Malware

Malware CS155 Spring 2009 Elie Bursztein

Welcome to the zoo What malware are How do they infect hosts How do they hide How do they propagate Zoo visit ! How to detect them Worms

What is a malware ? A Malware is a set of instructions that run on your computer and make your system do something that an attacker wants it to do.

What it is good for ? Steal personal information Delete files Click fraud Steal software serial numbers Use your computer as relay

A recent illustration Christians On Facebook Leader hacked on march 2009 Post Islamic message Lost >10 000 members

The Malware Zoo Virus Backdoor Trojan horse Rootkit Scareware Adware Worm

What is a Virus ? a program that can infect other programs by modifying them to include a, possibly evolved, version of itself Fred Cohen 1983

Some Virus Type Polymorphic : uses a polymorphic engine to mutate while keeping the original algorithm intact (packer) Methamorpic : Change after each infection

What is a trojan A trojan describes the class of malware that appears to perform a desirable function but in fact performs undisclosed malicious functions that allow unauthorized access to the victim computer Wikipedia

What is rootkit A root kit is a component that uses stealth to maintain a persistent and undetectable presence on the machine Symantec

What is a worm A computer worm is a self-replicating computer program. It uses a network to send copies of itself to other nodes and do so without any user intervention.

Almost 30 years of Malware From Malware fighting malicious code

History 1981 First reported virus : Elk Cloner (Apple 2) 1983 Virus get defined 1986 First PC virus MS DOS 1988 First worm : Morris worm 1990 First polymorphic virus 1998 First Java virus 1998 Back orifice 1999 Melissa virus 1999 Zombie concept 1999 Knark rootkit 2000 love bug 2001 Code Red Worm 2001 Kernel Intrusion System 2001 Nimda worm 2003 SQL Slammer worm Melissa spread by email and share Knark rootkit made by creed demonstrate the first ideas love bug vb script that abused a weakness in outlook Kernl intrusion by optyx gui and efficent hidding mechanims

Number of malware signatures Symantec report 2009

Malware Repartition Panda Q1 report 2009

Outline What malware are How do they infect hosts How do they propagate Zoo visit ! How to detect them Worms

What to Infect Executable Interpreted file Kernel Service MBR Hypervisor

Overwriting malware Targeted Executable Malware Malware

prepending malware Targeted Executable Malware Infected host Executable Malware

appending malware Targeted Executable Malware Infected host Executable Malware

Cavity malware Targeted Executable Infected host Executable Malware Malware

Multi-Cavity malware Targeted Executable Malware Malware Malware Malware

Packers Malware Infected host Executable Packer Payload

Packer functionalities Compress Encrypt Randomize (polymorphism) Anti-debug technique (int / fake jmp) Add-junk Anti-VM Virtualization

Auto start Folder auto-start : C:\Documents and Settings\[user_name]\Start Menu\Programs\Startup Win.ini : run=[backdoor]" or "load=[backdoor]". System.ini : shell=”myexplorer.exe” Wininit Config.sys

Auto start cont. Assign know extension (.doc) to the malware Add a Registry key such as HKCU\SOFTWARE\Microsoft\Windows \CurrentVersion\Run Add a task in the task scheduler Run as service

Unix autostart Init.d /etc/rc.local .login .xsession crontab crontab -e /etc/crontab

Macro virus Use the builtin script engine Example of call back used (word) AutoExec() AutoClose() AutoOpen() AutoNew()

Document based malware MS Office Open Office Acrobat

Userland root kit Perform login sshd passwd Hide activity ps netstat ls find du

Subverting the Kernel Kernel task Process management File access Memory management Network management What to hide Process Files Network traffic

Kernel rootkit PS KERNEL Hardware : HD, keyboard, mouse, NIC, GPU P1 P2 P3 P3 rootkit

Subverting techniques Kernel patch Loadable Kernel Module Kernel memory patching (/dev/kmem)

Windows Kernel P1 P2 Pn Csrss.exe Win32 subsystem DLLs User32.dll, Gdi32.dll and Kernel32.dll Other Subsytems (OS/2 Posix) Ntdll.dll ntoskrnl.exe Hardware Abstraction Layer (HAL.dll) Hardware Underlying kernel Executive

Kernel Device driver P2 Win32 subsystem DLLs Ntdll.dll ntoskrnl.exe Interrupt Hook System service dispatcher System service dispatch table Driver Overwriting functions Driver Replacing Functions New pointer A C B

MBR/Bootkit Bootkits can be used to avoid all protections of an OS, because OS consider that the system was in trusted stated at the moment the OS boot loader took control.

BIOS MBR VBS NT Boot Sector BOOTMGR.EXE WINLOAD.EXE Windows 7 kernel HAL.DLL

Vboot Work on every Windows (vista,7) 3ko Bypass checks by letting them run and then do inflight patching Communicate via ping

Hypervisor rootkit Target OS Hardware App App

Hypervisor rootkit Target OS Hardware App App Virtual machine monitor Host OS Rogue app

Email propagation from pandalab blog

Valentine day ... Waledac malicious domain from pandalab blog

Fake antivirus from pandalab blog

Hijack you browser from pandalab blog

Fake page ! from pandalab blog

P2P Files Popular query 35.5% are malwares ( Kalafut 2006 )

Basic InfectedHost Attacker TCP

Reverse InfectedHost Attacker TCP

covert InfectedHost Attacker ICMP

Rendez vous backdoor InfectedHost Attacker RDV Point

BackOrifice Defcon 1998 new version in 2000

Netbus 1998 Used for “prank”

Ransomware Trj/SMSlock.A Russian ransomware April 2009 To unlock you need to send an SMS with the text4121800286to the number3649Enter the resulting code:Any attempt to reinstall the system may lead to loss of important information and computer damage from pandalab blog

Anti-virus Analyze system behavior Analyze binary to decide if it a virus Type : Scanner Real time monitor

Impossibility result It is not possible to build a perfect virus/malware detector (Cohen)

Impossibility result Diagonal argument P is a perfect detection program V is a virus V can call P if P(V) = true -> halt if P(V) = false -> spread

Virus signature Find a string that can identify the virus Fingerprint like

Heuristics Analyze program behavior Network access File open Attempt to delete file Attempt to modify the boot sector

Checksum Compute a checksum for Good binary Configuration file Detect change by comparing checksum At some point there will more malware than “goodware” ...

Sandbox analysis Running the executable in a VM Observe it File activity Network Memory

Dealing with Packer Launch the exe Wait until it is unpack Dump the memory

Worm A worm is self-replicating software designed to spread through the network Typically, exploit security flaws in widely used services Can cause enormous damage Launch DDOS attacks, install bot networks Access sensitive information Cause confusion by corrupting the sensitive information Worm vs Virus vs Trojan horse A virus is code embedded in a file or program Viruses and Trojan horses rely on human intervention Worms are self-contained and may spread autonomously

Cost of worm attacks Morris worm, 1988 Infected approximately 6,000 machines 10% of computers connected to the Internet cost ~ $10 million in downtime and cleanup Code Red worm, July 16 2001 Direct descendant of Morris’ worm Infected more than 500,000 servers Programmed to go into infinite sleep mode July 28 Caused ~ $2.6 Billion in damages, Love Bug worm: $8.75 billion Statistics: Computer Economics Inc., Carlsbad, California

Internet Worm (First major attack) Released November 1988 Program spread through Digital, Sun workstations Exploited Unix security vulnerabilities VAX computers and SUN-3 workstations running versions 4.2 and 4.3 Berkeley UNIX code Consequences No immediate damage from program itself Replication and threat of damage Load on network, systems used in attack Many systems shut down to prevent further attack

Some historical worms of note Kienzle and Elder Worm Date Distinction Morris 11/88 Used multiple vulnerabilities, propagate to “nearby” sys ADM 5/98 Random scanning of IP address space Ramen 1/01 Exploited three vulnerabilities Lion 3/01 Stealthy, rootkit worm Cheese 6/01 Vigilante worm that secured vulnerable systems Code Red 7/01 First sig Windows worm; Completely memory resident Walk 8/01 Recompiled source code locally Nimda 9/01 Windows worm: client-to-server, c-to-c, s-to-s, … Scalper 6/02 11 days after announcement of vulnerability; peer-to-peer network of compromised systems Slammer 1/03 Used a single UDP packet for explosive growth

Increasing propagation speed Code Red, July 2001 Affects Microsoft Index Server 2.0, Windows 2000 Indexing service on Windows NT 4.0. Windows 2000 that run IIS 4.0 and 5.0 Web servers Exploits known buffer overflow in Idq.dll Vulnerable population (360,000 servers) infected in 14 hours SQL Slammer, January 2003 Affects in Microsoft SQL 2000 Exploits known buffer overflow vulnerability Server Resolution service vulnerability reported June 2002 Patched released in July 2002 Bulletin MS02-39 Vulnerable population infected in less than 10 minutes

Code Red Initial version released July 13, 2001 Sends its code as an HTTP request HTTP request exploits buffer overflow Malicious code is not stored in a file Placed in memory and then run When executed, Worm checks for the file C:\Notworm If file exists, the worm thread goes into infinite sleep state Creates new threads If the date is before the 20th of the month, the next 99 threads attempt to exploit more computers by targeting random IP addresses

Code Red of July 13 and July 19 Initial release of July 13 1 st through 20 th month: Spread via random scan of 32-bit IP addr space 20 th through end of each month: attack. Flooding attack against 198.137.240.91 ( www.whitehouse.gov) Failure to seed random number generator ⇒ linear growth Revision released July 19, 2001. White House responds to threat of flooding attack by changing the address of www.whitehouse.gov Causes Code Red to die for date ≥ 20 th of the month. But: this time random number generator correctly seeded Slides: Vern Paxson

Measuring activity: network telescope Monitor cross-section of Internet address space, measure traffic “ Backscatter” from DOS floods Attackers probing blindly Random scanning from worms LBNL’s cross-section: 1/32,768 of Internet UCSD, UWisc’s cross-section: 1/256 .

Spread of Code Red Network telescopes estimate of # infected hosts: 360K. (Beware DHCP & NAT) Course of infection fits classic logistic . Note: larger the vulnerable population, faster the worm spreads. That night ( ⇒ 20 th ), worm dies … … except for hosts with inaccurate clocks! It just takes one of these to restart the worm on August 1 st … Slides: Vern Paxson

Code Red 2 Released August 4, 2001. Comment in code: “Code Red 2.” But in fact completely different code base. Payload: a root backdoor, resilient to reboots. Bug: crashes NT , only works on Windows 2000. Localized scanning : prefers nearby addresses. Kills Code Red 1 . Safety valve: programmed to die Oct 1, 2001. Slides: Vern Paxson

Striving for Greater Virulence: Nimda Released September 18, 2001. Multi-mode spreading: attack IIS servers via infected clients email itself to address book as a virus copy itself across open network shares modifying Web pages on infected servers w/ client exploit scanning for Code Red II backdoors (!) worms form an ecosystem! Leaped across firewalls. Slides: Vern Paxson

Code Red 2 kills off Code Red 1 Code Red 2 settles into weekly pattern Nimda enters the ecosystem Code Red 2 dies off as programmed CR 1 returns thanks to bad clocks Slides: Vern Paxson

How do worms propagate? Scanning worms : Worm chooses “random” address Coordinated scanning : Different worm instances scan different addresses Flash worms Assemble tree of vulnerable hosts in advance, propagate along tree Not observed in the wild, yet Potential for 106 hosts in < 2 sec ! [Staniford] Meta - server worm :Ask server for hosts to infect (e.g., Google for “powered by phpbb”) Topological worm: Use information from infected hosts (web server logs, email address books, config files, SSH “known hosts”) Contagion worm : Propagate parasitically along with normally initiated communication

slammer 01/25/2003 Vulnerability disclosed : 25 june 2002 Better scanning algorithm UDP Single packet : 380bytes

Consequences ATM systems not available Phone network overloaded (no 911!) 5 DNS root down Planes delayed

Worm Detection and Defense Detect via honeyfarms : collections of “honeypots” fed by a network telescope. Any outbound connection from honeyfarm = worm. (at least, that’s the theory) Distill signature from inbound/outbound traffic. If telescope covers N addresses, expect detection when worm has infected 1/N of population. Thwart via scan suppressors : network elements that block traffic from hosts that make failed connection attempts to too many other hosts 5 minutes to several weeks to write a signature Several hours or more for testing

Need for automation Current threats can spread faster than defenses can reaction Manual capture/analyze/signature/rollout model too slow months days hrs mins secs Contagion Period Signature Response Period 1990 Time 2005 Slide: Carey Nachenberg, Symantec Program Viruses Macro Viruses E-mail Worms Network Worms Flash Worms Pre- automation Post- automation Contagion Period Signature Response Period

Signature inference Challenge need to automatically learn a content “signature” for each new worm – potentially in less than a second! Some proposed solutions Singh et al, Automated Worm Fingerprinting, OSDI ’04 Kim et al, Autograph: Toward Automated, Distributed Worm Signature Detection, USENIX Sec ‘04

Signature inference Monitor network and look for strings common to traffic with worm-like behavior Signatures can then be used for content filtering Slide: S Savage

Content sifting Assume there exists some (relatively) unique invariant bitstring W across all instances of a particular worm ( true today, not tomorrow... ) Two consequences Content Prevalence : W will be more common in traffic than other bitstrings of the same length Address Dispersion : the set of packets containing W will address a disproportionate number of distinct sources and destinations Content sifting : find W ’s with high content prevalence and high address dispersion and drop that traffic Slide: S Savage

Observation: High-prevalence strings are rare (Stefan Savage, UCSD *) Only 0.6% of the 40 byte substrings repeat more than 3 times in a minute

The basic algorithm (Stefan Savage, UCSD *) Address Dispersion Table Sources Destinations Prevalence Table Detector in network A B cnn.com C D E

(Stefan Savage, UCSD *) 1 (B) 1 (A) Address Dispersion Table Sources Destinations 1 Prevalence Table Detector in network A B cnn.com C D E

(Stefan Savage, UCSD *) 1 (A) 1 (C) 1 (B) 1 (A) Address Dispersion Table Sources Destinations 1 1 Prevalence Table Detector in network A B cnn.com C D E

(Stefan Savage, UCSD *) 1 (A) 1 (C) 2 (B,D) 2 (A,B) Address Dispersion Table Sources Destinations 1 2 Prevalence Table Detector in network A B cnn.com C D E

(Stefan Savage, UCSD *) 1 (A) 1 (C) 3 (B,D,E) 3 (A,B,D) Address Dispersion Table Sources Destinations 1 3 Prevalence Table Detector in network A B cnn.com C D E

Challenges Computation To support a 1Gbps line rate we have 12us to process each packet, at 10Gbps 1.2us, at 40Gbps… Dominated by memory references; state expensive Content sifting requires looking at every byte in a packet State On a fully-loaded 1Gbps link a naïve implementation can easily consume 100MB/sec for table Computation/memory duality: on high-speed (ASIC) implementation, latency requirements may limit state to on-chip SRAM (Stefan Savage, UCSD *)

Malware

More Related Content

What's hot(20)

Viewers also liked(20)

Similar to Malware(20)

More from Tuhin_Das(14)

Malware

Editor's Notes