Download Advance in Intrusion Detection Techniques

Advance in Intrusion Detection
Techniques
Associate Prof. Fang Xianjin(方贤进)
Computer Science & Engineering School of AUST
Outline
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Context of computer security problem
Brief summaries of computer security system
What is IDS?
Architecture and Classification of IDS
Intrusion detection techniques
My current research works
Questions and answer
Context of computer security problem
2006 Annual Report by CNCERT/CC
Context of computer security problem
2006 Annual Report by CNCERT/CC
Context of computer security problem
From 19th June to 31st December in 2006,
18,912 sample had been captured by
CNCERT/CC’s honeynet.
Brief summaries of computer security
system
Multi-layer defense:
 First layer is static access mechanisms,
such as passwords and file permissions.
Disadvantages:
Limited to provide comprehensive security;
— Overly restrictive for legitimate users of
computer system;
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Brief summaries of computer security
system
Multi-layer defense:
 second layer is cryptography, which is used
for providing secure channels and host
authentication
 Another layer is firewall, which filters out
undesirable network traffic in a network
system.
Brief summaries of computer security
system
Multi-layer defense:
 The latest layer of defense is provided by
dynamic protection systems that can
detect and prevent intrusion, namely, is
known as Intrusion Detection System(IDS).
What is IDS?
Mathematical description for IDS:
U：universe set,
S: normal/legitimate/acceptable pattern set (self set ),
N: anomalous/illegitimate/unacceptable pattern set (nonself
set),
S∪N=U, S∩N=Ф
IDS=（f, M), f is a nonlinear classification function, M is
detection range of detection system,
f: U*×U→{normal, anomalous}
 normal , s  M
f (M , s)  
anomalous, otherwise
M
Self
False
positives
U
Nonself
False
negatives
IDS’ Architecture and Classification for
IDS
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Architecture of IDS
Analysis console
Sensor
Alert
Analyzer
Response/control
Knowledge base
Policy/control info
IDS’ Architecture and Classification for
IDS
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Classification of IDS
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On the basis of detection techniques:
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Misuse detection (signature-based): high detection rate
high false negative rate, low false positive rate
Anomaly detection: low detection rate, high false
positive rate
On the basis of data input
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HIDS
NIDS
Hybrid IDS
Intrusion Detection Techniques
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Misuse detection
Method based on Expert system (P-BEST)
Firstly, according to experiment, creating knowledge
base (attack signature base)
Secondly, updating knowledge by using learning
and adaptive capacity
For example:
EMERALD, eXpert-BSM (SRI-international
developed)
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Intrusion Detection Techniques
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Misuse detection
Method based on TCP/IP Protocol Analysis
Decoding each packet from all kinds of layers of TCP/IP
architecture
For example:
When the value of SYN and FIN of a TCP packet is “1”, we
can think that a port-scanning attack occurred.
Features:
High performance, more accurate, anti-evade attack, low
resource requirement
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Intrusion Detection Techniques
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Misuse detection
Method based on Pattern-matching
For example:
SNORT IDS (Open source code software,
Sourcefire Company)
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Intrusion Detection Techniques
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Anomaly detection
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Statistic and Analysis methodology
Creating profile database of normal behavior by
analyzing a lot of system data;
 Adaptively learning normal pattern database;
 Comparing auditing data on system with normal
behavior profile, if comparison result exceed the
threshold, an attack event may happened.
Conventional statistic models:
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Average value and standard deviation model
– Markovian model
– Time/session/connection sequence model
Intrusion Detection Techniques
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Anomaly detection
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Method based on Artificial Neural Network
 Creating
signature profile of system
by learning a lot of samples in training
set
 Predicting the relationship between
input data and output data
 Comparison with threshold
Intrusion Detection Techniques
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Anomaly detection
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Data mining approaches for intrusion detection
The key ideas are to use data mining
techniques to discover consistent and useful
patterns of system features that describe
program and user behavior, and use the set
of relevant system features to compute
(inductively learned) classifiers that can
recognize anomalies and known intrusions.
Intrusion Detection Techniques
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Anomaly detection
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Agent-based distributed intrusion detection
framework
Intrusion Detection Techniques
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Anomaly detection
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Artificial immune model for intrusion detection system
Some terms in Natural Immunity System(NIS):
 T-cell, B-cell
 Antigen, epitope, receptor
 Antibody, paratope
 Affinity
 Immune recognition
 Immune tolerance
 Immune memory
 Autoimmune response
 vaccnine
Intrusion Detection Techniques
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Anomaly detection
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Artificial immune model for intrusion detection
system
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Self set (learning by using training set)
generating randomly Detector set
Negative selection algorithm (non-self set)
Anomaly detection
Clonal selection algorithm
Dynamic Clonal selection algorithm
Genetic algorithm based on immunity
r-contiguous match algorithm
Intrusion Detection Techniques
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Anomaly detection
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Artificial immune model for intrusion detection
system
LISYS Model is as follows:
Intrusion Detection Techniques
Intrusion Detection Techniques
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Anomaly detection
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Artificial immune model for intrusion detection
system
The following is Kim’s conceptual model for
intrusion detection:
Intrusion Detection Techniques
My current research works
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Topic: research on immune model for intrusion
detection system
Design An Artificial Immune model with Vaccine
operator for Network Intrusion Detection
Study Immune Evolutionary Algorithm of
detectors population.
Implement intrusion detection on network layer,
transport layer and application layer
Analyze detection rate, false positive rate
detector cover, detector hole in theory
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Thank you!
Questions?