Advance in Intrusion Detection Techniques

Associate Prof. Fang Xianjin( 方贤进 )

Computer Science & Engineering School of AUST

Outline

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;

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 d

ynamic 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}

otherwiseanomalous

MsnormalsMf

,

,),(

NonselfSelf

False positives

False negatives

UM

IDS’ Architecture and Classification for IDS

Architecture of IDS

Sensor Analyzer

Knowledge base

Response/control

Policy/control info

AlertAnalysis console

IDS’ Architecture and Classification for IDS

Classification of IDS– On the basis of detection techniques:

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 HIDS NIDS Hybrid IDS

Intrusion Detection Techniques

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 an

d adaptive capacityFor example:EMERALD, eXpert-BSM (SRI-international develop

ed)

Intrusion Detection Techniques

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

Intrusion Detection Techniques

Misuse detection– Method based on Pattern-matching

For example:

SNORT IDS (Open source code software, Sourcefire Company)

Intrusion Detection Techniques

Anomaly detection– 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 behavio

r profile, if comparison result exceed the threshold, an attack event may happened.

Conventional statistic models:– Average value and standard deviation model– Markovian model– Time/session/connection sequence model

Intrusion Detection Techniques

Anomaly detection– 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

Anomaly detection– 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

Anomaly detection– Agent-based distributed intrusion detection

framework

Intrusion Detection Techniques

Anomaly detection– 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

Anomaly detection– Artificial immune model for intrusion detection sys

tem 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

Anomaly detection– Artificial immune model for intrusion detection

system

LISYS Model is as follows:

Intrusion Detection Techniques

Intrusion Detection Techniques

Anomaly detection– Artificial immune model for intrusion detection

system

The following is Kim’s conceptual model for intrusion detection:

Intrusion Detection Techniques

My current research works

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?