1

DACODA [Crandall et al.; CCS 2005]

DAvis malCODe Analyzer Discover invariants in the exploit vector (ε)

Symbolic execution on the system trace during attacks that Minos catches

Used for an empirical analysis of polymorphism and metamorphismQuantify and understand the limits

2

Worm Polymorphism and Metamorphism Viruses: Defender has time to pick apart

the attacker’s techniques e.g. Algorithmic scanners, emulation

Worms: Attacker has time to pick apart the deployed network defense techniquesWhat can defenders do to evaluate the

robustness of defenses against attacks that don’t exist yet?

3

Measuring Poly/metamorphism

[Ma et al.; IMC 2006]Found relatively little polymorphism “in the

wild” Worm defense designers don’t have

samples of the poly/metamorphic techniques attackers will use on their defenses(Have to build the defense first)

4

How DACODA Works

“Information only has meaning in that it is subject to interpretation.” [Cohen, 1984]

Gives each byte of network data a unique label

Tracks these through the entire system Discovers predicates about how the host

under attack interprets the network bytes

5

mov al,[AddressWithLabel1832]

add al,4

cmp al,10

je JumpTargetIfEqualToTen

; AL.expr <= (Label 1832)

; AL.expr <= (ADD AL.Expr 4)

; /* AL.expr == (ADD (LABEL 1832) 4) */

; ZFLAG.left <= AL.expr

; /* ZFLAG.left == (ADD (Label 1832) 4) */

; ZFLAG.right <= 10

; P <= new Predicate(EQUAL ZFLAG.Left ZFLAG.Right)

; /* P == (EQUAL (ADD (Label 1832) 4) 10) */

; AddToSetOfKnownPredicates(P)

6

Actual Worms/Attacks Caught by Minos and Analyzed by DACODAName OS Port Class

Sasser WinXP 445TCP Buff.Over.

Blaster WinXP 135TCP Buff.Over.Workstation Serv. WinXP 445TCP Buff.Over.

RPCSS WinXP 135TCP Buff.Over.

Slammer Whist. 1434UDP Buff.Over.

Code Red II Whist. 80TCP Buff.Over.

Zotob Win2K 445TCP Buff.Over.

7

Other Attacks Caught by Minos and Analyzed by DACODA

Name OS Port Class

SQL Auth. Whist. 1434TCP Buff.Over.

rpc.statd Linux 111 & 918TCP

Form.Str.

innd Linux 119TCP Buff.Over.

Scalper OBSD 80TCP Int.Over.

ntpd FBSD 123TCP Buff.Over.

Turkey FBSD 21TCP OffByOne

8

Single Contiguous Byte Strings

Name Longest String

Sasser 36

Blaster 92

Work. 23

RPCSS 18Slammer 1

CRII 17

Zotob 36

Name Longest String

SQLAuth 4

rpc.statd 16

innd 27

Scalper 32

ntpd 8

Turkey 21

9

Single Contiguous Signatures

Autograph [Kim and Karp; USENIX Security 2004] and EarlyBird [Singh et al.; OSDI 2004] both demonstrated good results at about 40 bytes for the signature length

[Newsome et al.; IEEE S&P 2005] came to the same conclusion as we did and proposed sets of smaller byte strings called tokens

10

Tokens

GET /default.ida?XXXXXXXXXXXXXXX

XXXXXXXXXXXXXXXXXXXXXXXXXXX

X…XXXX%u9090%u6858%ucbd3%u7801%u9090%u6858%ucbd3%u7801%u9090%u6858%ucbd3%u7801%u9090%u9090%u8190%u00c3%u0003%u8b00%u531b%u53ff%u0078%u0000%u00=a HTTP/1.0

11

Where do These Tokens Come From? Scalper “Transfer-Encoding: chunked”

Same applies to most of these vulnerabilities “The Horns of a Dilemma”

Use protocol framing as a signature Be very precise

12

Conclusions from DACODA

Whole system analysis is important New focus on more semantic signatures

How to understand the semantics of the vulnerability?

We can learn a lot about emerging malware threats by studying existing malware samples and their interactions with the systems they run on

13

Temporal Search[Crandall et al.; ASPLOS 2006]

Automated discovery of timebomb attacks Analysis in the π stage

Prototype of behavior-based analysis Proposed a framework for a problem space nobody

has looked at before Implemented parts of it Identified the remaining challenges

By testing real worms with timebombs on our prototype

14

You as an antivirus professionalcatch a new worm…

Unpack it Polymorphism/

metamorphism? Anti-debugger tricks? Any behaviors predicated on

time? How it gets the time? UTC/Local? Conversions between

formats?

15

With Temporal Search… Infect a VM Automated, behavior-based

Temporal Search Respond

16

How to respond?

Sober.X – 6 and 7 January 2006 URLs blocked

Kama Sutra – 3rd of the month Users removed infections

Code Red – 20th of the month White House IP address changed

What if we have just hours or even minutes, not days?

17

Behavior-based Analysis

[Cohen, 1984] defined behavior-based detection as a question of “defining what is and is not a legitimate use of a service, and finding a means of detecting the difference.”

Behavior-based analysis is similarAssume the system is infected with malwareAnalyze its use of a service such as the PIT

18

Why not just speed up the clock?

Dramatic time perturbation would be easy to detectAlso not easy to do for a busy system

(effectively lowers perceived performance) May miss some behaviors

Kama Sutra Will not be able to explain behaviors it does

elicit

19

Basic Idea

Find timersRun the PIT at different rates of perceived

time System performance stays the same Correlate between PIT and memory writes

Symbolic execution e.g. with DACODA

Weakest precondition calculation

20

Filling in the Timetable

SystemTime Predicate Behavior

126,396,288e12

(13 July 2001)? >= 20 Spread

time

21

Filling in the Timetable

SystemTime Predicate Behavior

126,396,288e12

(13 July 2001)? >= 20 Spread

126,402,336e12

(20 July 2001)? >= 28 DoS White

House

time

22

Filling in the Timetable

SystemTime Predicate Behavior

126,396,288e12

(13 July 2001)? >= 20 Spread

126,402,336e12

(20 July 2001)? >= 28 DoS White

House

126,409,248e12

(28 July 2001)None Go to sleep

time

23

Windows

0

100

200

300

400

500

600

700

0 60 120 180 240 300 360 420 480

Real Time (seconds)

# P

red

icat

es C

hec

ked

per

Sec

on

d

Windows TickCount

Windows SystemTime

24

Manual Analysis

Many different library calls, APIs for date and time GetSystemTime(), GetLocalTime(), GetTimeZoneInformation(), DiffDate(),

GetDateFormat(), etc. System call not really necessary

Conversions back and forth between various represenations (e.g. MyParty.A, Blaster.E) UTC vs. Local 1600 vs. 1900 vs. 1970 32- vs 64-bit integers for day, month, year, etc. strings

Not always done with standard library functions Have to unpack it first, anti-debugging tricks All of this is simply dataflow from SystemTime timer

25

Setup

Bochs VM

w/ DACODA and Timer Discovery

Host @ 192.168.33.1

w/ DNS, NTP, HTTP, TIME, etc.

Windows XP @ 192.168.33.2

tuntap interface

ARP cache poisoning, DNS spoofing, etc.

ARP cache poisoning, DNS spoofing, etc.

26

Temporal Search

Symbolic Execution (DACODA)Cod Red, Blaster.E, MyParty.A, Klez.A

Discovers predicates on day, hour, minute, etc. on a real time trace

Control-flow sensitivity within loopsCod Red, Blaster.E, MyParty.A, Klez.A,

Sober.X Kama Sutra Month and year

27

Adversarial Analysis

For any technique, being applicable to every possible virus or worm is not a requirementAV companies collect intelligence

More details in the paper on this

28

Conclusions from Temporal Search

Manual analysis is tricky and time-consuming Temporal Search can dramatically improve response

time

Behavior-based analysis is all about the environment

Malware does not follow a linear timetable Gregorian calendar poses its own challenges

29

Why Behavior-Based Analysis?

“An ant, viewed as a behaving system, is quite simple. The apparent complexity of its behavior over time is largely a reflection of the complexity of the environment in which it finds itself.” –Herbert Simon