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Midterm. Quiz 2 Posted on DEN. Same as quiz 1 Due by Wed 3/16 Should be taken after you complete your Firewalls lab Grading : If you take both quizzes I’ll just use the higher grade. If you skip one I’ll average both grades. Human Behavior Modeling 1.

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quiz 2 posted on den
Quiz 2 Posted on DEN
  • Same as quiz 1
  • Due by Wed 3/16
  • Should be taken after you complete your Firewalls lab
  • Grading: If you take both quizzes I’ll just use the higher grade. If you skip one I’ll average both grades.
human behavior modeling 1
Human Behavior Modeling1

1“Modeling Human Behavior for Defense Against Flash Crowd Attacks”, Oikonomou, Mirkovic 2009.

  • Goal: defend against flash-crowd attacks on Web servers
  • Model human behavior along three dimensions
    • Dynamics of interaction with server (trained)
      • Detect aggressive clients as attackers
    • Semantics of interaction with server (trained)
      • Detect clients that browse unpopular content or use unpopular paths as attackers
    • Processing of visual and textual cues
      • Detect clients that click on invisible or uninteresting links as attackers
can it work
Can It Work?
  • Attackers can bypass detection if they
    • Act non-aggressively
    • Use each bot for just a few requests, then replace it
  • But this forces attacker to use many bots
    • Tens to hundreds of thousands
    • Beyond reach of most attackers
  • Other flooding attacks will still work
advantages and limitations
Advantages And Limitations
  • Transparent to users
  • Low false positives and false negatives
  • Requires server modification
  • Server must store data about each client
  • Will not work against other flooding attacks
  • May not protect services where humans do not generate traffic, e.g., DNS
viruses vs worms
Viruses vs. Worms
  • Viruses don’t break into your computer – they are invited by you
    • They cannot spread unless you run infected application or click on infected attachment
    • Early viruses spread onto different applications on your computer
    • Contemporary viruses spread as attachments through E-mail, they will mail themselves to people from your addressbook
  • Worms break into your computer using some vulnerability, install malicious code and move on to other machines
    • You don’t have to do anything to make them spread
what is a worm
What is a Worm?
  • A program that:
    • Scans network for vulnerable machines
    • Breaks into machines by exploiting the vulnerability
    • Installs some piece of malicious code – backdoor, DDoS tool
    • Moves on
  • Unlike viruses
    • Worms don’t need any user action to spread – they spread silently and on their own
    • Worms don’t attach themselves onto other programs – they exist as a separate code in memory
  • Sometimes you may not even know your machine has been infected by a worm
why are worms dangerous
Why Are Worms Dangerous?
  • They spread extremely fast
  • They are silent
  • Once they are out, they cannot be recalled
  • They usually install malicious code
  • They clog the network
first worm ever morris worm
First Worm Ever – Morris Worm
  • Robert Morris, a PhD student at Cornell, was interested in network security
  • He created the first worm with a goal to have a program live on the Internet in Nov. 1988
    • Worm was supposed only to spread, fairly slowly
    • It was supposed to take just a little bit of resources so not to draw attention to itself
    • But things went wrong …
  • Worm was supposed to avoid duplicate copies by asking a computer whether it is infected
    • To avoid false “yes” answers, it was programmed to duplicate itself every 7th time it received “yes” answer
    • This turned out to be too much
first worm ever morris worm1
First Worm Ever – Morris Worm
  • It exploited four vulnerabilities to break in
    • A bug insendmail
    • A bug in finger deamon
    • A trusted hosts feature (/etc/.rhosts)
    • Password guessing
  • Worm was replicating at a much faster rate than anticipated
  • At that time Internet was small and homogeneous (SUN and VAX workstations running BSD UNIX)
  • It infected around 6,000 computers, one tenth of then-Internet, in a day
first worm ever morris worm2
First Worm Ever – Morris Worm
  • People quickly devised patches and distributed them (Internet was small then)
  • A week later all systems were patched and worm code was removed from most of them
  • No lasting damage was caused
  • Robert Morris paid $10,000 fine, was placed on probation and did some community work
  • Worm exposed not only vulnerabilities in UNIX but moreover in Internet organization
  • Users didn’t know who to contact and report infection or where to look for patches
first worm ever morris worm3
First Worm Ever – Morris Worm
  • In response to Morris Worm DARPA formed CERT (Computer Emergency Response Team) in November 1988
    • Users report incidents and get help in handling them from CERT
    • CERT publishes security advisory notes informing users of new vulnerabilities that need to be patched and how to patch them
    • CERT facilitates security discussions and advocates better system management practices
code red
Code Red
  • Spread on July 12 and 19, 2001
  • Exploited a vulnerability in Microsoft Internet Information Server that allows attacker to get full access to the machine (turned on by default)
  • Two variants – both probed random machines, one with static seed for RNG, another with random seed for RNG (CRv2)
  • CRv2 infected more than 359,000 computers in less than 14 hours
    • It doubled in size every 37 minutes
    • At the peak of infection more than 2,000 hosts were infected each minute
code red v21
Code Red v2
  • 43% of infected machines were in US
  • 47% of infected machines were home computers
  • Worm was programmed to stop spreading at midnight, then attack www1.whitehouse.gov
    • It had hardcoded IP address so White House was able to thwart the attack by simply changing the IP address-to-name mapping
  • Estimated damage ~2.6 billion
sapphire slammer worm
Sapphire/Slammer Worm
  • Spread on January 25, 2003
  • The fastest computer worm in history
    • It doubled in size every 8.5 seconds.
    • It infected more than 90% of vulnerable hosts within 10 minutes
    • It infected 75,000 hosts overall
  • Exploited buffer overflow vulnerability in Microsoft SQL server, discovered 6 months earlier
sapphire slammer worm1
Sapphire/Slammer Worm
  • No malicious payload
  • The aggressive spread had severe consequences
    • Created DoS effect
    • It disrupted backbone operation
    • Airline flights were canceled
    • Some ATM machines failed
why was slammer so fast
Why Was Slammer So Fast?
  • Both Slammer and Code Red 2 use random scanning
    • Code Red uses multiple threads that invoke TCP connection establishment through 3-way handshake – must wait for the other party to reply or for TCP timeout to expire
    • Slammer packs its code in single UDP packet – speed is limited by how many UDP packets can a machine send
    • Could we do the same trick with Code Red?
  • Slammer authors tried to use linear congruentialgenerators to generate random addresses for scanning, but programmed it wrong
sapphire slammer worm3
Sapphire/Slammer Worm
  • 43% of infected machines were in US
  • 59% of infected machines were home computers
  • Response was fast – after an hour sites started filtering packetsfor SQL server port
stuxnet worm
Stuxnet Worm
  • Discovered in June/July 2010
  • Targets industrial equipment
  • Uses Windows vulnerabilities (known and new) to break in
  • Installs PLC (Programmable Logic Controller) rootkit and reprograms PLC
    • Without physical schematic it is impossible to tell what’s the ultimate effect
  • Spread via USB drives
  • Updates itself either by reporting to server or by exchanging code with new copy of the worm
scanning strategies
Scanning Strategies
  • Many worms use random scanning
  • This works well only if machines have very good RNGs with different seeds
  • Getting large initial population represents a problem
    • Then the infection rate skyrockets
    • The infection eventually reaches saturation since all machines are probing same addresses

“Warhol Worms: The Potential for Very Fast Internet Plagues”, Nicholas C Weaver

scanning strategies1
Scanning Strategies
  • Worm can get large initial population with hitlistscanning
  • Assemble a list of potentially vulnerable machines prior to releasing the worm – a hitlist
    • E.g., through a slow scan
  • When the scan finds a vulnerable machine, hitlist is divided in half and one half is communicated to this machine upon infection
    • This guarantees very fast spread – under one minute!
scanning strategies2
Scanning Strategies
  • Worm can get prevent die-out in the end with permutationscanning
  • All machines share a common pseudorandom permutation of IP address space
  • Machines that are infected continue scanning just after their point in the permutation
    • If they encounter already infected machine they will continue from a random point
  • Partitioned permutation is the combination of permutation and hitlist scanning
    • In the beginning permutation space is halved, later scanning is simple permutation scan
scanning strategies3
Scanning Strategies
  • Worm can get behind the firewall, or notice the die-out and then switch to subnetscanning
  • Goes sequentially through subnet address space, trying every address
infection strategies
Infection Strategies
  • Several ways to download malicious code
    • From a central server
    • From the machine that performed infection
    • Send it along with the exploit in a single packet
worm defense
Worm Defense
  • Three factors define worm spread:
    • Size of vulnerable population
      • Prevention – patch vulnerabilities, increase heterogeneity
    • Rate of infection (scanning and propagation strategy)
      • Deploy firewalls
      • Distribute worm signatures
    • Length of infectious period
      • Patch vulnerabilities after the outbreak
how well can containment do
How Well Can Containment Do?
  • This depends on several factors:
    • Reaction time
    • Containment strategy – address blacklisting and content filtering
    • Deployment scenario – where is response deployed
  • Evaluate effect of containment 24 hours after the onset

“Internet Quarantine: Requirements for Containing Self-Propagating Code”, Proceedings of INFOCOM 2003, D. Moore, C. Shannon, G. Voelker, S. Savage

how well can containment do code red
How Well Can Containment Do?Code Red

Idealized deployment: everyone deploysdefenses after given period

how well can containment do depending on worm aggressiveness
How Well Can Containment Do?Depending on Worm Aggressiveness

Idealized deployment: everyone deploysdefenses after given period

how well can containment do1
How Well Can Containment Do?
  • Reaction time needs to be within minutes, if not seconds
  • We need to use content filtering
  • We need to have extensive deployment on key points in the Internet
detecting and stopping worm spread
Detecting and Stopping Worm Spread
  • Monitor outgoing connection attempts to new hosts
  • When rate exceeds 5 per second, put the remaining requests in a queue
  • When number of requests in a queue exceeds 100 stop all communication

“Implementing and testing a virus throttle”, Proceedings of Usenix Security Symposium 2003,J. Twycross, M. Williamson

cooperative strategies for worm defense
Cooperative Strategies for Worm Defense
  • Organizations share alerts and worm signatures with their “friends”
    • Severity of alerts is increased as more infection attempts are detected
    • Each host has a severity threshold after which it deploys response
  • Alerts spread just like worm does
    • Must be faster to overtake worm spread
    • After some time of no new infection detections, alerts will be removed

“Cooperative Response Strategies for Large-Scale Attack Mitigation”, Proceedings of DISCEX 2003, D. Norjiri, J. Rowe, K. Levitt

cooperative strategies for worm defense1
Cooperative Strategies for Worm Defense
  • As number of friends increases, response is faster
  • Propagating false alarms is a problem
early worm detection
Early Worm Detection
  • Early detection would give time to react until the infection has spread
  • The goal of this paper is to devise techniques that detect new worms as they just start spreading
  • Monitoring:
    • Monitor and collect worm scan traffic
    • Observation data is very noisy so we have to filter new scans from
      • Old worms’ scans
      • Port scans by hacking toolkits

C. C. Zou, W. Gong, D. Towsley, and L. Gao. "The Monitoring and Early Detection of Internet Worms," IEEE/ACM Transactions on Networking. 

early worm detection1
Early Worm Detection
  • Detection:
    • Traditional anomaly detection: threshold-based
      • Check traffic burst (short-term or long-term).
      • Difficulties: False alarm rate
    • “Trend Detection”
      • Measure number of infected hosts and use it to detect worm exponential growth trend at the beginning
assumptions
Assumptions
  • Worms uniformly scan the Internet
    • No hitlists but subnet scanning is allowed
  • Address space scanned is IPv4
worm propagation model
Worm Propagation Model
  • Simple epidemic model:

Detect wormhere. Shouldhave exp. spread

monitoring system1
Monitoring System
  • Provides comprehensive observation data on a worm’s activities for the early detection of the worm
  • Consists of :
    • Malware Warning Center (MWC)
    • Distributed monitors
      • Ingress scan monitors – monitor incoming traffic going to unused addresses
      • Egress scan monitors – monitor outgoing traffic
monitoring system2
Monitoring System
  • Ingress monitors collect:
    • Number of scans received in an interval
    • IP addresses of infected hosts that have sent scans to the monitors
  • Egress monitors collect:
    • Average worm scan rate
  • Malware Warning Center (MWC) monitors:
    • Worm’s average scan rate
    • Total number of scans monitored
    • Number of infected hosts observed
worm detection
Worm Detection
  • MWC collects and aggregates reports from distributed monitors
  • If total number of scans is over a threshold for several consecutive intervals, MWC activates the Kalman filter and begins to test the hypothesis that the number of infected hosts follows exponential distribution
code red simulation
Code Red Simulation
  • Population: N=360,000, Infection rate:  = 1.8/hour,
  • Scan rate  = 358/min, Initially infected: I0=10
  • Monitored IP space 220, Monitoring interval:  = 1 minute

Infected hosts

 estimation

slammer simulation
Slammer Simulation
  • Population: N=100,000
  • Scan rate  = 4000/sec, Initially infected: I0=10
  • Monitored IP space 220, Monitoring interval:  = 1 second

Infected hosts

 estimation

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