Transcript Document
Automated Worm Fingerprinting Sumeet Singh, Cristian Estan, George Varghese, and Stefan Savage
Introduction Problem: how to react quickly to worms?
CodeRed 2001 Infected ~360,000 hosts within 11 hours Sapphire/Slammer (376 bytes) 2002 Infected ~75,000 hosts within 10 minutes
Existing Approaches Detection Ad hoc intrusion detection Characterization Manual signature extraction Isolates and decompiles a new worm Look for and test unique signatures Can take hours or days
Existing Approaches Containment Updates to anti-virus and network filtering products
Earlybird Automatically detect and contain new worms Two observations Some portion of the content in existing worms is invariant Rare to see the same string recurring from many sources to many destinations
Earlybird Automatically extract the signature of all known worms Also Blaster, MyDoom, and Kibuv.B hours or days before any public signatures were distributed Few false positives
Background and Related Work Almost all IPs were scanned by Slammer < 10 minutes Limited only by bandwidth constraints
The SQL Slammer Worm: 30 Minutes After “Release”
- Infections doubled every 8.5 seconds - Spread 100X faster than Code Red - At peak, scanned 55 million hosts per second.
Network Effects Of The SQL Slammer Worm At the height of infections Several ISPs noted significant bandwidth consumption at peering points Average packet loss approached 20% South Korea lost almost all Internet service for period of time Financial ATMs were affected Some airline ticketing systems overwhelmed
Signature-Based Methods Pretty effective if signatures can be generated quickly For CodeRed, 60 minutes For Slammer, 1 – 5 minutes
Worm Detection Three classes of methods Scan detection Honeypots Behavioral techniques
Scan Detection Look for unusual frequency and distribution of address scanning Limitations Not suited to worms that spread in a non random fashion (i.e. emails, IM, P2P apps) Based on a target list Spread topologically
Scan Detection More limitations Detects infected sites Does not produce a signature
Honeypots Monitored idle hosts with untreated vulnerabilities Used to isolate worms Limitations Manual extraction of signatures Depend on quick infections
Behavioral Detection Looks for unusual system call patterns Sending a packet from the same buffer containing a received packet Can detect slow moving worms Limitations Needs application-specific knowledge Cannot infer a large-scale outbreak
Characterization Process of analyzing and identifying a new worm Current approaches Use a priori vulnerability signatures Automated signature extraction
Vulnerability Signatures Example Slammer Worm UDP traffic on port 1434 that is longer than 100 bytes (buffer overflow) Can be deployed before the outbreak Can only be applied to well-known vulnerabilities
Some Automated Signature Extraction Techniques Allows viruses to infect decoy programs Extracts the modified regions of the decoy Uses heuristics to identify invariant code strings across infected instances
Some Automated Signature Extraction Techniques Limitation Assumes the presence of a virus in a controlled environment
Some Automated Signature Extraction Techniques Honeycomb Find longest common subsequences among sets of strings found in messages Autograph Uses network-level data to infer worm signatures Limitations Scale and full distributed deployments
Containment Mechanism used to deter the spread of an active worm Host quarantine Via IP ACLs on routers or firewalls String-matching Connection throttling On all outgoing connections
Host Quarantine Preventing an infected host from talking to other hosts Via IP ACLs on routers or firewalls
Defining Worm Behavior Content invariance Portions of a worm are invariant (e.g. the decryption routine) Content prevalence Appears frequently on the network Address dispersion Distribution of destination addresses more uniform to spread fast
Finding Worm Signatures Traffic pattern is sufficient for detecting worms Relatively straightforward Extract all possible substrings Raise an alarm when FrequencyCounter[substring] > threshold1 SourceCounter[substring] > threshold2 DestCounter[substring] > threshold3
Practical Content Sifting Characteristics Small processing requirements Small memory requirements Allows arbitrary deployment strategies
Estimating Content Prevalence Finding the packet payloads that appear at least x times among the N packets sent During a given interval
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
Multistage Filters Array of counters Hash(Pink) stream memory [Singh et al. 2002]
Multistage Filters Array of counters Hash(Green) packet memory
Multistage Filters Array of counters Hash(Green) packet memory
Multistage Filters packet memory
Multistage Filters Collisions are OK packet memory
Multistage Filters Reached threshold packet memory packet1 1 Insert
Multistage Filters packet memory packet1 1
Multistage Filters packet memory packet1 1 packet2 1
Multistage Filters Stage 1 packet memory packet1 1 No false negatives!
Stage 2
Conservative Updates Gray = all prior packets
Conservative Updates Redundant Redundant
Conservative Updates
Detecting Common Strings Cannot afford to detect all substrings Maybe can afford to detect all strings with a small fixed length
Detecting Common Strings Cannot afford to detect all substrings Maybe can afford to detect all strings with a small fixed length
A horse is a horse, of course, of course
F 1 = (c 1 p 4 + c 2 p 3 + c 3 p 2 + c 4 p 1 + c 5 ) mod M
Detecting Common Strings Cannot afford to detect all substrings Maybe can afford to detect all strings with a small fixed length F 2 = (c 2 p 4 + c 3 p 3 + c 4 p 2 + c 5 p 1 + c 6 ) mod M
A horse is a horse, of course, of course
F 1 = (c 1 p 4 + c 2 p 3 + c 3 p 2 + c 4 p 1 + c 5 ) mod M
Detecting Common Strings Cannot afford to detect all substrings Maybe can afford to detect all strings with a small fixed length F 2 = (c 2 p 4 + c 3 p 3 + c 4 p 2 + c 5 p 1 + c 6 ) mod M = (c 1 p 5 + c 2 p 4 + c 3 p 3 + c 4 p 2 + c 5 p 1 + c 6 - c 1 p 5 ) mod M = (pF 1 + c 6 - c 1 p 5 ) mod M
Detecting Common Strings Cannot afford to detect all substrings Maybe can afford to detect all strings with a small fixed length Still too expensive…
Estimating Address Dispersion Not sufficient to count the number of source and destination pairs e.g. send a mail to a mailing list Two sources —mail server and the sender Many destinations Need to track the distinct source and destination IP addresses For each substring
Bitmap counting – direct bitmap
Set bits in the bitmap using hash of the flow ID of incoming packets
HASH( green )=10001001
[Estan et al. 2003]
Bitmap counting – direct bitmap
Different flows have different hash values
HASH( blue )=00100100
Bitmap counting – direct bitmap
Packets from the same flow always hash to the same bit
HASH( green )=10001001
Bitmap counting – direct bitmap
Collisions OK, estimates compensate for them
HASH( violet )=10010101
Bitmap counting – direct bitmap
HASH( orange )=11110011
Bitmap counting – direct bitmap
HASH( pink )=11100000
Bitmap counting – direct bitmap
As the bitmap fills up, estimates get inaccurate
HASH( yellow )=01100011
Bitmap counting – direct bitmap
Solution: use more bits
HASH( green )=10001001
Bitmap counting – direct bitmap
Solution: use more bits Problem: memory scales with the number of flows
HASH( blue )=00100100
Bitmap counting – virtual bitmap
Solution: a) store only a portion of the bitmap b) multiply estimate by scaling factor
Bitmap counting – virtual bitmap
HASH( pink )=11100000
Bitmap counting – virtual bitmap
Problem: estimate inaccurate when few flows active
HASH( yellow )=01100011
Bitmap counting – multiple bmps
Solution: use many bitmaps, each accurate for a different range
Bitmap counting – multiple bmps
HASH( pink )=11100000
Bitmap counting – multiple bmps
HASH( yellow )=01100011
Bitmap counting – multiple bmps
Use this bitmap to estimate number of flows
Bitmap counting – multiple bmps
Use this bitmap to estimate number of flows
Bitmap counting – multires. bmp OR OR
Problem: must update up to three bitmaps per packet Solution: combine bitmaps into one
Bitmap counting – multires. bmp
HASH( pink )=11100000
Bitmap counting – multires. bmp
HASH( yellow )=01100011
Multiresolution Bitmaps Still too expensive to scale Scaled bitmap Recycles the hash space with too many bits set Adjusts the scaling factor according
Too CPU-Intensive A packet with 1,000 bytes of payload Needs 960 fingerprints for string length of 40 Prone to Denial-of-Service attacks
CPU Scaling Obvious approach: sampling - Random sampling may miss many substrings Solution: value sampling Track only certain substrings e.g. last 6 bits of fingerprint are 0 P(not tracking a worm) = P(not tracking
any
of its substrings)
CPU Scaling Example Track only substrings with last 6 bits = 0s String length = 40 P(finding a 100-byte signature) = 55% P(finding a 200-byte signature) = 92% P(finding a 400-byte signature) = 99.64%
Putting It Together Address Dispersion Table key src cnt dest cnt header payload substring fingerprints substring fingerprints AD entry exist?
update counters else update counter key cnt counters > dispersion threshold?
report key as suspicious worm Content Prevalence Table cnt > prevalence threshold?
create AD entry
Putting It Together Sample frequency: 1/64 String length: 40 Use 4 hash functions to update prevalence table Multistage filter reset every 60 seconds
System Design Two major components Sensors Sift through traffic for a given address space Report signatures An aggregator Coordinates real-time updates Distributes signatures
Implementation and Environment Written in C and MySQL (5,000 lines) rrd-tools library for graphical reporting PHP scripting for administrative control Prototype executes on a 1.6Ghz AMD Opteron 242 1U Server Linux 2.6 kernel
EarlyBird Processes 1TB of traffic per day Can keep up with 200Mbps of continuous traffic
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
Parameter Tuning Tradeoff between and speed and accuracy Can detect Slammer in 1 second as opposed to 5 seconds With 100x more reported signatures
Performance 200Mbps Can be pipelined and parallelized for achieve 40Gbps
Memory Consumption Prevalence table 4 stages Each with ~500,000 bins (8 bits/bin) 2MB total Address dispersion table 25K entries (28 bytes each) < 1 MB Total: < 4MB
Trace-Based Verification Two main sources of false positives 2,000 common protocol headers e.g. HTTP, SMTP Whitelisted SPAM e-mails BitTorrent Many-to-many download
False Negatives So far none Detected every worm outbreak
Inter-Packet Signatures An attacker might evade detection by splitting an invariant string across packets With 7MB extra, EarlyBird can keep per flow states and fingerprint across packets
Live Experience with EarlyBird Detected precise signatures CodeRed variants MyDoom mail worm Sasser Kibvu.B
Variant Content Polymorphic viruses Semantically equivalent but textually distinct code Invariant decoding routine
Extensions Self configuration Slow worms
Containment How to handle false positives?
If too aggressive, EarlyBird becomes a target for DoS attacks An attacker can fool the system to block a target message
Coordination Trust of deployed servers Validation Policy
Conclusions EarlyBird is a promising approach To detect unknown worms real-time To extract signatures automatically To detect SPAMs with minor changes Wire-speed signature learning is viable