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Text Analytics for Mobile App Security and Beyond. Tao Xie University of Illinois at Urbana-Champaign. [email protected]. Mobile App Markets. Google Play. Microsoft Windows Phone. Apple App Store. App Store beyond Mobile Apps!. What If Formal Specs Are Written?!. - PowerPoint PPT Presentation
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Text Analytics for Mobile App Security and Beyond
Tao XieUniversity of Illinois at Urbana-Champaign
Mobile App Markets
Apple App Store Google Play Microsoft Windows Phone
App Store beyond Mobile Apps!
What If Formal Specs Are Written?!
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APP DEVELOPERS
APP USERS
App Functional Requirements
App Security Requirements
User Functional Requirements
User Security Requirements
informal: app description, etc. permission list, etc.
Informal App Functional Requirements: App Description
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App Code
App Permissions
App Security Requirements: Permission List
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What If Formal Specs Are Written?!
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APP DEVELOPERS
APP USERS
App Functional Requirements
App Security Requirements
User Functional Requirements
User Security Requirements
informal: app description, etc. permission list, etc.
Example Andriod App: Angry Birds
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What If Formal Specs Are Written?!
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APP DEVELOPERS
APP USERS
App Functional Requirements
App Security Requirements
User Functional Requirements
User Security Requirements
In reality, few of these requirements are (formally) specified!! Hope?!: Bring human into the loop: user perception + judgment
informal: app description, etc. permission list, etc.
Our Yin-Yang View on Mobile App Security
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App Description
App Code
App Permissions
User-Perceived Information
App Security Behavior
o Reason about user-perceived info, e.g., WHYPER ( )
o Push app security behavior across the boundary ()
o Check consistency across the boundary ()
o Reduce user judgment effort ( )
App UIs, App categories, App metadata, User forums, …
[functional]
[security]
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oApple (Market’s Responsibility)o Apple performs manual inspection
oGoogle (User’s Responsibility)o Users approve permissions for security/privacyo Bouncer (static/dynamic malware analysis)
oWindows Phone (Hybrid)o Permissions / manual inspection
Assuring Market Security/Privacy
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o Previous approaches look at permissions code (runtime behaviors)
o What does the users expect?o GPS Tracker: record and send locationo Phone-Call Recorder: record audio during phone call
Need More Than Program Analysis
App Description
App Code
App Permissions
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oUser expectationso user perception + user judgment
o Focus on permission app descriptionso permissions (protecting user understandable
resources) should be discussed
Vision“Bridging the gap between
user expectation app behaviors”
App Description Sentence Permission
Linkage
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WHYPER Overview
Application Market
WHYPER
DEVELOPERS
USERSPandita et al. WHYPER: Towards Automating Risk Assessment of Mobile Applications. USENIX Security 2013http://web.engr.illinois.edu/~taoxie/publications/usenixsec13-whyper.pdf
• Enhance user experience while installing apps• Enforce functionality disclosure on developers• Complement program analysis to ensure justifications
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Example Sentence in App Desc.• E.g., “Also you can share the yoga
exercise to your friends via Email and SMS.” – Implication of using the contact
permission– Permission sentences
Keyword-based search on application descriptions
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Problems with Ctrl + F
• Confounding effects:– Certain keywords such as “contact” have a
confounding meaning – E.g., “... displays user contacts, ...” vs “... contact
me at [email protected]”.
• Semantic inference: – Sentences often describe a sensitive operation
without actually referring to keywords – E.g., “share yoga exercises with your friends via
Email and SMS”
Natural Language Processing
• Natural Language Processing (NLP) techniques help computers understand NL artifacts
• In general, NLP is still difficult
• NLP on domain specific sentences with specific styles is feasible– Text2Policy: extraction of security policies from use cases [FSE 12]– APIInfer: inferring contracts from API docs [ICSE 12]– WHYPER: domain knowledge from API docs [USENIX Security 13]
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Overview of WHYPER
APP Description
APP Permission
SemanticGraphs
PreprocessorIntermediate
RepresentationGenerator
SemanticEngine
NLP Parser
Semantic GraphGeneratorAPI Docs
AnnotatedDescription
FOLRepresentation
WHYPER
Domain Knowledge
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Preprocessor• Period Handling
– Decimals, ellipsis, shorthand notations (Mr., Dr.)
• Sentence Boundaries– Tabs, bullet points, delimiters (:)– Symbols (*,-) and enumeration sentence
• Named Entity Handling– E.g., “Pandora internet radio”
• Abbreviation Handling– E.g., “Instant Message (IM)”
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Intermediate-Representation Generator
Alsoyoucanshare yogaexercisetoyourfriendsviaEmailandSMSVBRB PRP MD NNDT NN NNSPRP NNP NNP
the
Alsoyoucan
share
exercise
yourfriendsEmail
SMS
yoga
advmodnsubjauxdobj
detnn
prep_topossprep_via
conj_and
the
shareto
youyoga exercise
ownedyouvia
friendsand
EmailSMS
Predicate
Governing
Entity
DependentEntit
y
Semantic Engine
shareto
youyoga exercise
ownedyouvia
friendsand
EmailSMSEmail
share
WordNet Similarity
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Inferred from API
DocsGoverning
Entity
DependentEntit
y
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Systematic approach to infer graphso Identify resource associated with the permissions
from the API class nameo ContactsContract.Contacts
o Inspect the member variables and member methods to identify actions and subordinate resourceso ContactsContract.CommonDataKinds.Email
Semantic-Graph Generator
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Evaluation• Subjects
– Permissions: • READ_CONTACTS • READ_CALENDAR • RECORD_AUDIO
– 581 application descriptions – 9,953 sentences
• Evaluation setup– Manual annotation of the sentences– WHYPER for identifying permission sentences– Comparison to keyword-based searching
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Evaluation Results
• Precision and recall of WHYPER – Average precision (82.8%) and recall (81.5%)
• Comparison to keyword-based searching – Improving precision (41.6%) and recall (-1.2%)– E.g., microphone-blow into and call-record
Permission KeywordsREAD_CONTACTS contact, data, number,
name, emailREAD_CALENDAR calendar, event, date,
month, day, yearRECORD_AUDIO record, audio, voice,
capture, microphone
Access Control Policies (ACP) in Requirements Document
• Access control is often governed by security policies called Access Control Policies (ACP)– Includes rules to control which principals have access to
which resources
• A policy rule includes four elements– Subject – HCP – Action – edit– Resource - patient's account– Effect - deny
“The Health Care Personnel (HCP) does not have the ability to edit the patient's account.”
ex.
Overview of Text2Policy
A HCP should not change patient’s account.
An [subject: HCP] should not [action: change] [resource: patient’s account].
ACP Rule
EffectSubject Action Resource
HCP UPDATE - change
patient’s account
deny
Linguistic Analysis
Model-Instance Construction
TransformationXiao et al. Automated Extraction of Security Policies from Natural-Language Software Documents. FSE 2012. http://web.engr.illinois.edu/~taoxie/publications/fse12-nlp.pdf
Example Technical Challenges in ACP Extraction
• Semantic Structure Variance– different ways to specify the same rule
• Negative Meaning Implicitness– verb could have negative meaning
ACP 1: An HCP cannot change patient’s account.ACP2: An HCP is disallowed to change patient’s account.
Road Ahead: Yin-Yang View
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App Description
App Code
App Permissions
User-Perceived Information
App Security Behavior
o Reason about user-perceived info, e.g., WHYPER ( )
o Push app security behavior across the boundary ()
o Check consistency across the boundary ()
o Reduce user judgment effort ( )
App UIs, App categories, App metadata, User forums, …
[functional]
[security]
Text Analytics for Mobile App Security and Beyond
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App Description
App Code
App Permissions
App UIs, App categories, App metadata, User forums, …
Acknowledgments: Supported in part by NSA Science of Security (SoS) Lablet, NSF SaTC, NSF SHF, NSF CAREER
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Problems with Ctrl + F
o Confounding effects:
o Certain keywords such as “contact” have a confounding meaning. o For instance, “... displays user contacts, ...” vs “... contact me at [email protected]”.
o Semantic Inference:
o Sentences often describe a sensitive operation such as reading contacts without actually referring to keyword “contact”.
o For instance, “share yoga exercises with your friends via email, sms”.
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• NLP techniques help computers understand NL artifacts
• NLP is still difficult
• NLP on domain specific sentences with specific styles is feasible
Natural Language Processing (NLP)
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RQ1 Results: Effectiveness of WHYPER
• Low FPs and FNs• out of 9,061 sentences, only 129 are flagged as FPs• among 581 applications, 109 applications (18.8%) contain at least one FP• among 581 applications, 86 applications (14.8%) contain at least one FN
Permission SI TP FP FN TN Prec. Recall F-Score Acc
READ_CONTACTS 204 186 18 49 2,930 91.2 79.2 84.8 97.9
READ_CALENDAR 288 241 47 42 2,422 83.7 85.2 84.5 96.8
RECORD_AUDIO 259 195 64 50 3,470 75.3 79.6 77.4 97.0
TOTAL 751 622 129 141 9,061 82.8 81.5 82.2 97.3
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• Incorrect parsing• “MyLink Advanced provides full
synchronization of all Microsoft Outlook emails (inbox, sent, outbox and drafts), contacts, calendar, tasks and notes with all Android phones via USB”
• Synonym analysis• “You can now turn recordings into
ringtones.”
Result Analysis (False Positives)
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• Incorrect parsing• Incorrect identification of sentence boundaries and limitations of
underlying NLP infrastructure
• Limitations of Semantic Graphs• Manual Augmentation
• microphone-blow into and call-record• significant improvement of Delta Recalls: -6.6% to 0.6%
• Automatic mining from user comments and forums
Result Analysis (False Negatives)
Overview of Text2Policy
A HCP should not change patient’s account.
An [subject: HCP] should not [action: change] [resource: patient’s account].
ACP Rule
EffectSubject Action Resource
HCP UPDATE - change
patient’s account
deny
Linguistic Analysis
Model-Instance Construction
Transformation
Linguistic Analysis
• Incorporate syntactic and semantic analysis– syntactic structure -> noun group, verb group, etc.– semantic meaning -> subject, action, resource, negative
meaning, etc.
• Provide New techniques for model extraction– Identify ACP and AS sentences– Infer semantic meaning
Common Techniques
• Shallow parsing• Domain dictionary• Anaphora resolution
An HCP can view patient’s account.He is disallowed to change the patient’s account.
Subject Main Verb Group
Object
NP PNP
UPDATEHCP
VG
Technical Challenges (TC) in ACP Extraction
• TC1: Semantic Structure Variance– different ways to specify the same rule
• TC2: Negative Meaning Implicitness– verb could have negative meaning
ACP 1: An HCP cannot change patient’s account.ACP2: An HCP is disallowed to change patient’s account.
Semantic-Pattern Matching
• Address TC1 Semantic Structure Variance
• Compose pattern based on grammatical function
An HCP is disallowed to change the patient’s account.ex.
passive voice to-infinitive phrasefollowed by
Negative-Expression Identification
• Address TC2 Negative Meaning Implicitness
• Negative expression– “not” in subject:
– “not” in verb group:
• Negative meaning words in main verb group
No HCP can edit patient’s account.ex.
HCP can not edit patient’s account.HCP can never edit patient’s account.
ex.
ex. An HCP is disallowed to change the patient’s account.
AS: Syntactic-Pattern Matching
• Syntactic elements– Subject , Main verb, Object
• Subject and Object Checking– subject is a not a user or object is not a resource
• Filtering negative-meaning sentences– Negative sentences tend not to describe ASs
The prescription list should include medication, the name of the doctor. . .
ex.
Overview of Text2Policy
A HCP should not change patient’s account.
An [subject: HCP] should not [action: change] [resource: patient’s account].
ACP Rule
EffectSubject Action Resource
HCP UPDATE - change
patient’s account
deny
Linguistic Analysis
Model-Instance Construction
Transformation
ACP Model-Instance Construction
• Identify subject, action, and resource:– Subject: HCP– Action: change– Resource: patient’s account
• Infer effect:– Negative Expression: none– Negative Verb: disallow– Inferred Effect: deny
An HCP is disallowed to change the patient’s account.
ex.
ACP Rule
EffectSubject Action Resource
HCP UPDATE - change
patient’s account
deny
AS Model-Instance Construction
• Use case patterns– industry use cases [DSN’09]– public use cases
• Model-Instance ConstructionThe patient views access log.ex.
Action Step
Actor Action Resource
patient OUTPUT – view
access log
Technical Challenges in Action-Step Extraction
• TC4: Transitive Subject
• TC5: Perspective Variance
AS 1:He edits the account.AS 2: The system updates the account.AS 3: The system displays the updated account.
HCPHCP views the updated account.
Subject Flow Tracking
• Address TC4 Transitive Subject• Apply data flow to track non-system subject:
AS 1: The HCP edits the account.AS 2: The system updates the account.
Tracking Only system as subject
replaced with HCP as subject
Perspective Conversion
• Address TC5 Perspective Variance• Apply data flow to track non-system subject:
AS 1: The HCP edits the account.AS 2: The system shows the updated account.
Tracking Only system as subject andaction is output
Converting to “HCP views the updated account”
Evaluation – RQs
• RQ1: How effectively does Text2Policy identify ACP sentences in NL documents?
• RQ2: How effectively does Text2Policy extract ACP rules from ACP sentences?
• RQ3: How effectively does Text2Policy extract action steps from action-step sentences?
Evaluation – Subject
• iTrust open source project– http://agile.csc.ncsu.edu/iTrust/wiki/– 448 use-case sentences (37 use cases)– preprocessed use cases
• Collected ACP sentences– 100 ACP sentences – From 17 sources (published papers and websites)
• A module of an IBMApp (financial domain)– 25 use cases
RQ1 ACP Sentence Identification
• Apply Text2Policy to identify ACP sentences in iTrust use cases and IBMApp use cases
• Text2Policy effectively identifies ACP sentences with precision and recall more than 88%
• Precision on IBMApp use cases is better– proprietary use cases are often of higher quality compared to open-source
use cases
Evaluation –RQ2 Accuracy of Policy Extraction
• Apply Text2Policy to extract ACP rules from ACP sentences
• Text2Policy effectively extracts ACP model instances with accuracy above 86%
Evaluation –RQ3 Accuracy of Action-Step Extraction
• Apply Text2Policy to extract action steps from iTrust and IBMApp use cases
• Text2Policy effectively extracts AS model instances with accuracy above 81%
• Limitations: – Subordinate conjunction or else and long phrases
Detected Inconsistencies
• No violation between ASs against the extracted ACPs
• Inconsistent names used for referring to the same entity (e.g., user) across different use cases
editor used in UC 4 of iTrust use cases actually refers to HCP, admin, and all usersin UCs 1, 2, and 4
ex.
Summary
• Natural Language Processing (NLP) for domain-specific purposes is feasible– Challenging for general documents– Feasible for domain-specific sentences with specific
styles
• New techniques are required – Addressing unique challenges in software engineering
http://research.csc.ncsu.edu/ase/projects/text2policy/
