Transcript Motorola-visit-revis.. - Computer Science Division

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Network Intrusion
Detection and Mitigation
Yan Chen
Northwestern Lab for Internet and Security
Technology (LIST)
Department of Computer Science
Northwestern University
http://list.cs.northwestern.edu
Our Theme
• Internet is becoming a new infrastructure for
service delivery
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World wide web,
VoIP
Email
Interactive TV?
• Major challenges for Internet-scale services
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Scalability: 600M users, 35M Web sites, 2.1Tb/s
Security: viruses, worms, Trojan horses, etc.
Mobility: ubiquitous devices in phones, shoes, etc.
Agility: dynamic systems/network, congestions/failures
– Ossification: extremely hard to deploy new technology in
the core
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Battling Hackers is a Growth Industry!
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--Wall Street Journal (11/10/2004)
• The past decade has seen an explosion in the
concern for the security of information
• Internet attacks are increasing in frequency,
severity and sophistication
• Denial of service (DoS) attacks
– Cost $1.2 billion in 2000
– Thousands of attacks per week in 2001
– Yahoo, Amazon, eBay, Microsoft, White House, etc.,
attacked
Battling Hackers is a Growth Industry
(cont’d)
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• Virus and worms faster and powerful
– Melissa, Nimda, Code Red, Code Red II, Slammer …
– Cause over $28 billion in economic losses in 2003, growing
to over $75 billion in economic losses by 2007.
– Code Red (2001): 13 hours infected >360K machines $2.4 billion loss
– Slammer (2003): 10 minutes infected > 75K machines - $1
billion loss
• Spywares are ubiquitous
– 80% of Internet computers have spywares installed
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The Spread of Sapphire/Slammer Worms
How can it affect cell phones?
• Cabir worm can infect a cell phone
– Infect phones running Symbian OS
– Started in Philippines at the end of 2004, surfaced
in Asia, Latin America, Europe, and recently in US
– Posing as a security management utility
– Once infected, propagate itself to other phones via
Bluetooth wireless connections
– Symbian officials said security was a high priority of
the latest software, Symbian OS Version 9.
• With ubiquitous Internet connections, more
severe viruses/worms for mobile devices will
happen soon …
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The Current Internet: Connectivity
Cable
and Processing
Modem
Premisesbased
Access
Networks
Core Networks
WLAN
Transit Net
WLAN
WLAN
Operatorbased
Cell
Cell
Cell
Regional
LAN
LAN
Transit Net
Premisesbased
Analog
Public
Peering
Voice
LAN
Private
Peering
NAP
Data
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Transit Net
H.323
RAS
H.323
PSTN
DSLAM
Data
Voice
Wireline
Regional
Current Intrusion Detection Systems (IDS)
• Mostly host-based and not scalable to high-speed
networks
– Slammer worm infected 75,000 machines in <10 mins
– Host-based schemes inefficient and user dependent
» Have to install IDS on all user machines !
• Mostly signature-based
– Cannot recognize unknown anomalies/intrusions
– New viruses/worms, polymorphism
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Current Intrusion Detection Systems (II)
• Statistical detection
– Hard to adapt to traffic pattern changes
– Unscalable for flow-level detection
» IDS vulnerable to DoS attacks
» WiMAX, up to 134Mbps, 10 min traffic may take 4GB memory
– Overall traffic based: inaccurate, high false positives
• Cannot differentiate malicious events with
unintentional anomalies
– Anomalies can be caused by network element faults
– E.g., router misconfiguration, signal interference of
wireless network, etc.
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Adaptive Intrusion Detection System
for Wireless Networks (WAIDM)
• Online traffic recording and analysis for highspeed WiMAX networks
– Leverage sketches for data streaming computation
– Record millions of flows (GB traffic) in a few Kilobytes
• Online adaptive flow-level anomaly/intrusion
detection and mitigation
– Leverage statistical learning theory (SLT) adaptively
learn the traffic pattern changes
– Use statistics from MIB of Access Point to understand
the wireless network status
» E.g., busy vs. idle wireless networks, with different level of
interferences, etc.
– Unsupervised learning without knowing ground truth
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WAIDM Systems (II)
• Integrated approach for false positive reduction
– 802.16 Signature-based detection
– WiMAX network element fault diagnostics
– Traffic signature matching of emerging applications
• Hardware speedup for real-time detection
– Collaborated with Gokhan Memik (ECE of NU)
– Try various hardware platforms: FPGAs, network
processors
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WAIDM Deployment
User
s
802.16
BS
802.16
BS
802.16
BS
User
s
Internet
Users
Inter
net
scan
port WAIDM
system
• Attached to a switch connecting BS as a black box
• Enable the early detection and mitigation of global
scale attacks
• Highly ranked as “powerful and flexible" by the
DARPA research agenda
Switch/
BS controller
Switch/
BS controller
802.16
BS
Users
(a)
Original configuration
(b) WAIDM
deployed
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GRAID Sensor
Architecture
Normal flows
Reversible
k-ary sketch
monitoring
Streaming
packet
data
Filtering
Remote
aggregated
sketch
records
Sent out for
aggregation
Local
sketch
records
Sketch based
statistical anomaly
detection (SSAD)
Keys of suspicious flows
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Part I
Sketchbased
monitoring
& detection
Keys of normal flows
Statistical
detection
Suspicious flows
Per-flow
monitoring
Signature
-based
detection
Network fault
detection
Traffic
profile
checking
Data path
Control path
Modules on
the critical
path
Part II
Per-flow
monitoring
& detection
Intrusion or
anomaly alarms
to fusion centers
Modules on
the non-critical
path
Scalable Traffic Monitoring and
Analysis - Challenge
• Potentially tens of millions of time series !
– Need to work at very low aggregation level (e.g., IP
level)
– Each access point (AP) can have 200 Mbps – a
collection of 10-100 APs can easily go up to 2-20 Gbps
– The Moore’s Law on traffic growth … 
• Per-flow analysis is too slow or too expensive
– Want to work in near real time
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Sketch-based Change Detection
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(ACM SIGCOMM IMC 2003, 2004)
(k,u) …
Sketch
module
Sketches
Forecast
module(s)
Error
Sketch
Change Alarms
detection
module
•
• Input stream: (key, update)
Summarize input stream using sketches
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Build forecast models on top of sketches
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Report flows with large forecast errors
Evaluation of Reversible K-ary Sketch
• Evaluated with tier-1 ISP trace and NU traces
• Scalable
– Can handle tens of millions of time series
• Accurate
– Provable probabilistic accuracy guarantees
– Even more accurate on real Internet traces
• Efficient
– For the worst case traffic, all 40 byte packets:
» 16 Gbps on a single FPGA board
» 526 Mbps on a Pentium-IV 2.4GHz PC
– Only less than 3MB memory used
• Patent filed
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GRAID Sensor
Architecture
Normal flows
Reversible
k-ary sketch
monitoring
Streaming
packet
data
Filtering
Remote
aggregated
sketch
records
Sent out for
aggregation
Local
sketch
records
Sketch based
statistical anomaly
detection (SSAD)
Keys of suspicious flows
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Part I
Sketchbased
monitoring
& detection
Keys of normal flows
Statistical
detection
Suspicious flows
Per-flow
monitoring
Signature
-based
detection
Network fault
detection
Traffic
profile
checking
Data path
Control path
Modules on
the critical
path
Part II
Per-flow
monitoring
& detection
Intrusion or
anomaly alarms
to fusion centers
Modules on
the non-critical
path
Current IDS Insufficient for
Wireless Networks
• Most existing IDS signature-based
– Especially for wireless networks
– Detect denial-of-service attacks caused by the WEP
authentication vulnerability, e.g., Airespace
• Current statistical IDS has manually set
parameters
– Cannot adapt to the traffic pattern changes
• However, wireless networks often have transient
connections
– Hard to differentiate collisions, interference, and
attacks
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Statistical Anomaly/Intrusion Detection
and Mitigation for Wireless Networks
• Use statistics from MIB of AP to understand
the current wireless network status
– Interference Detection MIB Group
» Retry count, FCS err count, Failed count …
– Intrusion Detection MIB Group
» Duplicate count, Authentication failure count, EAP
negotiation failure count, Abnormal termination percentage
…
– DoS Detection MIB Group
» Auth flood to BS, De-Auth flood to SS
• Automatically adapt to different learned
profiles on observing status changes
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Preliminary Algorithm
Collect MIBs
Process Interference
Collision MIB Group
Inter
H
Interference
L
Process Intrusion
Detection MIB Group
Process DoS MIB
Group
Intru
DoS
H
H
Intrusion
DoS Attack
Intrusion Detection
and Mitigation
P
O
R
T
N
U
M
B
E
R
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V
E
R
T
I
C
A
L
BLOCK
HORIZONTAL
SOURCE IP
Attacks detected
Mitigation
Denial of Service (DoS), SYN defender, SYN proxy, or SYN
e.g., TCP SYN flooding
cookie for victim
Port Scan and worms
Ingress filtering with attacker IP
Vertical port scan
Quarantine the victim machine
Horizontal port scan
Monitor traffic with the same port
# for compromised machine
Spywares
Warn the end users being spied
GRAID Sensor
Architecture
Normal flows
Remote
aggregated
sketch
records
Sent out for
aggregation
Reversible
k-ary sketch
monitoring
Streaming
packet
data
Filtering
Local
sketch
records
Sketch based
statistical anomaly
detection (SSAD)
Keys of suspicious flows
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Part I
Sketchbased
monitoring
& detection
Keys of normal flows
SIGCOMM04
Suspicious flows
Per-flow
monitoring
Signature
-based
detection
Statistical
detection
Network fault
detection
Traffic
profile
checking
Data path
Control path
Modules on
the critical
path
Part II
Per-flow
monitoring
& detection
Intrusion or
anomaly alarms
to fusion centers
Modules on
the non-critical
path
Research methodology
Combination of theory, synthetic/real trace
driven simulation, and real-world implementation
and deployment
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Potential Collaborative Research
Areas with Motorola
• Wireless virus/worm detection
• Spyware detection
• Both by operators at infrastructure level (e.g.,
access point)
• Intrusion detection and mitigation for cellular
network infrastructure
• Automatic attack responding and survival for
Motorola infrastructure products
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Thank You!
More Questions?