a machine learning approach for classifying and
play

A Machine-learning Approach for Classifying and Categorizing - PowerPoint PPT Presentation

Apr 03, 2023 •347 likes •534 views

A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks Siegfried Rasthofer, Steven Arzt, Eric Bodden

  1. A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks Siegfried Rasthofer, Steven Arzt, Eric Bodden    

  2.  2   

  3. sources report potential privacy leaks code analysis code analysis sinks Dynamic Approaches: TaintDroid [OSDI’10], Aurasium [USENIX’12], “Dr. Android and Mr. Hide“[SPSM’12], etc. Static Approaches: ScanDroid [TR 09], DeD [SEC’11], CHEX [CCS’12], LeakMiner [WCSE’12], ScanDal [Most’12], AndroidLeaks [TRUST’12], SAAF [SAC’13], FlowDroid [PLDI’14], etc.  3   

  4. ...but wait  4   

  5. sources report potential privacy leaks code analysis sinks  5   

  6. Method TaintDroid SCanDroid DeD ? Location.getLongitude() Location.getLatitude() Browser.getAllBookmarks() SmsManager.sendTextMessage Log.d() URL.openConnection()  6   

  7. Extracting Sources/Sinks Cat. 1 ... Cat. 2 List of Android API Sources ... Cat. n GoogleGlass API Oracle SuSi Cat. 1 Cat. 2 List of Chromecast API Sinks ... Cat. n ...  7   

  8. Machine-Learning Approach  8   

  9. Feature-Database: Classification specific return-type „ getter “ returns a value modifier dataflow to return  9   

  10. Feature-Database: Classification Feature-Categories: ‣ Method name ‣ Method has parameters ‣ Method’s return type ‣ Parameter type ‣ Method modifiers ‣ Modifiers of declaring class ‣ Name of declaring class ‣ Dataflow to return value ‣ Dataflow from parameter to (abstract) sink  10   

  11. Feature-Database: Categorization ... SMS/MMS Location Calendar Contact ... SMS/MMS NFC Internet Bluetooth Email  11   

  12. Evaluation Ten-fold cross validation: training better T P Recall = T P + F N T P Precision = T P + F P  12   

  13. Evaluation Chromecast GoogleGlass Manual validation: ‣ Google Glass API: Precision: 98% and Recall: 100% ‣ Google Chromecast API: Precision and Recall: 100%  13   

  14. Evaluation  14   

  15. Top Source/Sink Methods in Android-Malware Method TaintDroid SCanDroid DeD BluetoothAdapter.getAddress() WifiInfo.getMacAddress() Locale.getCountry() WifiInfo.getSSID() GsmCellLocation.getCid() GsmCellLocation.getLac() Location.getLongitude() Location.getLatitude() Browser.getAllBookmarks() SmsManager.sendTextMessage Log.d() URL.openConnection()  15   

  16. Newly discovered sources by SuSi Previously known sources Android 4.2 API SuSi’s categorized sources 2 % SuSi’s categorized sinks 2 % 5 % 98 % Newly discovered sinks by SuSi Previously known sinks 92 % 1 % 99 %  16   

  17. Open-Source on GitHub: https://github.com/secure-software-engineering/SuSi Siegfried Rasthofer Secure Software Engineering Group (EC-SPRIDE) Email: siegfried.rasthofer@cased.de Blog: http://sse-blog.ec-spride.de  17   

Recommend

Classifying unstructured text Deterministic and machine learning approaches Stephanie Fischer

Classifying unstructured text Deterministic and machine learning approaches Stephanie Fischer

Using a simple tool to solve a complex problem does not result in a simple solution. Larry Wall Classifying unstructured text Deterministic and machine learning approaches Stephanie Fischer Hamburg Mnchen Berlin Kln Leipzig

506 views • 39 slides
Language and Computers Unsupervised Learning Features & Classifying Documents Evidence

Language and Computers Unsupervised Learning Features & Classifying Documents Evidence

Language and Computers Classifying Documents Introduction Language Identification Machine Learning Supervised Learning Language and Computers Unsupervised Learning Features & Classifying Documents Evidence Measuring sucess

800 views • 45 slides
Classifying Adjectives for Attribute Learning: an Empirical Investigation Matthias Hartung

Classifying Adjectives for Attribute Learning: an Empirical Investigation Matthias Hartung

Background & Motivation Annotation Experiment Outlook: Alternative Approach Conclusions Classifying Adjectives for Attribute Learning: an Empirical Investigation Matthias Hartung Anette Frank Computational Linguistics Department

725 views • 38 slides
A machine learning approach against a masked AES L. LERMAN , S. FERNANDES MEDEIROS, G. BONTEMPI,

A machine learning approach against a masked AES L. LERMAN , S. FERNANDES MEDEIROS, G. BONTEMPI,

A machine learning approach against a masked AES A machine learning approach against a masked AES L. LERMAN , S. FERNANDES MEDEIROS, G. BONTEMPI, and O. MARKOWITCH Universit Libre de Bruxelles Faculty of Sciences Department of Computer

349 views • 20 slides
Machine Learning - MT 2017 7 Bayesian Approach to Machine Learning Christoph Haase University of

Machine Learning - MT 2017 7 Bayesian Approach to Machine Learning Christoph Haase University of

Machine Learning - MT 2017 7 Bayesian Approach to Machine Learning Christoph Haase University of Oxford October 23, 2017 Frequentist vs Bayesian Approaches Different views on probability: Frequentists: Probability of an event represents

469 views • 32 slides
Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior

Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior

Bayesian Learning l A powerful approach in machine learning l Combine data seen so far with prior beliefs This is what has allowed us to do machine learning, have good inductive biases, overcome "No free lunch", and obtain good

1.42k views • 29 slides
Learning to de-anonymize social networks A machine learning approach to social graph

Learning to de-anonymize social networks A machine learning approach to social graph

Learning to de-anonymize social networks A machine learning approach to social graph de-anonymization Kumar Sharad October 28, 2016 Royal Holloway, University of London ACM Workshop on Artificial Intelligence and Security, Vienna, Austria

1.01k views • 69 slides
CLASSIFYING LABORATORY TEST RESULTS USING MACHINE LEARNING Joy (Sizhe) Chen , Kenny Chiu , William

CLASSIFYING LABORATORY TEST RESULTS USING MACHINE LEARNING Joy (Sizhe) Chen , Kenny Chiu , William

CLASSIFYING LABORATORY TEST RESULTS USING MACHINE LEARNING Joy (Sizhe) Chen , Kenny Chiu , William Lu , Nilgoon Zarei A U GU ST 31, 2018 TEAM Joy (Sizhe) Chen Kenny Chiu Nelly (Nilgoon) Zarei William Lu 2 AGENDA Background

761 views • 40 slides
The Probabilistic Approach to Learning from Data Prob. Readings: Matt Gormley

The Probabilistic Approach to Learning from Data Prob. Readings: Matt Gormley

10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University The Probabilistic Approach to Learning from Data Prob. Readings: Matt

736 views • 58 slides
V0D 2016 Classifying Studies V0D V0D 2016 Classifying Studies 1 2016 Classifying Studies

V0D 2016 Classifying Studies V0D V0D 2016 Classifying Studies 1 2016 Classifying Studies

V0D 2016 Classifying Studies V0D V0D 2016 Classifying Studies 1 2016 Classifying Studies 2 Classifying Studies: Influences on Statistics Features and Benefits Typically, statistics are used as evidence for MILO SCHIELD, causal

651 views • 26 slides
An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning

An Exercise in An Exercise in Machine Learning Machine Learning http://www.cs.iastate.edu/~cs573x/bbsilab.html Machine Learning Software Preparing Data Building Classifiers Interpreting Results Machine Learning Software

496 views • 26 slides
Introduction to Machine Learning Introduction Prof. Andreas Krause Institute for Machine

Introduction to Machine Learning Introduction Prof. Andreas Krause Institute for Machine

Introduction to Machine Learning Introduction Prof. Andreas Krause Institute for Machine Learning (las.ethz.ch) What is Machine Learning I: An example Classify email messages as Spam or Non Spam Classical Approach : manual rules

1.27k views • 59 slides
Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by

Machine Learning By Alex Scarlatos What is Machine Learning? Machine Learning is the process by which computers can be trained through observation, rather than being explicitly programmed. Basically, a program is given input and adjusts its

556 views • 17 slides
Decision Trees TJ Machine Learning Club Classification vs. Regression Classification

Decision Trees TJ Machine Learning Club Classification vs. Regression Classification

Decision Trees TJ Machine Learning Club Classification vs. Regression Classification Classifying photos of fruits Determining whether tumor is benign or malignant Regression Predicting COVID-19 cases given demographic data

505 views • 26 slides
Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning: Study of algorithms that improve their performance P at some task T

Machine Learning 10-601 Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 12, 2015 Today: Readings: What is machine learning? The Discipline of ML Decision tree learning Mitchell, Chapter 3

872 views • 29 slides
A Machine Learning Approach A Machine Learning Approach A Machine Learning Approach A Machine

A Machine Learning Approach A Machine Learning Approach A Machine Learning Approach A Machine

A Machine Learning Approach A Machine Learning Approach A Machine Learning Approach A Machine Learning Approach to Preventing to Preventing to Preventing to Preventing Avoidable ED Utilization Avoidable ED Utilization Avoidable ED

727 views • 13 slides
Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning

GCT634/AI613: Musical Applications of Machine Learning (Fall 2020) Traditional Machine Learning: Unsupervised Learning Juhan Nam Traditional Machine Learning Pipeline in Classification Tasks A set of hand-designed audio features are selected

398 views • 26 slides
Machine Learning for Sequence Learning Learning in an All-Subsequence Space Severin Gsponer,

Machine Learning for Sequence Learning Learning in an All-Subsequence Space Severin Gsponer,

Machine Learning for Sequence Learning Learning in an All-Subsequence Space Severin Gsponer, Georgiana Ifrim, Barry Smyth January 20, 2016 Outline Background Linear Classifiers for Sequences SEQL Approach Contribution Future

298 views • 18 slides
Prediction without probability: a PDE approach to a model problem from machine learning Robert V.

Prediction without probability: a PDE approach to a model problem from machine learning Robert V.

Prediction without probability: a PDE approach to a model problem from machine learning Robert V. Kohn Courant Institute, NYU Joint work with Kangping Zhu (PhD 2014) and Nadejda Drenska (in progress) Mathematics for Nonlinear Phenomena:

924 views • 44 slides
CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine

1/22/19 CS 335 Machine Learning What is Machine Learning? Dan Sheldon Spring 2019 What is Machine Learning? A Simple Task: Recognize Obama How do you program a computer to Recognize faces? Recommend movies? Decide which web

581 views • 5 slides
The Boosting Approach to Machine Learning Maria-Florina Balcan 03/16/2015 Boosting General

The Boosting Approach to Machine Learning Maria-Florina Balcan 03/16/2015 Boosting General

The Boosting Approach to Machine Learning Maria-Florina Balcan 03/16/2015 Boosting General method for improving the accuracy of any given learning algorithm. Works by creating a series of challenge datasets s.t. even modest

715 views • 32 slides
Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their

Machine Learning Machine Learning: algorithms that use experience to improve their performance We use machine learning in situations where it is very challenging (or impossible) to define the rules by hand: e.g. face detection

1.04k views • 28 slides
How Effective was the UK Carbon Tax? A Machine Learning Approach to Policy Evaluation Jan Abrell,

How Effective was the UK Carbon Tax? A Machine Learning Approach to Policy Evaluation Jan Abrell,

How Effective was the UK Carbon Tax? A Machine Learning Approach to Policy Evaluation Jan Abrell, Mirjam Kosch, Sebastian Rausch IAEE, 25.8.2019 Two main questions 1. What was the impact of the UK carbon price support on emissions? 2. How can

523 views • 34 slides
Malware Analysis Machine Learning Approach Chiheb Chebbi TEK-UP University DeepSec 14-17 Nov

Malware Analysis Machine Learning Approach Chiheb Chebbi TEK-UP University DeepSec 14-17 Nov

Malware Analysis Machine Learning Approach Chiheb Chebbi TEK-UP University DeepSec 14-17 Nov 2017 Vienna, Austria <whoami/> Computer science engineering student @TEK-UP Cyber security leadership program fellow @Kaspersky_Lab

626 views • 58 slides

More recommend