Digital Forensics

1. Digital Forensics Dr. Bhavani Thuraisingham The University of Texas at Dallas Application Forensics November 5, 2008

2. Outline • Email Forensics • UTD work on Email worm detection - revisited • Mobile System Forensics • Note: Other Application/systems related forensics • Database forensics, Network forensics (already discussed) • Papers to discuss November 10, 2008 and November 17, 2008 • Reference: Chapters 12 and 13 of text book • Optional paper to read: • http://www.mindswap.org/papers/Trust.pdf

3. Email Forensics • Email Investigations • Client/Server roles • Email crimes and violations • Email servers • Email forensics tools

4. Email Investigations • Types of email investigations • Emails have worms and viruses – suspicious emails • Checking emails in a crime – homicide • Types of suspicious emails • Phishing emails i- they are in HTML format and redirect to suspicious web sites • Nigerian scam • Spoofing emails

5. Client/Server Roles • Client-Server architecture • Email servers runs the email server programs – example Microsoft Exchange Server • Email runs the client program – example Outlook • Identitication/authntictaion is used for client to access the server • Intranet/Internet email servers • Intranet – local environment • Internet – public: example: yahoo, hotmail etc.

6. Email Crimes and Violations • Goal is to determine who is behind the crime such as who sent the email • Steps to email forensics • Examine email message • Copy email message – also forward email • View and examine email header: tools available for outlook and other email clients • Examine additional files such as address books • Trace the message using various Internet tools • Examine network logs (netflow analysis) • Note: UTD Netflow tools SCRUB are in SourceForge

7. Email Servers • Need to work with the network administrator on how to retrieve messages from the server • Understand how the server records and handles the messages • How are the email logs created and stored • How are deleted email messages handled by the server? Are copies of the messages still kept? • Chapter 12 discussed email servers by UNIX, Microsoft, Novell

8. Email Forensics Tools • Several tools for Outlook Express, Eudora Exchange, Lotus notes • Tools for log analysis, recovering deleted emails, • Examples: • AccessData FTK • FINALeMAIL • EDBXtract • MailRecovery

9. Worm Detection: Introduction • What are worms? • Self-replicating program; Exploits software vulnerability on a victim; Remotely infects other victims • Evil worms • Severe effect; Code Red epidemic cost $2.6 Billion • Goals of worm detection • Real-time detection • Issues • Substantial Volume of Identical Traffic, Random Probing • Methods for worm detection • Count number of sources/destinations; Count number of failed connection attempts • Worm Types • Email worms, Instant Messaging worms, Internet worms, IRC worms, File-sharing Networks worms • Automatic signature generation possible • EarlyBird System (S. Singh -UCSD); Autograph (H. Ah-Kim - CMU)

10. Email Worm Detection using Data Mining • Task: • given some training instances of both “normal” and “viral” emails, • induce a hypothesis to detect “viral” emails. • We used: • Naïve Bayes • SVM Outgoing Emails The Model Test data Feature extraction Classifier Machine Learning Training data Cleanor Infected ?

11. Assumptions • Features are based on outgoing emails. • Different users have different “normal” behaviour. • Analysis should be per-user basis. • Two groups of features • Per email (#of attachments, HTML in body, text/binary attachments) • Per window (mean words in body, variable words in subject) • Total of 24 features identified • Goal: Identify “normal” and “viral” emails based on these features

12. Feature sets • Per email features • Binary valued Features • Presence of HTML; script tags/attributes; embedded images; hyperlinks; • Presence of binary, text attachments; MIME types of file attachments • Continuous-valued Features • Number of attachments; Number of words/characters in the subject and body • Per window features • Number of emails sent; Number of unique email recipients; Number of unique sender addresses; Average number of words/characters per subject, body; average word length:; Variance in number of words/characters per subject, body; Variance in word length • Ratio of emails with attachments

13. Data Mining Approach Classifier Clean/ Infected Test instance Clean/ Infected infected? SVM Naïve Bayes Test instance Clean? Clean

14. Data set • Collected from UC Berkeley. • Contains instances for both normal and viral emails. • Six worm types: • bagle.f, bubbleboy, mydoom.m, • mydoom.u, netsky.d, sobig.f • Originally Six sets of data: • training instances: normal (400) + five worms (5x200) • testing instances: normal (1200) + the sixth worm (200) • Problem: Not balanced, no cross validation reported • Solution: re-arrange the data and apply cross-validation

15. Our Implementation and Analysis • Implementation • Naïve Bayes: Assume “Normal” distribution of numeric and real data; smoothing applied • SVM: with the parameter settings: one-class SVM with the radial basis function using “gamma” = 0.015 and “nu” = 0.1. • Analysis • NB alone performs better than other techniques • SVM alone also performs better if parameters are set correctly • mydoom.m and VBS.Bubbleboy data set are not sufficient (very low detection accuracy in all classifiers) • The feature-based approach seems to be useful only when we have • identified the relevant features • gathered enough training data • Implement classifiers with best parameter settings

16. Mobile Device/System Forensics • Mobile device forensics overview • Acquisition procedures • Summary

17. Mobile Device Forensics Overview • What is stored in cell phones • Incoming/outgoing/missed calls • Text messages • Short messages • Instant messaging logs • Web pages • Pictures • Calendars • Address books • Music files • Voice records

18. Mobile Phones • Multiple generations • Analog, Digital personal communications, Third generations (increased bandwidth and other features) • Digital networks • CDMA, GSM, TDMA, - - - • Proprietary OSs • SIM Cards (Subscriber Identity Module) • Identifies the subscriber to the network • Stores personal information, addresses books, etc. • PDAs (Personal digital assistant) • Combines mobile phone and laptop technologies

19. Acquisition procedures • Mobile devices have volatile memory, so need to retrieve RAM before losing power • Isolate device from incoming signals • Store the device in a special bag • Need to carry out forensics in a special lab (e.g., SAIAL) • Examine the following • Internal memory, SIM card, other external memory cards, System server, also may need information from service provider to determine location of the person who made the call

20. Mobile Forensics Tools • Reads SIM Card files • Analyze file content (text messages etc.) • Recovers deleted messages • Manages PIN codes • Generates reports • Archives files with MD5, SHA-1 hash values • Exports data to files • Supports international character sets

21. Papers to discuss: November 10, 2008 • FORZA – Digital forensics investigation framework that incorporate legal issues • http://dfrws.org/2006/proceedings/4-Ieong.pdf • A cyber forensics ontology: Creating a new approach to studying cyber forensics • http://dfrws.org/2006/proceedings/5-Brinson.pdf • Arriving at an anti-forensics consensus: Examining how to define and control the anti-forensics problem • http://dfrws.org/2006/proceedings/6-Harris.pdf

22. Papers to discuss November 17, 2008 • Forensic feature extraction and cross-drive analysis • http://dfrws.org/2006/proceedings/10-Garfinkel.pdf • md5bloom: Forensic file system hashing revisited (OPTIONAL) • http://dfrws.org/2006/proceedings/11-Roussev.pdf • Identifying almost identical files using context triggered piecewise hashing (OPTIONAL) • http://dfrws.org/2006/proceedings/12-Kornblum.pdf • A correlation method for establishing provenance of timestamps in digital evidence • http://dfrws.org/2006/proceedings/13-%20Schatz.pdf