Transcript PPT file - University of Central Florida

Session 1341: Case Studies of Security
Studies of Intrusion Traffic Patterns Using
OPNET
Mian Zhou, Sheau-Dong Lang
University of Central Florida
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
1341 Security
Outline
 Simulation of network intrusion scenarios.
 Testing a frequency-based intrusion detection
strategy.
 Studying the effects of transmission delays on our
detection strategy.
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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Our Approach to Intrusion Simulation
 Use OPNET to simulate intrusion scenarios by replaying
the network traffic.
 Traffic data sources.
• The publicly available datasets from MIT Lincoln lab.
• Self-generated attack traffic.
Attack tools: Nmap, Battle
Sniffer: Ethereal
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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Simulation using OPNET
Network domain
C code for a process node
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Node domain
Process domain
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Pre-processing Traffic Data
 Process the TCPDUMP data.
The packet inter-arrival times.
The traffic duration.
A list of the distinct IP addresses in the traffic source.
 Build a network model with the end nodes corresponding to
the extracted IP addresses.
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OPNET Model — Packet Generator
Packet format:
Drop the payload of original packets but
retain the IP header information including
IP address, port number, packet size, time
stamp, flags, etc.
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The attribute panel of the packet generator,
with scripted packet inter-arrival times
calculated from pre-processing the source data
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Two Sample Outputs from Simulation of
the ProcessTable attack
(a) Number of distinct port connections to a victim.
(b) Data traffic to Port 25 of the victim PC .
Data Source: 1999 Lincoln Week 5 outside data
DOS attack: ProcessTable
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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Frequency-based Intrusion Detection
Observation:
• Certain network attacks are executed by running pre-written scripts which
automate the process of connecting to various ports, sending packets with
fabricated payloads, etc.
 Frequency-based intrusion detection.
• Use Discrete Fourier Transform (DFT) to identify periodicity patterns.
 Where to find the periodicity patterns.
• The time series of packets’ inter-arrival times.
• The time series of packet arrival rates.
• The size distribution of packet payloads.
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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Overall Detection Strategy
Traffic
data
Parse
new connections
New connection
history
Variance
analysis
Average variance of packet
size for each connection
Generate the time-series data
Pass the trusty
Connections
Data sequence for
each connection
Data sequence for
multiple connections
Compare with a
threshold value
DFT
Local frequency
pattern
Global frequency
pattern
Report attacks
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Frequency Extraction by Discrete
Fourier Transform (DFT)
For a given data sequence s(n) where n  0 is a discrete value representing
the time, its DFT coefficients F(k) are defined as follows
N 1
F (k )   s(n)e  j 2kn / N
n 0
0  k  N –1, N is the length
of s(n)
Expanding the right-hand side yields
N 1
N 1
n 0
n 0
F (k )   s(n) cos(2kn / N )  j  s(n) sin(2kn / N )
Using the Fast Fourier Transformation (FFT) procedure, the frequency data
F(k) can be computed in O(N logN) time.
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Detection Results: the ProcessTable
Attack
1
2
4
5
3
6
Frequency patterns extracted by DFT on inter-arrival times of six connections.
Connections 2 and 4 show periodicity patterns. The traffic of connection 2 is the
ProcessTable attack; connection 4 is a Probe attack, which probes the target’s
ports ranging from 1794 to 2631.
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Detection Results: the Dictionary
Attack
1
2
3
4
5
6
Frequency patterns on inter-arrival times of six connections for the Dictionary
attack. Connection 2 shows the password guessing (dictionary) attack.
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Detection Results: the sshProcessTable
Attack
1
4
2
5
3
6
Frequency patterns of the rates of packet
arrivals of six connections for the
sshProccessTable attack. Connection 2 contains
the attack traffic.
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
1
2
4
5
3
6
Frequency patterns of inter-arrival times for six
connections, Connection 2 shows the attack
traffic.
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The Effect of Transmission Delays on
Frequency patterns
(a)
(b)
(c)
The spectrum (frequency patterns) of three time series data,
where the original data values  [0.002, 0.5].
(a)The spectrum of the original data series X(t).
(b)The spectrum of the X(t) + exp(0.5) (exponentially distributed
delay with mean value 0.5 seconds)
(c)The spectrum of the X(t) + exp(5).
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Transmission delay in LANs
A simple LAN, in which the web client
sends the traffic to three servers. We
collected the inter-arrival times of the traffic
to the main server.
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The profile configuration panel
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The Effect of Transmission Delays
(a)
(b)
Frequency patterns collected at the main server,
when other types of explicit traffic loads are added
to the web client traffic.
The inter-arrival times and frequency patterns
collected: (a) at the sender (the web client); (b) at
the receiver (the main server).
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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Transmission Delays in WANs
The packet delivery process of the custom traffic is
controlled by scripted packet time intervals.
A WAN, in which the site Dublin sends traffic to
site London through an Internet cloud. Other
types of traffic such as email and ftp are created
by the other 5 nodes and coexisted with the
custom traffic from Dublin.
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The configuration panel for the Internet cloud, where we
specify the statistical distribution of the packet latency
caused by traversing the Internet.
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The Effect of Transmission Delays in
WANs
Frequency patterns of the packet inter-arrival times
with different Internet transmission delays. The
distributions for transmission delay include constant,
uniform, and exponential.
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
Frequency patterns of the packet inter-arrival times with
exponentially distributed transmission delays. The
spectrum starts to deviate from the original as the mean
value increases.
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Conclusions
 Frequency-based intrusion detection
 Detects anomalous traffic behaviors (that contain periodicity patterns)
 Improves the effectiveness of signature-based intrusion detection systems when
combined with other simple statistical features of the traffic data.
 Needs measures to counter attacks with randomized script.
 limited to the attacks with relatively long duration and heavy load.
 Transmission delay on frequency patterns
 Frequency patterns will not be affected by near constant transmission delay.
 Frequency patterns persist in LANs.
 In WANs, further studies on packet latency required.
Copyright © 2003 OPNET Technologies, Inc. Confidential, not for distribution to third parties.
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