15-446 Networked Systems Practicum Lecture 14 – Worms/Viruses/Botnets 1

15-446 Networked Systems Practicum

Lecture 14 – Worms/Viruses/Botnets

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Outline

• Worms

• Worm Defense

• Botnet/Viruses

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What is a Computer Worm?

• Self replicating network program• Exploit vulnerabilities to infect remote machines• Victim machines continue to propagate infection

• Three main stages• Detect new targets• Attempt to infect new targets• Activate code on victim machine

• Difference w/ computer virus?• No human intervention required

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NetworkNetwork

Why Worry About Worms?

• Speed• Much faster than viruses

• CRv2: 14 hours for 359.000 victims• Slammer: 10 minutes for 75.000 victims

• Faster than human reaction• Highly malicious payloads

• DDoS or data corruption

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Some Major Worms

Worm Year Strategy Victims Other Notes

Morris 1988 Topological

scanning

6K First major autonomous worm

Code Red 2001 Random

scanning

~300K First recent "fast" worm

Nimda 2001 Local scanning

~200K Local subnet scanning Effective mix of techniques

Slammer 2003 Random

scanning

>75K Spread worldwide in 10 minutes

MyDoom 2004 Topological

scanning

<15K First “Zero Day” Worm

Conficker 2008 Random

scanning

>15M? Largest infection, capability of updates

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Threat Model

Traditional• High-value targets• Insider threats

Worms & Botnets• Automated attack of

millions of targets• Value in aggregate,

not individual systems• Threats: Software

vulnerabilities; naïve users

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... and it's profitable

• Botnets used for• Spam (and more spam)?• Credit card theft• DDoS extortion

• Flourishing Exchange market• Spam proxying: 3-10 cents/host/week• 25k botnets: $40k - $130k/year• Also for stolen account compromised

machines, credit cards, identities, etc. (be worried)?

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Why is this problem hard?

• Monoculture: little “genetic diversity” in hosts

• Instantaneous transmission: Almost entire network within 500ms

• Slow immune response: human scales (10x-1Mx slower!)?

• Poor hygiene: Out of date / misconfigured systems; naïve users

• Intelligent designer ... of pathogens

• Near-Anonymitity

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Code Red I v1

• July 12th, 2001• Exploited a known vulnerability in Microsoft’s Internet

Information Server (IIS)• Buffer overflow in a rarely used URL decoding routine –

published June 18th

• 1st – 19th of each month: attempts to spread• Random scanning of IP address space• 99 propagation threads, 100th defaced pages on server• Static random number generator seed

• Every worm copy scans the same set of addresses Linear growth

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Code Red I v1

• 20th – 28th of each month: attacks• DDOS attack against 198.137.240.91 (

www.whitehouse.gov)

• Memory resident – rebooting the system removes the worm• However, could quickly be reinfected

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Code Red I v2

• July 19th, 2001• Largely same codebase – same author?• Ends website defacements• Fixes random number generator seeding bug

• Scanned address space grew exponentially• 359,000 hosts infected in 14 hours• Compromised almost all vulnerable IIS servers on internet

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Analysis of Code Red I v2

• Random Constant Spread model• Constants

• N = total number of vulnerable machines• K = initial compromise rate, per hour• T = Time at which incident happens

• Variables• a = proportion of vulnerable machines

compromised• t = time in hours

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Analysis of Code Red I v2

N = total number of vulnerable machinesK = initial compromise rate, per hourT = Time at which incident happens

Variablesa = proportion of vulnerable machines compromisedt = time in hours

“Logistic equation”Rate of growth of epidemic in finite systems when all entities have an equal likelihood of infecting any other entity

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Code Red I v2 – Plot

• K = 1.8• T = 11.9

Hourly probe rate data for inbound port 80 at the Chemical Abstracts Service during the initial outbreak of Code Red I on July 19th, 2001.

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Improvements: Localized scanning

• Observation: Density of vulnerable hosts in IP address space is not uniform

• Idea: Bias scanning towards local network• Used in CodeRed II

• P=0.50: Choose address from local class-A network (/8)

• P=0.38: Choose address from local class-B network (/16)

• P=0.12: Choose random address

• Allows worm to spread more quickly15

Code Red II (August 2001)

• Began : August 4th, 2001

• Exploit : Microsoft IIS webservers (buffer overflow)

• Named “Code Red II” because :• It contained a comment stating so. However the

codebase was new.

• Infected IIS on windows 2000 successfully but caused system crash on windows NT.

• Installed a root backdoor on the infected machine.

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Improvements: Multi-vector

• Idea: Use multiple propagation methods simultaneously

• Example: Nimda• IIS vulnerability• Bulk e-mails• Open network shares• Defaced web pages• Code Red II backdoor

Onset of Nimda

Time (PDT) 18 September, 2001

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1/2 hour

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Better Worms: Hit-list Scanning

• Worm takes a long time to “get off the ground”

• Worm author collects a list of, say, 10000 vulnerable machines

• Worm initially attempts to infect these hosts

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How to build Hit-List

• Stealthy randomized scan over number of months

• Distributed scanning via botnet• DNS searches – e.g. assemble domain list,

search for IP address of mail server in MX records

• Web crawling spider similar to search engines• Public surveys – e.g. Netcraft• Listening for announcements – e.g. vulnerable

IIS servers during Code Red I

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Better Worms: Permutation scanning

• Problem: Many addresses are scanned multiple times

• Idea: Generate random permutation of all IP addresses, scan in order• Hit-list hosts start at their own position in the

permutation• When an infected host is found, restart at a random

point• Can be combined with divide-and-conquer approach

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H0 H4 H1 H3 H2H1 (Restart)

Warhol Worm

• Simulation shows that employing the two previous techniques, can attack 300,000 hosts in less than 15 minutes

• Conventional = 10 scans/sec

• Fast Scanning = 100 scans/sec

• Warhol = 100 scans/sec,• Permutation scanning

and 10,000 entry hit list

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Flash worms

• A flash worm would start with a hit list that contains most/all vulnerable hosts

• Realistic scenario:• Complete scan takes 2h with an OC-12• Internet warfare?

• Problem: Size of the hit list• 9 million hosts 36 MB• Compression works: 7.5MB• Can be sent over a 256kbps DSL link in 3 seconds

• Extremely fast:• Full infection in tens of seconds!

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Surreptitious worms

• Idea: Hide worms in inconspicuous traffic to avoid detection

• Leverage P2P systems?• High node degree• Lots of traffic to hide in• Proprietary protocols• Homogeneous software• Immense size

(30,000,000 Kazaa downloads!)

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Example Outbreak: SQL Slammer (2003)

• Single, small UDP packet exploit (376 b)• First ~1min: classic random scanning

• Doubles # of infected hosts every ~8.5sec• (In comparison: Code Red doubled in 40min)

• After 1min, starts to saturate access b/w• Interferes with itself, so it slows down• By this point, was sending 20M pps• Peak of 55 million IP scans/sec @ 3min

• 90% of Internet scanned in < 10mins• Infected ~100k or more hosts

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Stuxnet Worm

• The first worm for control systems• Discovered in June 2010• Attack SCADA systems using Siemens

WinCC/PCS 7 software • Not only spying but also reprogram

programmable logic controllers (PLCs)• Four zero-day attacks used• Infection includes Iran (62K) and China (6M?)• Nation-wide support cyberwarefare?

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Prevention

• Get rid of the or permute vulnerabilities• (e.g., address space randomization) • makes it harder to compromise

• Block traffic (firewalls)• only takes one vulnerable computer wandering between in &

out or multi-homed, etc.• Keep vulnerable hosts off network

• incomplete vuln. databases & 0-day worms• Slow down scan rate

• Allow hosts limited # of new contacts/sec.• Can slow worms down, but they do still spread

• Quarantine• Detect worm, block it

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Outline

• Worms

• Worm Defense

• Botnet/Viruses

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Context

• Worm Detection• Scan detection• Honeypots• Host based behavioral detection

• Payload-based

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Worm: Countermeasures

• Signature-based worm scan filtering• Vulnerable to polymorphic worms

• Scan detection• High scanning activity to identify victims• Scanning with high failure rate compared to legitimate users

(DNS)• TCP RST, ICMP Unreachable

• Two dimensions: time, space• Rate limiting, rate halting• False positive (Index crawler, NAT, etc.)• Disruption to legitimate services• Not applicable to UDP based propagation

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Worm behavior

• Content Invariance• Limited polymorphism e.g. encryption

• key portions are invariant e.g. decryption routine

• Content Prevalence• invariant portion appear frequently

• Address Dispersion• # of infected distinct hosts grow overtime

• reflecting different source and dest. addresses

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Signature Inference

• Content prevalence: Autograph, EarlyBird, etc.• Assumes some content invariance• Pretty reasonable for starters.

• Goal: Identify “attack” substrings• Maximize detection rate• Minimize false positive rate

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Content Sifting

• For each string w, maintain • prevalence(w): Number of times it is found in

the network traffic• sources(w): Number of unique sources

corresponding to it• destinations(w): Number of unique destinations

corresponding to it

• If thresholds exceeded, then block(w)

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Issues

• How to compute prevalence(w), sources(w) and destinations(w) efficiently?

• Scalable

• Low memory and CPU requirements

• Real time deployment over a Gigabit link

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Estimating Content Prevalence

• Table[payload] • 1 GB table filled in 10 seconds

• Table[hash[payload]]• 1 GB table filled in 4 minutes• Tracking millions of ants to track a few

elephants• Collisions...false positives

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stream memoryArray of counters

Hash(Pink)

Multistage Filters

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packet memoryArray of counters

Hash(Green)

Multistage Filters

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packet memoryArray of counters

Hash(Green)

Multistage Filters

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packet memory

Multistage Filters

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packet memoryCollisions are OK

Multistage Filters

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packet memory

packet1 1

Insert

Reached threshold

Multistage Filters

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packet memory

packet1 1

Multistage Filters

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packet memory

packet1 1

packet2 1

Multistage Filters

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Stage 2

packet memory

packet1 1

Stage 1

Multistage Filters

No false negatives!(guaranteed detection)

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Gray = all prior packets

Conservative Updates

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Redundant

Redundant


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Value Sampling

• The problem: s-b+1 substrings

• Solution: Sample

• But: Random sampling is not good enough

• Trick: Sample only those substrings for which the fingerprint matches a certain pattern

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sources(w) & destinations(w)

• Address Dispersion

• Counting distinct elements vs. repeating elements

• Simple list or hash table is too expensive

• Key Idea: Bitmaps

• Trick : Scaled Bitmaps

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Bitmap counting – direct bitmap

HASH(green)=10001001

Set bits in the bitmap using hash of the flow ID of incoming packets

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HASH(blue)=00100100

Different flows have different hash values

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Packets from the same flow always hash to the same bit

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HASH(violet)=10010101

Collisions OK, estimates compensate for them

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HASH(orange)=11110011

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HASH(pink)=11100000

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HASH(yellow)=01100011

As the bitmap fills up, estimates get inaccurate

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Solution: use more bits


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Solution: use more bits

Problem: memory scales with the number of flows

HASH(blue)=00100100

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Bitmap counting – virtual bitmap

Solution: a) store only a portion of the bitmap

b) multiply estimate by scaling factor

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HASH(pink)=11100000

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Problem: estimate inaccurate when few flows active

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Bitmap counting – multiple bmps

Solution: use many bitmaps, each accurate for a different range

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HASH(pink)=11100000

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64


Use this bitmap to estimate number of flows

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Use this bitmap to estimate number of flows

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Bitmap counting – multires. bmp

Problem: must update up to three bitmaps per packet

Solution: combine bitmaps into one

OR

OR

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HASH(pink)=11100000


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Multiresolution Bitmaps

• Still too expensive to scale

• Scaled bitmap• Recycles the hash space with too many bits set• Adjusts the scaling factor according

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Scaled Bitmap

• Idea: Subsample the range of hash space• How it works?

• multiple bitmaps each mapped to progressively smaller and smaller portions of the hash space.

• bitmap recycled if necessary.

Result

Roughly 5 time less memory + actual estimation of address dispersion

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Putting It Together

header payload

substring fingerprintssubstring fingerprints

key src cnt dest cnt

AD entry exist?update counters

key cntelseupdate counter

cnt > prevalence threshold?create AD entry

Content Prevalence Table

Address Dispersion Table

counters > dispersion threshold?report key as suspicious worm

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Putting It Together

• Sample frequency: 1/64

• String length: 40

• Use 4 hash functions to update prevalence table• Multistage filter reset every 60 seconds

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Parameter Tuning

• Prevalence threshold: 3• Very few signatures repeat

• Address dispersion threshold• 30 sources and 30 destinations• Reset every few hours• Reduces the number of reported signatures

down to ~25,000

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Parameter Tuning

• Tradeoff between and speed and accuracy• Can detect Slammer in 1 second as opposed to

5 seconds • With 100x more reported signatures

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False Negatives in EB

• False Negatives• Very hard to prove...

• Earlybird detected all worm outbreaks reported on security lists over 8 months

• EB detected all worms detected by Snort (signature-based IDS)?

• And some that weren't

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False Positives in EB

• Common protocol headers• HTTP, SMTP headers• p2p protocol headers

• Non-worm epidemic activity• Spam• BitTorrent (!)

• Solution:• Small whitelist...

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Outline

• Worms

• Worm Defense

• Botnet/Viruses

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... and it's profitable

• Botnets used for• Spam (and more spam)?• Credit card theft• DDoS extortion

• Flourishing Exchange market• Spam proxying: 3-10 cents/host/week• 25k botnets: $40k - $130k/year• Also for stolen account compromised

machines, credit cards, identities, etc. (be worried)?

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Botnet

• A group of zombie computers under the remote control of an attacker via a command and control (C&C) server

C&C Server

Attacker

ZombiesTarget Sites

CommandAttack

C&C Server

C&C Server

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Botnet Countermeasure

• Detecting new botnets by using honeypots, analyzing spam pools, capturing group activities in DNS

• Sinkholing or nullrouting C&C server connections and cleaning zombies

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Outline

• Worms

• Worm Defense

• Botnet/Viruses

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Malicious Code

• Many types of malicious code• Virus, worm, botnet, spyware, spam, etc.

• Who writes this and why?• Challenge (for fun)• Fame (for pride)• Business (for money)

• Black markets for attacks (DDoS and spams) and info(credit cards, vulnerabilities)

• Ideology (for activism)• Hactivism, cyberterrorism, cyberwarefare

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What is a Computer Virus?

• Program that spreads itself by infecting (modifying) an executable file and making copies of itself

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Components

1. Propagation mechanism• Sharing infected file with other computers

• USB drive, email attachment, and shared folders

• Executing infected file Infect other computers and spread infection

2. Trigger• Time/condition when payload is activated

3. Payload• Damage existing files• Extort sensitive information• Consume computer’s resources

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Infected File

1 Insert document in fax machine. (Program entry-point)

2 Dial the phone number.

3 Hit the SEND button on the fax.

4 Wait for completion. If a problem occurs, go back to step 1.

5 End task.

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1 Skip to step 6.

2 Dial the phone number.

3 Hit the SEND button on the fax.

4 Wait for completion. If a problem occurs, go back to step 1.

5 End task.

6 VIRUS instructions

7 Insert document in fax machine and go to step 2.

Before

After

Nachenberg, Computer Virus-Antivirus Coevolution, CACM 1997

Propagation

• Virus replicates when infected file is executed• Task is not entirely automated

• User makes the first step• Virus copies malicious code to other files• Jump instruction to malicious code is added

• Why are Windows-based viruses most prolific?• Largest population• Why write a virus if only a few people are infected?

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Detection

• Infected file has a larger size than initial version of file

• Scanners record files lengths and searches for changes

• Virus can easily bypass detection through compression

(packing)

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P P

V

PP P

V

Detection (cont’d)

• Virus signature• Same structure and bit pattern for

uniquely identifying a virus

90New malicious code signatures, Symantec 2010

More Advanced Viruses

• Encrypted viruses• Prevent “signature” to detect virus via encryption

Polymorphic viruses Change virus code to prevent signature

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Detection of Encrypted Virus

• A different encryption key is generated for each new infection• Therefore, encrypted virus body appears

different in each infected file• Antivirus can no longer parse virus body for the

virus signatures

• Still pattern matching possible• Still identical copy of decryption routine

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Detection of Polymorphic Viruses

• Advanced encrypted virus• Formerly, constant decryption routine• Now, mutable decryption routine

• unique crypto code generated for each copy

• No more signatures in code

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Documents

15-446 Networked Systems Practicum Lecture 14 – Worms/Viruses/Botnets 1