Midterm 1

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 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 3 1 “Modeling Human Behavior for Defense Against Flash Crowd Attacks”, Oikonomou, Mirkovic 2009.

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 4

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 5

Worms 6

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 , 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 7 Viruses vs. Worms

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 8 What is a Worm?

They spread extremely fast They are silent Once they are out, they cannot be recalled They usually install malicious code They clog the network 9 Why Are Worms Dangerous?

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 – 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 7 th time it received “yes” answer – This turned out to be too much 10 First Worm Ever – Morris Worm

It exploited four vulnerabilities to break in – A bug in sendmail – 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 11 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 12 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 13 First Worm Ever – Morris Worm

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 14 Code Red

15 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 16 Code Red v2

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 17 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 18 Sapphire/Slammer Worm

19 Sapphire/Slammer Worm

Both Slammer and Code Red 2 use random scanning o 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 o Slammer packs its code in single UDP packet – speed is limited by how many UDP packets can a machine send o Could we do the same trick with Code Red? Slammer authors tried to use linear congruential generators to generate random addresses for scanning, but programmed it wrong 20 Why Was Slammer So Fast?

43% of infected machines were in US 59% of infected machines were home computers Response was fast – after an hour sites started filtering packets for SQL server port 21 Sapphire/Slammer Worm

22 BGP Impact of Slammer Worm

23 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

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

25 Random Scanning

Worm can get large initial population with hitlist scanning 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! 26 Scanning Strategies

27 Hitlist Scanning

Worm can get prevent die-out in the end with permutation scanning 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 28 Scanning Strategies

29 Permutation Scanning

Worm can get behind the firewall, or notice the die-out and then switch to subnet scanning Goes sequentially through subnet address space, trying every address 30 Scanning 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 31 Infection Strategies

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 Worm Defense

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 How Well Can Containment Do? “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 Idealized deployment: everyone deploys defenses after given period

How Well Can Containment Do? Depending on Worm Aggressiveness Idealized deployment: everyone deploys defenses after given period

How Well Can Containment Do? Depending on Deployment Pattern

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 How Well Can Containment Do?

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

Detecting and Stopping Worm Spread

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

As number of friends increases, response is faster Propagating false alarms is a problem Cooperative Strategies for Worm Defense

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

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 Early Worm Detection

Worms uniformly scan the Internet – No hitlists but subnet scanning is allowed Address space scanned is IPv4 Assumptions

Simple epidemic model: Worm Propagation Model Detect worm here. Should have exp. spread

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 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 Monitoring System

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 Worm Detection

Population: N=360,000, Infection rate:  = 1.8/hour, Scan rate  = 358/min, Initially infected: I 0 =10 Monitored IP space 2 20, Monitoring interval:  = 1 minute Code Red Simulation Infected hosts  estimation

Population: N=100,000 Scan rate  = 4000/sec, Initially infected: I 0 =10 Monitored IP space 2 20, Monitoring interval:  = 1 second Slammer Simulation Infected hosts  estimation