Data Mining

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Data Mining Intrusion Detection

• Shan Bai
• Instructor Dr. Yingshu Li
• CSC 8712 ,Spring 08

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Outline

• Intrusion Detection
• Data Mining
• Data Mining in Intrusion Detection
• Reference

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What is an intrusion?

• An intrusion can be defined as any set of
  actions that attempt to compromise the
• Integrity
• confidentiality, or
• availability
• of a resource.

Incidents Reported to Computer Emergency Response
Team/Coordination Center
Spread of SQL Slammer worm 10 minutes after its
deployment
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Intrusion Examples

• Trojan horse /worm
• Address spoofing
• a malicious user uses a fake IP address to send
  malicious packets to a target.
• Many others

• DOS
• denial-of-service
• R2L
• unauthorized access from a remote machine, e.g.
  guessing password
• U2R
• unauthorized access to local super user (root)
  privileges, e.g., various buffer overflow''
  attacks
• Probing
• surveillance and other probing, e.g., port
  scanning.

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Intrusion Detection System (IDS)

• Intrusion Detection System
• combination of software and hardware that
  attempts to perform intrusion detection raises
  the alarm when possible intrusion happens.

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IDS Categories

• Intrusion detection systems are split into two
  groups
• Anomaly detection systems
• Identify malicious traffic based on deviations
  from established normal network.
• Misuse detection systems
• Identify intrusions based on a known pattern
  (signatures) for the malicious activity.

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Anomaly Detection
probable intrusion
activity measures
baseline the normal traffic and then look for
things that are out of the norm
Relatively high false positive rate -
anomalies can just be new normal activities.
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Misuse Detection
Example if (src_ip dst_ip) then land attack
look for known indicators ICMP Scans, port scans,
connection attempts CPU, RAM I/O Utilization,
File system activity, modification of system
files, permission modifications
Cant detect new attacks
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• Goal of Intrusion Detection Systems (IDS)
• To detect an intrusion as it happens and be able
  to respond to it.
• False positives
• A false positive is a situation where something
  abnormal (as defined by the IDS) happens, but it
  is not an intrusion.
• Too many false positives
• User will quit monitoring IDS because of noise.
• False negatives
• A false negative is a situation where an
  intrusion is really happening, but IDS doesn't
  catch it.

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Outline

• Intrusion Detection
• Data Mining
• Data Mining in Intrusion Detection
• Reference

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Why do we need Data Mining?

• Despite the enormous amount of data, particular
  events of interest are still quite rare,
  frequency ranges from 0.1 to less than 10
• We are drowning in data, but starving for
  knowledge!??

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Data Mining vs. KDD

• Knowledge Discovery in Databases (KDD) The whole
  process of finding useful information and
  patterns in data
• Data Mining Use of algorithms to extract the
  information and patterns derived by the KDD
  process
• Data mining is the core of the knowledge
  discovery process

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KDD Process

• Selection Obtain data from various sources.
• Preprocessing Cleanse data.
• Transformation Convert to common format.
  Transform to new format.
• Data Mining Obtain desired results.
• Interpretation/Evaluation Present results to
  user in meaningful manner

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Data Mining A KDD Process
Knowledge

• Data mining core of knowledge discovery process

Pattern Evaluation
Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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Typical Data Mining Architecture
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data warehouse server
Filtering
Data cleaning data integration
Data Warehouse
Databases
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Outline

• Intrusion Detection
• Data Mining
• Data Mining in Intrusion Detection
• Reference

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• Network intrusion detection
• Number of intrusions on the network is
  typically a very small fraction of the total
  network traffic

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Why Can Data Mining Help?

• Learn from traffic data
• Supervised learning learn precise models from
  past intrusions
• Unsupervised learning identify suspicious
  activities
• Maintain models on dynamic data
• Correlation of suspicious events across network
  sites
• Helps detect sophisticated attacks not
  identifiable by single site analyses
• Analysis of long term data (months/years)
• Uncover suspicious stealth activities (e.g.
  insiders leaking/modifying information)

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Intrusion Detection

• Traditional intrusion detection system IDS tools
  (e.g. SNORT) are based on signatures of known
  attacks
• Limitations
• Signature database has to be manually revised
  for each new type of discovered intrusion
• They cannot detect emerging cyber threats
• Substantial latency in deployment of newly
  created signatures across the computer system

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Data Mining for Intrusion Detection Techniques
and Applications

• Frequent pattern mining
• Classification
• Clustering
• Mining data streams

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Frequent pattern mining

• Patterns that occur frequently in a database
• Mining Frequent patterns finding regularities
• Process of Mining Frequent patterns for intrusion
  detection
• Phase I mine a repository of normal frequent
  itemsets for attack-free data
• Phase II find frequent itemsets in the last n
  connections and compare the patterns to the
  normal profile

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Frequent pattern mining

• Apriori
• Any subset of a frequent itemset must be also
  frequent an anti-monotone property
• A transaction containing beer, diaper, nuts
  also
• contains beer, diaper
• beer, diaper, nuts is frequent beer,
  diaper must
• also be frequent
• No superset of any infrequent itemset should be
  generated or tested
• Many item combinations can be pruned

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Sequential Pattern Analysis

• Models sequence patterns
• (Temporal) order is important in many situations
• Time-series databases and sequence databases
• Frequent patterns ? (frequent) sequential
  patterns
• Sequential patterns for intrusion detection
• Capture the signatures for attacks in a series of
  packets

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Sequential Pattern Mining

• Given a set of sequences, find the complete set
  of frequent subsequences

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Apriori Property in Sequences
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Classification A Two-Step Process

• Model construction describe a set of
  predetermined classes
• Training dataset tuples for model construction
• Each tuple/sample belongs to a predefined class
• Classification rules, decision trees, or math
  formulae
• Model application classify unseen objects
• Estimate accuracy of the model using an
  independent test set
• Acceptable accuracy ? apply the model to classify
  data tuples with unknown class labels

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Classification
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Classification Decision Tree

• A node in the tree a test of some attribute
• A branch a possible value of the attribute
• Classification
• Start at the root
• Test the attribute
• Move down the tree branch

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Neural classification HIDE

• A hierarchical network intrusion detection
  system using statistical processing and neural
  network classification by Zheng et al.
• Five major components
• Probes collect traffic data
• Event preprocessor preprocesses traffic data and
  feeds the statistical model
• Statistical processor maintains a model for
  normal activities and generates vectors for new
  events
• Neural network classifies the vectors of new
  events
• Post processor generates reports

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Clustering

• What Is Clustering?
• Group data into clusters
• Similar to one another within the same cluster
• Dissimilar to the objects in other clusters
• Unsupervised learning no predefined classes

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Clustering

• What Is A Good Clustering?
• High intra-class similarity and low
  interclasssimilarity
• Depending on the similarity measure
• The ability to discover some or all of the hidden
  patterns

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Clustering

• Clustering Approaches
• Partitioning algorithms
• Partition the objects into k clusters
• Iteratively reallocate objects to improve the
  clustering
• Hierarchy algorithms
• Agglomerative each object is a cluster, merge
  clusters to form larger ones
• Divisive all objects are in a cluster, split
  it up into smaller clusters

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Clustering

• K-Means Example

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Mining Data Streams for Intrusion Detection

• Maintaining profiles of normal activities
• The profiles of normal activities may drift
• Identifying novel attacks
• Identifying clusters and outliers in traffic data
  streams
• Reduce the future alarm load by writing filtering
  rules that automatically discard well-understood
  false positives

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Data Mining for Intrusion Detection

• Misuse detection
• Predictive models are built from labeled data
  sets (instances are labeled as normal or
  intrusive)
• These models can be more sophisticated and
  precise than manually created signatures
• Recent research e.g. JAM (Java Agents for
  Metalearning)

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Misuse Detection
Example if (src_ip dst_ip) then land attack
look for known indicators ICMP Scans, port scans,
connection attempts CPU, RAM I/O Utilization,
File system activity, modification of system
files, permission modifications
Cant detect new attacks
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JAM (Java Agents for Metalearning)

• JAM (developed at Columbia University) uses data
  mining techniques to discover patterns of
  intrusions. It then applies a meta-learning
  classifier to learn the signature of attacks.
• The association rules algorithm determines
  relationships between fields in the audit trail
  records, and the frequent episodes algorithm
  models sequential patterns of audit events.
  Features are then extracted from both algorithms
  and used to compute models of intrusion behavior.
  
• The classifiers build the signature of attacks.
  So thus, data mining in JAM builds misuse
  detection model.
• Classifiers in the JAM are generated by using
  rule learning program on training data of system
  usage. After training, resulting classification
  rules is used to recognize anomalies and detect
  known intrusions.
• The system has been tested with data from
  Sendmail-based attacks, and with network attacks
  using TCP dump data.

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Data Mining for Intrusion Detection

• Anomaly detection
• Identifies anomalies as deviations from normal
  behavior
• E.g. ADAM Audit Data Analysis and Mining MINDS
  MINnesota INtrusion Detection System

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Anomaly Detection
probable intrusion
activity measures
baseline the normal traffic and then look for
things that are out of the norm
Relatively high false positive rate -
anomalies can just be new normal activities.
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ADAM Audit Data Analysis and Mining

• Detecting Intrusion by Data Mining
• Combination of Association Rule and
  Classification Rule
• Firstly, ADAM collects known frequent datasetsan
  off-line algorithm
• Secondly, ADAM runs an online algorithm
• Finds last frequent connection records
• Compare them with known mined data
• Discards those, which seems to be normal
• Suspicious ones are forwarded to the classifier
• Trained classifier then classify the suspicious
  data as one of the following
• Known type of attack
• Unknown type of attack
• False alarm

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ADAM Detecting Intrusion by Data Mining
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ADAM Audit Data Analysis and Mining

• ADAM has two phases in their model
• 1st Phase Train the classifier
• Offline process
• Takes place only once
• Before the main experiment
• 2nd Phase Using the trained classifier
• Trained classifier is then used to detect
  anomalies
• Online process

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The MINDS Project

• MINDS MINnesota INtrusion Detection System
• Learning from Rare Class Building rare class
  prediction models
• Anomaly/outlier detection
• Summarization of attacks using association
  pattern analysis

Rules Discovered Milk --gt Coke
Diaper, Milk --gt Beer
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MINDS - Learning from Rare Class

• Problem Building models for rare network attacks
  (Mining needle in a haystack)
• Standard data mining models are not suitable for
  rare classes
• Models must be able to handle skewed class
  distributions
• Learning from data streams - intrusions are
  sequences of events

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MINDS - Anomaly Detection

• Detect novel attacks/intrusions by identifying
  them as deviations from normal, i.e. anomalous
  behavior
• Identify normal behavior
• Construct useful set of features
• Define similarity function
• Use outlier detection algorithm
• Nearest neighbor approach
• Density based schemes
• Unsupervised Support Vector Machines (SVM)

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Experimental Evaluation

• Publicly available data set
• DARPA 1998 Intrusion Detection Evaluation Data
  Set prepared and managed by MIT Lincoln Lab
  includes a wide variety of intrusions simulated
  in a military network environment
• Real network data from
• University of Minnesota

• Anomaly detection is applied
• 4 times a day
• 10 minutes time window

Open source signature-based network IDS
network
www.snort.org
10 minutes cycle 2 millions connections
net-flow data using CISCO routers
Anomaly scores
Association pattern analysis

MINDSanomaly detection
Data preprocessing
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MINDS - Framework for Mining Associations
Ranked connections
attack
Discriminating Association Pattern Generator
Anomaly Detection System
normal
update

1. Build normal profile
2. Study changes in normal behavior
3. Create attack summary
4. Detect misuse behavior
5. Understand nature of the attack

R1 TCP, DstPort1863 ? Attack R100 TCP,
DstPort80 ? Normal
Knowledge Base
MINDS association analysis module
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Discovered Real-life Association Patterns

• Rule 1 SrcIPXXXX, DstPort80, ProtocolTCP,
  FlagSYN, NoPackets 3, NoBytes120180
  (c1256, c2 1)
• Rule 2 SrcIPXXXX, DstIPYYYY, DstPort80,
  ProtocolTCP, FlagSYN, NoPackets 3, NoBytes
  120180 (c1177, c2 0)

• At first glance, Rule 1 appears to describe a Web
  scan
• Rule 2 indicates an attack on a specific machine
• Both rules together indicate that a scan is
  performed first, followed by an attack on a
  specific machine identified as vulnerable by the
  attacker

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Discovered Real-life Association Patterns
DstIPZZZZ, DstPort8888, ProtocolTCP (c1369,
c20)DstIPZZZZ, DstPort8888, ProtocolTCP,
FlagSYN (c1291, c20)

• This pattern indicates an anomalously high number
  of TCP connections on port 8888 involving machine
  ZZZZ
• Follow-up analysis of connections covered by the
  pattern indicates that this could be a machine
  running a variation of the Kazaa file-sharing
  protocol
• Having an unauthorized application increases the
  vulnerability of the system

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Discovered Real-life Association Patterns(ctd)
SrcIPXXXX, DstPort27374, ProtocolTCP,
FlagSYN, NoPackets4, NoBytes189200 (c1582,
c22) SrcIPXXXX, DstPort12345, NoPackets4,
NoBytes189200 (c1580, c23) SrcIPYYYY,
DstPort27374, ProtocolTCP, FlagSYN,
NoPackets3, NoBytes144 (c1694, c23)

• This pattern indicates a large number of scans on
  ports 27374 (which is a signature for the
  SubSeven worm) and 12345 (which is a signature
  for NetBus worm)
• Further analysis showed that no fewer than five
  machines scanning for one or both of these ports
  in any time window

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Discovered Real-life Association Patterns(ctd)
DstPort6667, ProtocolTCP (c1254, c21)

• This pattern indicates an unusually large number
  of connections on port 6667 detected by the
  anomaly detector
• Port 6667 is where IRC (Internet Relay Chat) is
  typically run
• Further analysis reveals that there are many
  small packets from/to various IRC servers around
  the world
• Although IRC traffic is not unusual, the fact
  that it is flagged as anomalous is interesting
• This might indicate that the IRC server has been
  taken down (by a DOS attack for example) or it is
  a rogue IRC server (it could be involved in some
  hacking activity)

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Discovered Real-life Association Patterns(ctd)
DstPort1863, ProtocolTCP, Flag0, NoPackets1,
NoByteslt139 (c1498, c26)DstPort1863,
ProtocolTCP, Flag0 (c1587, c26)DstPort1863,
ProtocolTCP (c1606, c28)

• This pattern indicates a large number of
  anomalous TCP connections on port 1863
• Further analysis reveals that the remote IP block
  is owned by Hotmail
• Flag0 is unusual for TCP traffic

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MINDS Conclusion

• Data mining based algorithms are capable of
  detecting intrusions that cannot be detected by
  state-of-the-art signature based methods
• SNORT has static knowledge manually updated by
  human analysts
• MINDS anomaly detection algorithms are adaptive
  in nature
• MINDS anomaly detection algorithms can also be
  effective in detecting anomalous behavior
  originating from a compromised or infected machine

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IDS Using both Misuse and Anomaly
DetectionRIDS-100
 
 

• RIDS( Rising Intrusion Detection System) is
  provided by Rising Tech. It is a leader in
  antivirus and content security software and
  services in China.
• The company is a leading provider of client,
  gateway and server security solutions for virus
  protection, firewall and intrusion detection
  technologies and security services to enterprises
  and service providers around China.
• RIDS make the use of both intrusion detection
  technique, misuse and anomaly detection.
• Distance based outlier detection algorithm is
  used for detection deviational behavior among
  collected network data.
• For misuse detection, it has very vast set of
  collected data pattern which can be matched with
  scanned network data for misuse detection.
• This large amount of data pattern is scanned
  using data mining classification Decision Tree
  algorithm.
• http//www.rising-global.com/

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A cooperative anomaly and intrusiondetection
system (CAIDS),

• built with a network-based intrusion detection
  system (NIDS) and an anomaly detection system
  (ADS) operating interactively through a signature
  generator.

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A cooperative anomaly and intrusiondetection
system (CAIDS),

• A frequent episode rule (FER) is generated out of
  a collection of frequent episodes. The FER is
  defined over episode sequences with multiple
  connection events.
• For an example, we envision a window where we
  observe a 3-event sequence
• E, D, and F. An FER is generated as E ? D, F
• confidence level freq (a U b)/freq (b)0.8,
• where a represents the event E on the LHS and b
  corresponds to the two events D and F on the RHS
  of the rule.
• If the b occurs with 5 and the joint event a and
  b has 4 to occur, there is a (0.04/0.05) 80
  chance that D and F will follow in the same
  window.

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A cooperative anomaly and intrusiondetection
system (CAIDS),

• In practice, the event E could be an
  authentication service characterized by two
  attributes
• (service authentication, flagSF).
• The events D, F may be two sequential smtp
  requests denoted by (service smtp).
• Thus we can derive an FER with a confidence level
  of c 80, that two smtp services will follow
  the authentication service within a window w 2
  sec. The three joint traffic events accounts with
  a support level s 10 out of all the network
  connections being evaluated. This FER is formally
  stated as follows
• (service authentication) ? (service smtp)
• (service smtp) (0.8, 0.1, 2 sec)
  (1)

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A cooperative anomaly and intrusiondetection
system (CAIDS),

• An association rule is aimed at finding
  interesting intra-relationship inside a single
  connection record
• In general, an FER is specified by the following
  expression
• L1, L2,, Ln ? R1,, Rm (c, s, window)
  (2)
• Li (1 i n) and Rj (1 j m) are ordered
  traffic connection events.
• We call L1, L2,, Ln the LHS episode and R1,,
  Rm the RHS of the episode rule.

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A cooperative anomaly and intrusiondetection
system (CAIDS),
Architecture of the CAIDS simulator built with a
2,000-signature Snort and an anomaly detection
subsystem (ADS) with 60 FERs after 2 weeks of
rule training over the Lincoln Lab IDS evaluation
dataset
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Conclusion

• In this report we have studied basic concept and
  some classic system models, like ADAM ,MINDSin
  this area.
• To make summary of those system models, their
  technologies and their validation methods.
• Hope to a overview on currently development in
  this area and how data mining is evolving into
  the field of network intrusion detection.

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Reference

• DARPA 1998 data set
• A cleansed set in KDDCup99
• DARPA 1991 data set is also available
• http//www.ll.mit.edu/IST/ideval/data/data_index.h
  tml
• Daniel Barbara, Julia Couto, Sushil Jajodia,
  Leonard Popyack, Ningning Wu, ADAM Detecting
  Intrusions by Data Mining, Proceedings of the
  2001 IEEE Workshop on Information Assurance and
  Security, United States Military Academy, West
  Point, NY, 5-6 June 2001
• Zhang, J. and Zulkernine, M. 2006. A Hybrid
  Network Intrusion Detection Technique Using
  Random Forests. In Proceedings of the First
  international Conference on Availability,
  Reliability and Security (April 20 - 22, 2006).
• W. Lee et al. A data mining framework for
  building intrusion detection models. In
  Information and System Security, Vol. 3, No. 4,
  2000.
• Ertoz L. et Al, "MINDS - Minnesota Intrusion
  Detection System", Next Generation Data Mining
  Chapter 3, 2004
• Exploiting efficient data mining techniques to
  enhance intrusion detection systems Lu, C.-T.
  Boedihardjo, A.P. Manalwar, P. Information Reuse
  and Integration, Conf, 2005. IRI -2005 IEEE
  International Conference on. Volume , Issue ,
  15-17 Aug. 2005 Page(s) 512 - 517
• Sal Stolfo, Andreas Prodromidis, Shelley
  Tselepis, Wenke Lee, Dave Fan, and Phil Chan
  (Honorable mention (runner-up) for Best Paper
  Award in Applied Research Category) In
  Proceedings of the Third International Conference
  on Knowledge Discovery and Data Mining (KDD '97),
  Newport Beach, CA, August 1997

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