Why Is DDoS Hard to Solve?

Why Is DDoS Hard to Solve?

1. A simple form of attack2. Designed to prey on the Internet’s strengths3. Easy availability of attack machines4. Attack can look like normal traffic5. Lack of Internet enforcement tools6. Hard to get cooperation from others7. Effective solutions hard to deploy

1. Simplicity Of Attack

• Basically, just send someone a lot of traffic• More complicated versions can add refinements, but

that’s the crux of it• No need to find new vulnerabilities• No need to worry about timing, tracing, etc.• Toolkits are readily available to allow the novice to

perform DDoS• Even distributed parts are very simple

2. Preys On Internet’s Strengths

• The Internet was designed to deliver lots of traffic – From lots of places, to lots of places

• DDoS attackers want to deliver lots of traffic from lots of places to one place

• Any individual packet can look proper to the Internet• Without sophisticated analysis, even the entire flow

can appear proper

Internet Resource Utilization

• Internet was not designed to monitor resource utilization– Most of it follows first come, first served model

• Many network services work the same way• And many key underlying mechanisms do, too• Thus, if a villain can get to the important resources

first, he can often deny them to good users

3. Availability Of Attack Machines

• DDoS is feasible because attackers can enlist many machines

• Attackers can enlist many machines because many machines are readily vulnerable

• Not hard to find 1,000 crackable machines on the Internet– Particularly if you don’t care which 1,000

• Botnets numbering hundreds of thousands of hosts have been discovered

Can’t We Fix These Vulnerabilities?

• DDoS attacks don’t really harm the attacking machines

• Many people don’t protect their machines even when the attacks can harm them

• Why will they start protecting their machines just to help others?

• Altruism has not yet proven to be a compelling argument for for network security

4. Attacks Resemble Normal Traffic

• A DDoS attack can consist of vast number of requests for a web server’s home page

• No need for attacker to use particular packets or packet contents

• So neat filtering/signature tools may not help• Attacker can be arbitrarily sophisticated at mirroring

legitimate traffic– In principle– Not often done because dumb attacks work so well

5. Lack Of Enforcement Tools

• DDoS attackers have never been caught by tracing or observing attack

• Only by old-fashioned detective work– Really, only when they’re dumb enough to boast about

their success• The Internet offers no help in tracing a single attack

stream, much less multiple ones• Even if you trace them, a clever attacker leaves no

clues of his identity on those machines

What Is the Internet Lacking?

• No validation of IP source address• No enforcement of amount of resources used• No method of tracking attack flows

– Or those controlling attack flows• No method of assigning responsibility for bad packets

or packet streams• No mechanism or tools for determining who

corrupted a machine

6. Poor Cooperation In the Internet• It’s hard to get anyone to help you stop or trace or

prevent an attack• Even your ISP might not be too cooperative• Anyone upstream of your ISP is less likely to be

cooperative– ISPs more likely to cooperate with each other, though

• Even if cooperation occurs, it occurs at human timescales– The attack might be over by the time you figure out who to

call

7. Effective Solutions Hard To Deploy• The easiest place to deploy defensive systems is near your

own machine – Defenses there might not work well (firewall example)

• There are effective solutions under research– But they require deployment near attackers or in the Internet

core– Or, worse, in many places

• A working solution is useless without deployment– Hard to get anything deployed if deploying site

gets no direct advantage

Resource Limitations• Don’t allow an individual attack machine to use many

of a target’s resources• Requires:

– Authentication, or– Making the sender do special work (puzzles)

• Authentication schemes are often expensive for the receiver

• Existing legitimate senders largely not set up to handle doing special work

• Can still be overcome with a large enough army of zombies

Hiding From the Attacker

• Make it hard for anyone but legitimate clients to deliver messages at all

• E.g., keep your machine’s identity obscure• A possible solution for some potential targets

– But not for others, like public web servers• To the extent that approach relies on secrecy, it’s

fragile– Some such approaches don’t require secrecy

Resource Multiplication• As attacker demands more resources, supply them• Essentially, never allow resources to be depleted• Not always possible, usually expensive• Not clear that defender can keep ahead of the attacker• But still a good step against limited attacks• More advanced versions might use

Akamai-like techniques

Trace and Stop Attacks• Figure out which machines attacks come from• Go to those machines (or near them) and stop

the attacks• Tracing is trivial if IP source addresses aren’t

spoofed– Tracing may be possible even if they are spoofed

• May not have ability/authority to do anything once you’ve found the attack machines

• Not too helpful if attacker has a vast supply of machines

Filtering Attack Streams• The basis for most defensive approaches• Addresses the core of the problem by limiting the

amount of work presented to target• Key question is:

– What do you drop?• Good solutions drop all (and only) attack traffic• Less good solutions drop some (or all) of everything

Filtering Vs. Rate Limiting• Filtering drops packets with particular characteristics

– If you get the characteristics right, you do little collateral damage

– At odds with the desire to drop all attack traffic• Rate limiting drops packets on basis of amount of

traffic– Can thus assure target is not overwhelmed– But may drop some good traffic

• You can combine them (drop traffic for which you are sure is suspicious, rate-limit the rest) but you gain a little

Where Do You Filter?

Near the target?

Near the source?

In the network core?

In multiple places?

Filtering Location Choices• Near target• Near source• In core

Filtering Location Choices• Near target

– Easier to detect attack– Sees everything– May be hard to prevent collateral damage– May be hard to handle attack volume

• Near source• In core

Filtering Location Choices

• Near target• Near source

– May be hard to detect attack– Doesn’t see everything– Easier to prevent collateral damage– Easier to handle attack volume

• In core

Filtering Location Choices• Near target• Near source• In core

– Easier to handle attack volume– Sees everything (with sufficient deployment)– May be hard to prevent collateral damage– May be hard to detect attack

How Do You Detect Attacks?• Have database of attack signatures• Detect anomalous behavior

– By measuring some parameters for a long time and setting a baseline

• Detecting when their values are abnormally high– By defining which behavior must be obeyed starting from

some protocol specification

How Do You Filter?• Devise filters that encompass most of anomalous

traffic• Drop everything but give priority to legitimate-

looking traffic– It has some parameter values– It has certain behavior

DDoS Defense Challenges• Need for a distributed response • Economic and social factors• Lack of detailed attack information• Lack of defense system benchmarks• Difficulty of large-scale testing• Moving target

TCP SYN Flood• Attacker sends lots of TCP SYN packets

– Victim sends an ack, allocates space in memory– Attacker never replies– Goal is to fill up memory before entries time out and get

deleted• Usually spoofed traffic

– Otherwise patterns may be used for filtering– OS at the attacker or spoofed address may send RST and

free up memory

TCP SYN Cookies• Effective defense against TCP SYN flood

– Victim encodes connection information and time in ACK number

– Must be hard to craft values that get encoded into the same ACK number – use crypto for encoding

– Memory is only reserved when final ACK comes• Only the server must change

– But TCP options are not supported– And lost SYN ACKs are not repeated

Small-Packet Floods• Overwhelm routers

– Create a lot of pps– Exhaust CPU– Most routers can’t handle full bandwidth’s load of small

packets• No real solution, must filter packets somehow to

reduce router load

Shrew Attack• Periodically slam the victim with short, high-volume

pulses– Lead to congestion drops on client’s TCP traffic– TCP backs off– If loss is large back off to 1 MSS per RTT– Attacker slams again after a few RTTs

• Solution requires TCP protocol changes – Tough to implement since clients must be changed

Flash-Crowd Attack• Generate legitimate application traffic to the victim

– E.g., DNS requests, Web requests– Usually not spoofed– If enough bots are used no client appears too aggressive– Really hard to filter since both traffic and client behavior

seem identical between attackers and legitimate users

Reflector Attack• Generate service requests to public servers spoofing

the victim’s IP– Servers reply back to the victim overwhelming it– Usually done for UDP and ICMP traffic (TCP SYN flood

would only overwhelm CPU if huge number of packets is generated)

– Often takes advantage of amplification effect – some service requests lead to huge replies; this lets attacker amplify his attack

Sample Research Defenses• Pushback• Traceback• SOS• Proof-of-work systems• Human behavior modeling

Pushback1

• Goal: Preferentially drop attack traffic to relieve congestion

• Local ACC: Enable core routers to respond to congestion locally by:– Profiling traffic dropped by RED– Identifying high-bandwidth aggregates– Preferentially dropping aggregate traffic to enforce

desired bandwidth limit • Pushback: A router identifies the upstream

neighbors that forward the aggregate traffic to it, requests that they deploy rate-limit

1”Controlling high bandwidth aggregates in the network,” Mahajan, Bellovin, Floyd, Paxson, Shenker, ACM CCR, July 2002

Can it Work?• Even a few core routers are able to control high-

volume attacks• Separation of traffic aggregates improves current

situation – Only traffic for the victim is dropped– Drops affect a portion containing the attack traffic

• Likely to successfully control the attack, relieving congestion in the Internet

• Will inflict collateral damage on legitimate traffic

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Advantages and Limitations+ Routers can handle high traffic volumes+ Deployment at a few core routers can affect

many traffic flows, due to core topology+ Simple operation, no overhead for routers+ Pushback minimizes collateral damage by placing

response close to the sources– Pushback only works in contiguous deployment– Collateral damage is inflicted by response, whenever

attack is not clearly separable– Requires modification of existing core routers

Traceback1

• Goal: locate the agent machines• Each packet header may carry a mark, containing:

– EdgeID (IP addresses of the routers) specifying an edge it has traversed

– The distance from the edge• Routers mark packets probabilistically• If a router detects half-marked packet (containing only

one IP address) it will complete the mark• Victim under attack reconstructs the path from the

marked packets

1“Practical network support for IP Traceback,” Savage, Wetherall, Karlin, Anderson, ACM SIGCOMM 2000

Traceback and IP Spoofing

• Traceback does nothing to stop DDoS attacks• It only identifies attackers’ true locations

– Comes to a vicinity of attacker• If IP spoofing were not possible in the Internet,

traceback would not be necessary• There are other approaches to filter out spoofed traffic

Can it Work?• Incrementally deployable, a few disjoint routers can

provide beneficial information• Moderate router overhead (packet modification)• A few thousand packets are needed even for long path

reconstruction• Does not work well for highly distributed attacks• Path reassembly is computationally demanding, and is

not 100% accurate:– Path information cannot be used for legal purposes– Routers close to the sources can efficiently block attack traffic,

minimizing collateral damage

Advantages and Limitations+ Incrementally deployable+ Effective for non-distributed attacks and for highly

overlapping attack paths+ Facilitates locating routers close to the sources– Packet marking incurs overhead at routers, must

be performed at slow path– Path reassembly is complex and prone to errors– Reassembly of distributed attack paths is

prohibitively expensive

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SOS1

• Goal: route only “verified user” traffic to the server, drop everything else

• Clients use overlay network to reach the server• Clients are authenticated at the overlay entrance, their

packets are routed to proxies• Small set of proxies are “approved” to reach the server,

all other traffic is heavily filtered out

1“ SOS: Secure Overlay Services,” Keromytis, Misra, Rubensteain, ACM SIGCOMM 2002

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SOS• User first contacts nodes that can check its legitimacy and

let him access the overlay – access points• An overlay node uses Chord overlay routing

protocol to send user’s packets to a beacon• Beacon sends packets to a secret servlet• Secret servlets tunnel packets to the firewall• Firewall only lets through packets with an IP

of a secret servlet– Secret servlet’s identity has to be hidden, because

their source address is a passport for the realm beyond the firewall

– Beacons are nodes that know the identity of secret servlets• If a node fails, other nodes can take its role

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Can It Work?• SOS successfully protects communication with a

private server:– Access points can distinguish legitimate from attack

communications – Overlay protects traffic flow– Firewall drops attack packets

• Redundancy in the overlay and secrecy of the path to the target provide security against DoS attacks on SOS

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Advantages And Limitations+ Ensures communication of “verified user”

with the victim+ Resilient to overlay node failure+ Resilient to DoS on the defense system– Does not work for public service– Traffic routed through the overlay travels on

suboptimal path– Brute force attack on links leading to the firewall still

possible

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Client Puzzles1

• Goal: defend against connection depletion attacks• When under attack:

– Server distributes small cryptographic puzzles to clients requesting service

– Clients spend resources to solve the puzzles– Correct solution, submitted on time, leads to state

allocation and connection establishment– Non-validated connection packets are dropped

• Puzzle generation is stateless• Client cannot reuse puzzle solutions• Attacker cannot make use of intercepted packets

1“Client puzzles: A cryptographic countermeasure against connection depletion attacks,” Juels, Brainard, NDSS 1999

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Can It Work?• Client puzzles guarantee that each client has spent a

certain amount of resources• Server determines the difficulty of the puzzle

according to its resource consumption– Effectively server controls its resource consumption

• Protocol is safe against replay or interception attacks• Other flooding attacks will still work

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Advantages And Limitations+ Forces the attacker to spend resources, protects

server resources from depletion+ Attacker can only generate a certain number of

successful connections from one agent machine+ Low overhead on server– Requires client modification– Will not work against highly distributed attacks– Will not work against bandwidth consumption

attacks (Defense By Offense paper changes this)

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Human Behavior Modeling1

• 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

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

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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

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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

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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 51

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 worm52

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

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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. 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

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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

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• 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 patches56


• 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

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• 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 minute58

Code Red

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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

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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

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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

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• Both Slammer and Code Red 2 use random scanningo 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

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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 packetsfor SQL server port

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BGP Impact of Slammer Worm

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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

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Scanning Strategies

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

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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!

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Scanning Strategies

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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 scan72

Scanning Strategies

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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

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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

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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 deploysdefenses after given period

How Well Can Containment Do?Depending on Worm Aggressiveness

Idealized deployment: everyone deploysdefenses 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



• 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 wormhere. Shouldhave 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: I0=10• Monitored IP space 220, Monitoring interval: = 1 minute

Code Red Simulation

Infected hosts estimation

• Population: N=100,000• Scan rate = 4000/sec, Initially infected: I0=10• Monitored IP space 220, Monitoring interval: = 1 second

Slammer Simulation

Infected hosts estimation

Documents

Why Is DDoS Hard to Solve?