Transcript PPT - USC`s Center for Computer Systems Security

Grade Projections
• I’ve calculated two grades for everyone:
– Realistic: assumes your performance in the course
continues the same
– Optimistic: assumes you get maximum scores for
the rest of the course
• Some statistics:
– Realistic: 1 A, 3 A-, 6 Bs, 4 Cs, 5 Ds, 1 F
– Optimistic: 12 A, 7 A-, 1 B+, 1 B
• There is still a lot of room to improve
Dynamic Quarantine
• Worms spread very fast (minutes, seconds)
– Need automatic mitigation
• If this is a new worm, no signature exists
– Must apply behaviour-based anomaly detection
– But this has a false-positive problem! We don’t
want to drop legitimate connections!
• Dynamic quarantine
– “Assume guilty until proven innocent”
– Forbid access to suspicious hosts for a short time
– This significantly slows down the worm spread
C. C. Zou, W. Gong, and D. Towsley. "Worm Propagation Modeling and Analysis
under Dynamic Quarantine Defense," ACM CCS Workshop on Rapid Malcode (WORM'03),
Dynamic Quarantine
• Behavior-based anomaly detection can point
out suspicious hosts
– Need a technique that slows down worm spread
but doesn’t hurt legitimate traffic much
– “Assume guilty until proven innocent” technique
will briefly drop all outgoing connection attempts
(for a specific service) from a suspicious host
– After a while just assume that host is healthy, even
if not proven so
– This should slow down worms but cause only
transient interruption of legitimate traffic
Dynamic Quarantine
• Assume we have some anomaly detection
program that flags a host as suspicious
– Quarantine this host
– Release it after time T
– The host may be quarantined multiple times if the
anomaly detection raises an alarm
– Since this doesn’t affect healthy hosts’ operation a
lot we can have more sensitive anomaly detection
technique
Dynamic Quarantine
• An infectious host is quarantined after 1 time
l1
units
• A susceptible host is falsely quarantined after
time units 1
l2
• Quarantine time is T, after that we release the
host
• A few new categories:
– Quarantined infectious R(t)
– Quarantined susceptible Q(t)
Slammer With DQ
DQ With Large T?
T=10sec
T=30sec
DQ And Patching?
Patch Only Quarantined Hosts
Cleaning I(t)
Cleaning R(t)
DOMINO
• The goal is to build an overlay network so that
nodes cooperatively detect intrusion activity
– Cooperation reduces the number of false positives
• Overlay can be used for worm detection
• Main feature are active-sink nodes that detect
traffic to unused IP addresses
• The reaction is to build blacklists of infected
nodes
V. Yegneswaran, P. Barford, S. Jha, “Global Intrusion Detection in the DOMINO
Overlay System,” NDSS 2004
DOMINO Architecture
DOMINO Architecture
• Axis nodes collect, aggregate and share data
– Nodes in large, trustworthy ISPs
– Each node maintains a NIDS and an active sink over
large portion of unused IP space
• Access points grant access to axis nodes after
thorough administrative checks
• Satellite nodes form trees below an axis node,
collect information, deliver it to axis nodes and
pull relevant information
• Terrestrial nodes supply daily summaries of
port scan data
Information Sharing
• Every axis node maintains a global and local
view of intrusion activity
• Periodically a node receives summaries from
peers which are used to update global view
– List of worst offenders grouped per port
– Lists of top scanned ports
• RSA is used to authenticate nodes and signed
SHA digests are used to ensure message
integrity and authenticity
How Many Nodes We Need?
40 for port summaries
20 for worst offender list
How Frequent Info Exchange?
Staleness doesn’t matter much but more frequent lists are better to catch worst offenders
How Long Blacklists?
About 1000 IPs are enough
How Close Monitoring Nodes?
Blacklists in same /16 space are similar  satellites in /16 space should be grouped
under the same axis node and sets of /16 spaces should be randomly distributed
among different axis nodes
Automatic Worm Signatures
• Focus on TCP worms that propagate via
scanning
• Idea: vulnerability exploit is not easily mutable
so worm packets should have some common
signature
• Step 1: Select suspicious TCP flows using
heuristics
• Step 2: Generate signatures using content
prevalence analysis
Kim, H.-A. and Karp, B., Autograph: Toward Automated, Distributed Worm
Signature Detection, in the Proceedings of the 13th Usenix Security Symposium
(Security 2004), San Diego, CA, August, 2004.
Suspicious Flows
• Detect scanners as hosts that make many
unsuccessful connection attempts (>2)
• Select their successful flows as suspicious
• Build suspicious flow pool
– When there’s enough flows inside trigger signature
generation step
Signature Generation
• Use most frequent byte sequences across
flows as the signature
• Naïve techniques fail at byte insertion,
deletion, reordering
• Content-based payload partitioning (COPP)
– Partition if Rabin fingerprint of a sliding window
matches breakmark = content blocks
– Configurable parameters: window size, breakmark
– Analyze which content blocks appear most
frequently and what is the smallest set of those that
covers most/all samples in suspicious flow pool
How Well Does it Work?
• Tested on traces of HTTP traffic interlaced with
known worms
• For large block sizes and large coverage of
suspicious flow pool (90-95%) Autograph
performs very well
– Small false positives and false negatives
Distributed Autograph
• Would detect more scanners
• Would produce more data for suspicious flow
pool
– Reduce false positives and false negatives
Automatic Signatures (approach 2)
• Detect content prevalence
– Some content may vary but some portion of worm
remains invariant
• Detect address dispersion
– Same content will be sent from many hosts to many
destinations
• Challenge: how to detect these efficiently (low
cost = fast operation)
S.Singh, C. Estan, G. Varghese and S. Savage “Automated Worm Fingerprinting,”
OSDI 2004
Content Prevalence Detection
• Hash content + port + proto and use this as key
to a table where counters are kept
– Content hash is calculated over overlapping blocks
of fixed size
– Use Rabin fingerprint as hash function
– Autograph calculates Rabin fingerprint over
variable-length blocks that are non-overlapping
– Rabin fingerprint is a hash function that is efficient
to recalculate if a portion of the input changes
Address Dispersion Detection
• Remembering sources and destinations for
each content would require too much memory
• Scaled bitmap:
– Sample down input space, e.g., hash into values 063 but only remember those values that hash into
0-31
– Set the bit for the output value (out of 32 bits)
– Increase sampling-down factor each time bitmap is
full = constant space, flexible counting
How Well Does This Work?
• Implemented and deployed at UCSD network
How Well Does This Work?
• Some false positives
– Spam, common HTTP protocol headers .. (easily
whitelisted)
– Popular BitTorrent files (not easily whitelisted)
• No false negatives
– Detected each worm outbreak reported in news
– Cross-checked with Snort’s signature detection
Polymorphic Worm Signatures
• Insight: multiple invariant substrings must be
present in all variants of the worm for the
exploit to work
– Protocol framing (force the vulnerable code down
the path where the vulnerability exists)
– Return address
• Substrings not enough = too short
• Signature: multiple disjoint byte strings
– Conjunction of byte strings
– Token subsequences (must appear in order)
– Bayes-scored substrings (score + threshold)
J. Newsome, B. Karp and D. Song, “Polygraph:
Automatically Generating Signatures for Polymorphic Worms,”
IEEE Security and Privacy Symposium, 2005
Worm Code Structure
• Invariant bytes: any change makes the worm
fail
• Wildcard bytes: any change has no effect
• Code bytes: Can be changed using some
polymorphic technique and worm will still
work
– E.g., encryption
Polygraph Architecture
• All traffic is seen, some is identified as part of
suspicious flows and sent to suspicious traffic
pool
– May contain some good traffic
– May contain multiple worms
• Rest of traffic is sent to good traffic pool
• Algorithm makes a single pass over pools and
generates signatures
Signature Detection
• Extract tokens (variable length) that occur in at
least K samples
– Conjuction signature is this set of tokens
– To find token-subsequence signatures samples in
the pool are aligned in different ways (shifted left or
right) so that the maximum-length subsequences
are identified
– Contiguous tokens are preferred
– For Bayes signatures for each token a probability is
computed that it is contained by a good or a
suspicious flow – use this as a score
– Set high value of threshold to avoid false positives
How Well Does This Work?
• Legitimate traffic traces: HTTP and DNS
– Good traffic pool
– Some of this traffic mixed with worm traffic to
model imperfect separation
• Worm traffic: Ideally-polymorphic worms
generated from 3 known exploits
• Various tests conducted
How Well Does This Work?
• When compared with single signature (longest
substring) detection, all proposed signatures
result in lower false positive rates
– False negative rate is always zero if the suspicious
pool has at least three samples
• If some good traffic ends up in suspicious pool
– False negative rate is still low
– False positive rate is low until noise gets too big
• If there are multiple worms in suspicious pool
and noise
– False positives and false negatives are still low
Borrowed from Brent ByungHoon Kang, GMU
Borrowed from Brent ByungHoon Kang, GMU
A Network of Compromised Computers on the Internet
IP locations of the Waledac botnet.
Borrowed from Brent ByungHoon Kang, GMU
Botnets
• Networks of compromised machines under
the control of hacker, “bot-master”
• Used for a variety of malicious purposes:
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Sending Spam/Phishing Emails
Launching Denial of Service attacks
Hosting Servers (e.g., Malware download site)
Proxying Services (e.g., FastFlux network)
Information Harvesting (credit card, bank
credentials, passwords, sensitive data.)
Borrowed from Brent ByungHoon Kang, GMU
Botnet with Central Control Server
After resolving the IP address for the IRC server, bot-infected machines
CONNECT to the server, JOIN a channel, then wait for commands.
Borrowed from Brent ByungHoon Kang, GMU
Botnet with Central Control Server
The botmaster sends a command to the channel.
This will tell the bots to perform an action.
Borrowed from Brent ByungHoon Kang, GMU
Botnet with Central Control Server
The IRC server sends (broadcasts) the message to bots
listening on the channel.
Borrowed from Brent ByungHoon Kang, GMU
Botnet with Central Control Server
The bots perform the command.
In this example: attacking / scanning CNN.COM.
Borrowed from Brent ByungHoon Kang, GMU
Botnet Sophistication Fueled by
Underground Economy
• Unfortunately, the detection, analysis and
mitigation of botnets has proven to be quite
challenging
• Supported by a thriving underground economy
– Professional quality sophistication in creating
malware codes
– Highly adaptive to existing mitigation efforts such
as taking down of central control server.
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Borrowed from Brent ByungHoon Kang, GMU
Emerging Decentralized
Peer to Peer Multi-layered Botnets
• Traditional botnet communication
– Central IRC server for Command & Control (C&C)
– Single point of mitigation:
• C&C Server can be taken down or blacklisted
• Botnets with peer to peer C&C
– No single point of failure.
– E.g., Waldedac, Storm, and Nugache
• Multi-layered architecture to obfuscate and
hide control servers in upper tiers.
Borrowed from Brent ByungHoon Kang, GMU
Expected Use of DHT P2P Network
Publish and Search
Botmaster publishes
commands under the
key.
 Bots are searching for
this key periodically
 Bots download the
commands
=>Asynchronous C&C

Borrowed from Brent ByungHoon Kang, GMU
Multi-Layered Command and Control
Architecture Through P2P
Each Supernode (server)
publishes its location (IP
address) under the key 1 and
key 2
 Subcontrollers search for key
1
 Subnodes (workers) search
for key 2
to open connection
to the Supernodes
=> Synchronous C&C

Borrowed from Brent ByungHoon Kang, GMU
Current Approaches to Botnet
• Virus Scanner at Local Host
– Polymorphic binaries against signature scanning
– Not installed even though it is almost free
– Rootkit
• Network Intrusion Detection Systems
– Keeping states for network flows
– Deep packet inspection is expensive
– Deployed at LAN, and not scalable to ISP-level
– Requires Well-Trained Net-Security SysAdmin
Borrowed from Brent ByungHoon Kang, GMU
Conficker infections are still increasing after
one year!!!
There are millions of computers on the Internet
that do not have virus scanner nor IDS
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Borrowed from Brent ByungHoon Kang, GMU
Botnet Enumeration Approach
• Used for spam blocking, firewall configuration, DNS
rewriting, and alerting sys-admins regarding local
infections.
• Fundamentally differs from existing Intrusion
Detection System (IDS) approaches
– IDS protects local hosts within its perimeter (LAN)
– An enumerator would identify both local as well as
remote infections
• Identifying remote infections is crucial
– There are numerous computers on the Internet that are
not under the protection of IDS-based systems.
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Borrowed from Brent ByungHoon Kang, GMU
How to Enumerate Botnet
• Need to know the method and protocols for
how a bot communicates with its peers
• Using sand-box technique
– Run bot binary in a controlled environment
– Network behaviors are captured/analyzed
• Investigating the binary code itself
– Reversing the binary into high level codes
– C&C Protocol knowledge and operation details
can be accurately obtained
Borrowed from Brent ByungHoon Kang, GMU
Simple Crawler Approach
• Given network protocol knowledge, crawlers:
1.
2.
3.
4.
5.
collect list of initial bootstrap peers into queue
choose a peer node from the queue
send to the node look-up or get-peer requests
add newly discovered peers to the queue
repeat 2-5 until no more peer to be contacted
• Can’t enumerate a node behind NAT/Firewall
• Would miss bot-infected hosts at
home/office!
Borrowed from Brent ByungHoon Kang, GMU
Passive P2P Monitor (PPM)
• Given P2P protocol knowledge that bot uses
• A collection of “routing-only” nodes that
– Act as peer in the P2P network, but
– Controlled by us, the defender
• PPM nodes can observe the traffic from the
peer infected hosts
• PPM node can be contacted by the infected
hosts behind NAT/Firewall
Borrowed from Brent ByungHoon Kang, GMU
Crawler and Passive P2P Monitor (PPM)
PPM
Crawler
PPM
PPM
Borrowed from Brent ByungHoon Kang, GMU
Crawler vs. PPM: # of IPs found
Botnet Enumeration Challenges
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DHCP
NATs
Non-uniform bot distribution
Churn
Most estimates put size of largest botnets at
tens of millions of bots
– Actual size may be much smaller if we account for
all of the above
Fast Flux
• Botnets use a lot of newly-created domains
for phishing and malware delivery
• Fast flux: changing name-to-IP mapping very
quickly, using various IPs to thwart defense
attempts to bring down botnet
• Single-flux: changing name-to-IP mapping for
individual machines, e.g., a Web server
• Double-flux: changing name-to-IP mapping for
DNS nameserver too
• Proxies on compromised nodes fetch content
from backend servers
Fast Flux
• Advantages for the attacker:
– Simplicity: only one back end server is needed to
deliver content
– Layers of protection through disposable proxy
nodes
– Very resilient to attempts for takedown
Fast Flux Detection
• Look for domain names where mapping to IP
changes often
– May be due to load balancing
– May have other (non-botnet) cause, e.g., adult
content delivery
– Easy to fabricate domain names
• Look for DNS records with short-lived domain
names, with lots of A records, lots of NS records
and diverse IP addresses (wrt AS and network
access type)
• Look for proxy nodes by poking them
Poking Botnets is Dangerous
• They have been known to fight back
– DDoS IPs that poke them (even if low workers are
scanned)
• They have been known to fabricate data for
honeynets
– Honeynet is a network of computers that sits in
otherwise unused (dark) address space and is
meant to be compromised by attackers
Privacy
What is Privacy?
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Privacy is about PII
It is primarily a policy issue
Privacy is an issue of user education
• Make sure users are aware of the potential use of the
information they provide
Give the user control
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Privacy is a security issue
• Security is needed to implement the policy
Security v. Privacy
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Sometimes conflicting
• Many security technologies depend on
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identification
Many approaches to privacy depend on hiding
one’s identity
Sometimes supportive
• Privacy depends on protecting PII (personally
•
identifiable information)
Poor security makes it more difficult to protect
such information
Debate on Attribution
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How much low level information should
be kept to help track down cyber
attacks
• Such information can be used to breach
privacy assurances
• How long can such data be kept
Privacy is Not the Only Concern
• Business Concerns
• Disclosing Information we think of as privacyrelated can divulge business plans
• Mergers
• Product plans
• Investigations
• Some “private” information is used for
authentication
• SSN
• Credit card numbers
Aggregation of Data
• Consider whether it is safe to release
information in aggregate
• Such information is presumably no longer
personally identifiable
• But given partial information, it is sometimes
possible to derive other information by
combining it with the aggregated data.
Anonymization of Data
• Consider whether it is safe to release
information that has been stripped of so
called personal identifiers
• Such information is presumably no longer
personally identifiable
• What is important is not just anonymity,
but linkability
• If I can link multiple queries, I might be
able to infer the identity of the person
issuing the query through one query, at
which point, all anonymity is lost
Traffic Analysis
• Even when specifics of communication
are hidden, the mere knowledge of
communication between parties
provides useful information to an
adversary
• E.g. pending mergers or acquisitions
• Relationships between entities
• Created visibility of the structure of an
organizations
• Allows some inference about interests
Information for Traffic Analysis
Lists of the web sites you visit
Email logs
Phone records
Perhaps you expose the linkages
through web sites like linked in
• Consider what information remains in
the clear when you design security
protocols
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Network Trace Sharing
• Researchers need network data
– To validate their solutions
– To mine and understand trends
• Sharing network data creates necessary
diversity
– Enables generalization of results
– Creates a lot of privacy concerns
– Very few public traffic trace archives
(CAIDA, WIDE, LBNL, ITA, PREDICT, CRAWDAD,
MIT DARPA)