Transcript Session 10 2012

What is Firewall?

Design goals
– All traffic from inside to outside and vice versa must pass through
the firewall
– A single checking point that keeps unauthorized traffic (i.e., worm)
out of the protected network
Internet
How it functions?

Technique
– Control access via security policy

Types
– Packet filter router
– Application-level gateway
– Stateful filter vs. stateless filter
– Personal firewall
Packet-Filtering Router

Packet-Filtering Router
–
–
–

Applies a set of rules to each incoming IP packet
Decides forwarding or discarding the packet
Only examine the header, do not “see inside” a packet
Pros & Cons
–
–
Simple
No application-specific protection
HTTP
Telnet
Internet
129.10.10.1
209.10.10.1
Packet-Filtering Router

Filtering rules
– Src/dest IP address; src/dest port; protocol field; etc.
– Default
 Discard vs. forward
action
Internal host
address
Internal port
External host
address
External port
function
block
*
*
192.10.*.*
*
Block all packets
from 192.10.*.*
action
Internal host
address
Internal port
External host
address
External port
function
allow
129.10.10.3
25
*
*
Allow inbound mail
to 129.10.10.3
Packet-Filtering Router

The dangerous services
–
–
–
–
–
finger (port 79)
telnet (port 23)
ftp (port 21)
rlogin (port 513)
ICMP
finger
Telnet
Internet
ftp
rlogin
Stateful Inspection Firewall

Stateful Inspection Firewall
– Maintains state information from one packet to another in the input stream
– Tightens up the rules for TCP traffic
Source address
Source port
Destination address
Destination port
State
192.10.10.16
3321
216.10.18.123
80
established
Application-level gateway

Application level proxy/gateway
–
–
–
–

Relay of application traffic
Run pseudoapplications
Looks to the inside as if it is the outside connection
Looks to the outside as if it is the inside
Pros & Cons
–
–
Processing overhead
Diverse functionality
HTTP
Internet
SMTP
FTP
TELNET
Deployment

Considerations
– Performance
– Security of firewall itself
– Runs on minimized OS
 non-firewall functions should not be done on the same machine

Network Topology
HTTP
SMTP
Internet
FTP
TELNET
Packet filter
Application gateway
Personal Firewall

Personal Firewall
– An application that runs on a personal computer to block unwanted
traffic

Product
– ZoneAlarm
 www.zonelabs.com
– BlackICE Defender
 blackice.iss.net
– Tiny Personal Firewall
 www.tinysoftware.com
– Norton Personal Firewall
 www.symantec.com
– Windows
Benefit & Limitation

Benefit
– Provides a location for monitoring security-
related events
– Provides a platform for security-related
functions: NAT, IPSec

Limitations
–
–
–
–
Attacks that bypass firewall
Internal threats
Performance
Usability vs. security
Intrusion Detection System
Background

What is Intrusion
– An intrusion can be defined as any set of actions that attempt to
compromise the integrity, confidentiality or availability of a
resource. [Heady R. 1990]

Three classes of intruder



Masquerader – illegitimate user penetrates the system using a
legitimate user’s account
Misfeasor – legitimate user misuses his/her privileges, accessing
resources that is not authorized
Clandestine user -- privileged
suppress audit control
user uses supervisory control to
Background

What is Intrusion Detection System
– An Intrusion Detection System (IDS) must identify, preferably in
real time, unauthorized use, misuse and abuse of computer systems
– It is a reactive, rather than proactive, form of system defense.

Classification
– Misuse intrusion detection vs. Anomaly intrusion detection
 Misuse intrusion detection -- detect attacks on known weak points of
a system.
 Anomaly intrusion detection -- detect by building up a profile of the
system being monitored and detecting significant deviations from this
profile.
– Host-based detection vs. Network-based detection
History






Conventional approach to system security: Authentication, Access
control and Authorization.
In 1980, James Anderson first proposed that audit trails should be used
to monitor threats.
In 1987, Dorothy Denning presented an abstract model of an Intrusion
Detection System.
In 1988, IDES (Intrusion Detection Expert System) – host-based IDS is
developed.
In 1990, Network Security Monitor is developed – network-based IDS
is developed.
In 1994, Mark Crosbie and Gene Spafford suggested the use of
autonomous agents in order to improve the scalability, maintainability,
efficiency and fault tolerance of an IDS.
Structure of IDS
Alerting
System  Data collection
Agent Manager
Classifier
Agent Agent Agent
I
II
III
Agent
IV
Data reduction
Data
collection
System
Call
Audit Files
system
 Data reduction
system
 Classifier
 Alerting system
Data Collection and Reduction

Data source
– Audit files
 system audit files: messages,xferlog,syslog,sulog, .bash_ history...
 application audit files: Web server log files,….
– System Call

Audit record [Denning 87]
– Subject, Action, Object, Exception, Resource usage, Time stamp
– User operation  elementary actions

COPY GAME.EXE to /usr/GAME.EXE
Smith
exec
COPY.EXE
0
CPU = 00002
1058721678
Smith
read
GAME.EXE
0
RECORDS = 1
1058721679
Smith
exec
COPY.EXE
Write-viol
RECORDS = 0
1058721680
Misuse intrusion detection

Misuse intrusion detection
– Use patterns of well-known attacks or weak
spots of the system to match and identify
intrusions
– Perform pattern matching
– Used in the environment where a rule can be
recognized.

Example

Misuse Intrusion Detection (Purdue) using Patten
Matching
Port scan
Password guessing
Anomaly Intrusion Detection

Anomaly Intrusion Detection
– Establish normal usage profiles
– Observe deviation from the normal usage patterns
– Example profiles: loginfrequency, locationfrequency, UseofCPU,UseofIO,
ExecutionFrequencyFileReadFails、FileWriteFails

Metrics
–
–
–
–

Mean and standard deviation
Multivariate
Markov process
Time Series
Approaches
– Data Mining Approaches
– Neural Networks
– Colored-petri-net
Distributed IDS
Honey Pot
• Honeypots are closely monitored network decoys
• Distract adversaries from more valuable machines on a network
• Provide early warning about new attack and exploitation trends
• Example
• Honeypot can simulate one or more network services that you
designate on your computer's ports.
Product

http://www.snort.org/
–

http://www.dshield.org/
–
–

A distributed intrusion detection system, or a distributed firewall system.  an attempt to
collect data about cracker activity from all over the internet. This data will be cataloged
and summarized. It can be used to discover trends in activity and prepare better firewall
rules.
Right now, the system is tailored to simple packet filters. As firewall systems that produce
easy to parse packet filter logs are now available for most operating systems, this data can
be submitted and used without much effort.
NFR Security Inc.
–

Snort® is an open source network intrusion prevention and detection system utilizing a
rule-driven language, which combines the benefits of signature, protocol and anomaly
based inspection methods.
NFR Security provides a comprehensive, integrated intrusion detection system that protects
networks and hosts from known/unknown attacks, misuse, abuse and anomalies.
http://www.nfr.com
Real Secure by ISS
http://www.iss.net/products_services/enterprise_protection/
Outline




Introduction
A Frame for Intrusion Detection System
Intrusion Detection Techniques
Ideas for Improving Intrusion Detection
What is the Intrusion Detection

Intrusions are the activities that violate the
security policy of system.
 Intrusion Detection is the process used to
identify intrusions.
Types of Intrusion Detection System(1)
Based on the sources of the audit information
used by each IDS, the IDSs may be classified
into
– Host-base IDSs
– Distributed IDSs
– Network-based IDSs
Types of Intrusion Detection System(2)

Host-based IDSs
– Get audit data from host audit trails.
– Detect attacks against a single host

Distributed IDSs
– Gather audit data from multiple host and possibly the
network that connects the hosts
– Detect attacks involving multiple hosts

Network-Based IDSs
– Use network traffic as the audit data source, relieving
the burden on the hosts that usually provide normal
computing services
– Detect attacks from network.
Intrusion Detection
Techniques

Misuse detection
– Catch the intrusions in terms of the
characteristics of known attacks or system
vulnerabilities.

Anomaly detection
– Detect any action that significantly deviates
from the normal behavior.
Misuse Detection

Based on known attack actions.
 Feature extract from known intrusions
 Integrate the Human knowledge.
 The rules are pre-defined
 Disadvantage:
– Cannot detect novel or unknown attacks
Misuse Detection Methods & System
Method
Rule-based Languages
State Transition Analysis
Colored Petri Automata
Expert System
Case Based reasoning
Anomaly Detection

Based on the normal behavior of a subject.
Sometime assume the training audit data
does not include intrusion data.
 Any action that significantly deviates from
the normal behavior is considered intrusion.
Anomaly Detection Methods & System
Method
Statistical method
Machine Learning techniques




Time-Based inductive Machine
Instance Based Learning
Neural Network
…
Data mining approaches
Anomaly Detection Disadvantages

Based on audit data collected over a period
of normal operation.
– When a noise(intrusion) data in the training
data, it will make a mis-classification.

How to decide the features to be used. The
features are usually decided by domain
experts. It may be not completely.
Misuse Detection vs. Anomaly Detection
Advantage
Disadvantage
Misuse
Detection
Accurately and
generate much
fewer false alarm
Cannot detect
novel or unknown
attacks
Anomaly
Detection
Is able to detect
unknown attacks
based on audit
High false-alarm
and limited by
training data.
The Frame for Intrusion
Detection
Intrusion Detection Approaches
1.
2.
3.
Define and extract the features of behavior
in system
Define and extract the Rules of Intrusion
Apply the rules to detect the intrusion
Audit Data
3
Training
Audit Data
1
2
Features
3
Rules
Pattern matching
or Classification
Thinking about The Intrusion
Detection System


Intrusion Detection system is a pattern
discover and pattern recognition system.
The Pattern (Rule) is the most important
part in the Intrusion Detection System
–
–
–
Pattern(Rule) Expression
Pattern(Rule) Discover
Pattern Matching & Pattern Recognition.
Machine
Learning &
Data
mining &
Statistics
methods
Traning
Audit
Data
Feature
Extraction
Training
Data &
Knowled
ge
Pattern
Extraction
Expert
Knowledge
& Rule
collection
& Rule
abstraction
Pattern &
Decision
Rule
Pattern
Matching
Alarms
Intrusion
Detection
System
Discriminate
function
Pass
Pattern
Recognition
Real-Time
Aduit data
Rule Discover Method

Expert System
 Measure Based method
– Statistical method
– Information-Theoretic Measures
– Outlier analysis

Discovery Association Rules
 Classification
 Cluster
Pattern Matching & Pattern
Recognition Methods





Pattern Matching
State Transition & Automata Analysis
Case Based reasoning
Expert System
Measure Based method
– Statistical method
– Information-Theoretic Measures
– Outlier analysis

Association Pattern
 Machine Learning method
Intrusion Detection Techniques
Intrusion Detection Techniques

Pattern Matching
 Measure Based method
 Data Mining method
 Machine Learning Method
Association Pattern Discover

Goal is to derive multi-feature (attribute)
correlations from a set of records.
 An expression of an association pattern:

The Pattern Discover Algorithm:
Apriori Algorithm
2. FP(frequent pattern)-Tree
1.
Association Pattern Detecting

Statistics Approaches
– Constructing temporal statistical features from
discovered pattern.
– Using measure-based method to detect intrusion
Machine Learning Method

Time-Based Inductive Machine
– Like Bayes Network, use the probability and a
direct graph to predict the next event

Instance Based Learning
– Define a distance to measure the similarity
between feature vectors

Neural Network
Classification

This is supervised learning. The class will
be predetermined in training phase.
 Define the character of classes in training
phase.
 A common approach in pattern recognition
system
Clustering

This is unsupervised learning. There are not
predetermined classes in data.
 Given a set of measurement, the aim is that
establishes the class or group in the data. It
will output the character of each class or
group.
 In the detection phase, this method will get
more time cost (O(n2)). I suggest this
method only use in pattern discover phase
Association Pattern Detecting

Using the pattern matching algorithm to
match the pattern in sequent data for
detecting intrusion. No necessary to construct
the measure.
 But its time cost depends on the number of
association patterns.
 Constructs a pattern tree to improve the
pattern matching time cost to linear time
Discover Pattern from Rules

The existing rules are the knowledge from experts
knowledge or other system.
 The different methods will measure different
aspects of intrusions.
 Combine these rules may find other new patterns of
unknown attack.
 For example:
– Snort has a set of rule which come from different people.
The rules may have different aspects of intrusions.
– We can use the data mining or machine learning method
to discover the pattern from these rule.
Machine
Learning &
Data
mining &
Statistics
methods
Traning
Audit
Data
Feature
Extraction
Training
Data &
Knowled
ge
Pattern
Extraction
Expert
Knowledge
& Rule
collection
& Rule
abstraction
Pattern &
Decision
Rule
Pattern
Matching
Alarms
Intrusion
Detection
System
Discriminate
function
Pass
Pattern
Recognition
Real-Time
Aduit data
Penetration Testing

1. Define target and requirements.
 2. Obtain a trusted agent.
 3. Prepare test plan.
 4. Obtain management signoff.
 5. Confirm target addresses.
 6. Port scanning
Penetration Testing
7. Enumeration of web interfaces.
8. Initial vulnerability assessment using port
scanner.
9. Verification scanned results.
10. Exploit vulnerabilities.
11. Password cracking.
12. Profile the target system.
Penetration Testing
13. Try to find valuable info in hidden fields
of html, xml, forms, applets and
dynamically generated pages.
14. Try to attack application servers.
15. Glean info from banners, welcome
messages and help screens.
Penetration Testing
16. Glean info from cookies and session IDs.
17. Try to determine login access controls.
18. Try to spoof IDs and replay passwords.
19. Try to decompile code.
20. Try to view the password rules.
21. Locate and try to attack the DNS.