machine Learning-Based System for Detecting Android Malware Sen

StormDroid: A streaminglized Machine Learning-Based System for Detecting Android Malware Sen Chen, Minhui Xue, Zhushou Tang, Lihua Xu, Haojin Zhu Malware Detection in Android 1.6 million apps in Google Play Store in July 2015

441 views • 22 slides

Dissecting Android Malware: Characterization and Evolution 1 Problems to solve 18 Requirement

Dissecting Android Malware: Characterization and Evolution 1 Problems to solve 18 Requirement 1: Sufficient Malware data set Anti Virus Communities or Researchers are hampered by the lack of malware data set. Req equires a a suf

648 views • 34 slides

Apposcopy: Semantics- Based Detection of Android Malware through Static Analysis By Feng et al

Apposcopy: Semantics- Based Detection of Android Malware through Static Analysis By Feng et al [FSE 14] Presented by Maaz Ahmad The Malware Problem (Feb, 2015) Motive Security Labs estimates 16 million infected mobile devices. [1]

828 views • 30 slides

Android malware that wont make you fall asleep ukasz Siewierski lukasz.siewierski@cert.pl

Android malware that wont make you fall asleep ukasz Siewierski lukasz.siewierski@cert.pl @maldr0id Hackito Ergo Sum 2015 Android malware is boring! ukasz Siewierski (@maldr0id) Android malware that wont make you fall asleep 2 / 24

535 views • 34 slides

MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models Enrico

MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models Enrico Mariconti , Lucky Onwuzurike, Panagiotis Andriotis, Emiliano De Cristofaro, Gordon Ross, Gianluca Stringhini. NDSS 2017, 28-02-2017 Motivation: Android

1.21k views • 37 slides

GroDDViewer: Dynamic dual view of Android malware Jean-Franois Lalande Mathieu Simon Valrie

Introduction Malware analysis Visualization Conclusion GroDDViewer: Dynamic dual view of Android malware Jean-Franois Lalande Mathieu Simon Valrie Viet Triem Tong GraMSec 2020 CIDRE team June 22th 2020 Introduction Malware analysis

530 views • 28 slides

Andrubis 1,000,000 Apps Later A View on Current Android Malware Behaviors Martina

Andrubis 1,000,000 Apps Later A View on Current Android Malware Behaviors Martina Lindorfer, Matthias Neugschwandtner, Lukas Weichselbaum, Yanick Fratantonio, Victor van der Veen, Christian Platzer Vienna University of

425 views • 31 slides

SigPID: Significant Permission Identification for Android Malware Detection Authors: Lichao Sun,

SigPID: Significant Permission Identification for Android Malware Detection Authors: Lichao Sun, Zhiqiang Li, Qiben Yan, Witawas Srisa-an and Yu Pan Department of Computer Science and Engineering University of Nebraska Lincoln Presenters: Yu

999 views • 24 slides

Obfuscated Financial Fraud Android Malware : Detection and Behavior Tracking In Seung, Yang

Obfuscated Financial Fraud Android Malware : Detection and Behavior Tracking In Seung, Yang (KrCERT/CC, KISA) DeepSEC IDSC 2016 Friday, 11 November 2016 Analysis Team at KrCERT/CC, KISA Mobile malware analyst In Seung, Yang Who am I

1.59k views • 72 slides

AppSpear: Bytecode Decryp0ng and DEX Reassembling for Packed Android Malware Yang Wenbo, Zhang

AppSpear: Bytecode Decryp0ng and DEX Reassembling for Packed Android Malware Yang Wenbo, Zhang Yuanyuan, Li Juanru, Shu Junliang, Li Bodong, Hu Wenjun, Gu Dawu Sudeep Nanjappa Jayakumar Agenda Introduc0on AppSpear Goals,

322 views • 19 slides

DroidScribe Classifying Android Malware Based on Runtime Behavior Santanu Kumar Dash, Guillermo

DroidScribe Classifying Android Malware Based on Runtime Behavior Santanu Kumar Dash, Guillermo Suarez-Tangil , Salahuddin Khan, Kimberly Tam, Mansour Ahmadi, Johannes Kinder, and Lorenzo Cavallaro Royal Holloway, University of London

352 views • 33 slides

AVPASS: Automatically Bypassing Android Malware Detection System Jinho Jung, Chanil Jeon, Max

AVPASS: Automatically Bypassing Android Malware Detection System Jinho Jung, Chanil Jeon, Max Wolotsky, Insu Yun, and Taesoo Kim Georgia Institute of Technology, July 27, 2017 Ab About t Us SSLab (@GT) Focusing on s ystem and security

1.25k views • 72 slides

Kharon dataset: Android malware under a microscope Nicolas Kiss Jean-Franois Lalande Mourad

Kharon dataset: Android malware under a microscope Nicolas Kiss Jean-Franois Lalande Mourad Leslous Valrie Viet Triem Tong The LASER Workshop 2016 Learning from Authoritative Security Experiment Results May 26th 2016 N. Kiss & J.-F.

367 views • 18 slides

DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android

DroidScope: Seamlessly Reconstructing the OS and Dalvik Semantic Views for Dynamic Android Malware Analysis Lok Yan Heng Yin August 10, 2012 1 Android Java Components System Services Native Components Apps 2 Android Java Components

631 views • 33 slides

Analyzing Sophisticated Android Malware with CodeInspect Siegfried Rasthofer SECURE SOFTWARE

Analyzing Sophisticated Android Malware with CodeInspect Siegfried Rasthofer SECURE SOFTWARE ENGINEERING GROUP #whoami 3rd year PhD-Student at Secure Software Engineering Group Darmstadt, Germany (Prof. Dr. Eric Bodden)

603 views • 19 slides

Kharon dataset: Android malware under a microscope N. Kiss J.-F. Lalande EPI CIDRE INSA Centre

Kharon dataset: Android malware under a microscope N. Kiss J.-F. Lalande EPI CIDRE INSA Centre Val de Loire CentraleSupelec, Inria, Univ. Rennes 1, CNRS, Univ. Orlans, LIFO EA 4022, F-35065 Rennes, France F-18020 Bourges, France M.

459 views • 12 slides

Malware Collusions Karim O. Elish, Danfeng (Daphne) Yao, and Barbara G. Ryder Department of

On the Need of Precise Inter-App ICC Classification for Detecting Android Malware Collusions Karim O. Elish, Danfeng (Daphne) Yao, and Barbara G. Ryder Department of Computer Science Virginia Tech May 21, 2015 Problem and Motivation Malware

139 views • 13 slides

Ubiquitous and Mobile Computing CS 528: A Survey of Mobile Malware in the Wild Alex Fortier

Ubiquitous and Mobile Computing CS 528: A Survey of Mobile Malware in the Wild Alex Fortier Computer Science Dept. Worcester Polytechnic Institute (WPI) What is mobile malware? Targeted at Android, iOS, Symbian (discontinued), Windows Phone

507 views • 17 slides

How Current Android Malware Seeks to Evade Automated Code Analysis Siegfried Rasthofer, Irfan

How Current Android Malware Seeks to Evade Automated Code Analysis Siegfried Rasthofer, Irfan Asrar , Stephan Huber, Eric Bodden Technische Universitt Darmstadt, Darmstadt, Germany Fraunhofer SIT, Darmstadt, Germany Appthority, San

344 views • 22 slides

BLOWING THE COVER: HANDS-ON ANALYSIS OF HANDCRAFTED ANDROID MALWARE Alex Reshetniak | September

BLOWING THE COVER: HANDS-ON ANALYSIS OF HANDCRAFTED ANDROID MALWARE Alex Reshetniak | September 26 2018 About Me Senior Security Researcher @ Lookout B.S. in Information Security More than 5 years working in Information Security

663 views • 31 slides

Enhancing Mobile Malware: an Android RAT Case Study BSIDES VIENNA 2014 November 22 About Marco

Enhancing Mobile Malware: an Android RAT Case Study BSIDES VIENNA 2014 November 22 About Marco Lancini Security Consultant, CEFRIEL @lancinimarco Roberto Puricelli Security Consultant, CEFRIEL @robywankenoby 2 Introduction Intro

870 views • 35 slides

Experiences of of La Landing Machine Le Learning onto Market-Scale Mobile Malware Detection

Experiences of of La Landing Machine Le Learning onto Market-Scale Mobile Malware Detection Liangyi Gong, Zhenhua Li, Feng Qian, Zifan Zhang, Qi Alfred Chen, Zhiyun Qian, Hao Lin, Yunhao Liu Mobile Malware Detection Android App Markets

424 views • 29 slides