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    Data Mining and Intrusion Detection Data Mining and Intrusion Detection Presentation Transcript

    • Data Mining: Concepts and Techniques — Chapter 11 — — Data Mining and Intrusion Detection —
      • Jiawei Han and Micheline Kamber
      • Department of Computer Science
      • University of Illinois at Urbana-Champaign
      • www.cs.uiuc.edu/~hanj
      • ©2006 Jiawei Han and Micheline Kamber. All rights reserved.
      • Acknowledgements: Jian Pei and Huiping Chen (Spring 2004)
    •  
    • Outline
      • Intrusion detection and computer security
      • Current intrusion detection approaches
      • Data Mining Approaches for Intrusion Detection
      • Summary
    • Intrusion Detection and Computer Security
      • Computer security goals:
        • Confidentiality, integrity, and availability
      • Intrusion is a set of actions aimed to compromise these security goals
      • Intrusion prevention (authentication, encryption, etc.) alone is not sufficient
      • Intrusion detection is needed
    • Intrusion Examples
      • Intrusions : Any set of actions that threaten the integrity, availability, or confidentiality of a network resource
      • Examples
        • Denial of service (DoS): attempts to starve a host of resources needed to function correctly
        • Scan: reconnaissance on the network or a particular host
        • Worms and viruses: replicating on other hosts
        • Compromises: obtain privileged access to a host by known vulnerabilities
    • Intrusion Detection
      • Intrusion detection: The process of monitoring and analyzing the events occurring in a computer and/or network system in order to detect signs of security problems
      • Primary assumption : User and program activities can be monitored and modeled
      • Steps
        • Monitoring and analyzing traffic
        • Identifying abnormal activities
        • Assessing severity and raising alarm
    • Monitoring and Analyzing Traffic
      • TCPdump and Windump
        • Provide insight into the traffic activity on a network
          • ftp:// ftp.ee.lbl.gov/tcpdump.tar.Z
          • http:// netgroupserv.polito.it/windump
      • Ethereal
        • GUI to interpret all layers of the packet
    • Goals of Intrusion Detection System (IDS)
      • Detect wide variety of intrusions
        • Previously known and unknown attacks
        • Suggests need to learn/adapt to new attacks or changes in behavior
      • Detect intrusions in timely fashion
        • May need to be real-time, especially when system responds to intrusion
          • Problem: analyzing commands may impact response time of system
        • May suffice to report intrusion occurred a few minutes or hours ago
    • Goals of Intrusion Detect. System (IDS) (2)
      • Present analysis in simple, easy-to-understand format
      • Be accurate
        • Minimize false positives, false negatives
          • False positive : An event, incorrectly identified by the IDS as being an intrusion when none has occurred
          • False negative : An event that the IDS fails to identify as an intrusion when one has in fact occurred
        • Minimize time spent verifying attacks, looking for them
    • IDS Architecture
      • Sensors (agent)
        • to collect data and forward info to the analyzer
          • network packets
          • log files
          • system call traces
      • Analyzers (detector)
        • To receive input from one or more sensors or from other analyzers
        • To determine if an intrusion has occurred
      • User interface
        • To enable a user to view output from the system or control the behavior of the system
    • IDS Architecture
    • Signature-Based Intrusion Detection
      • Human analysts investigate suspicious traffic
      • Extract signatures
        • Features of known intrusions
      • Use pre-defined signatures to discover malicious packets
      • Examples
        • LaBrea Tarpit by Tom Liston
        • Snort and Snort rules Marty Roesch
    • Snort by Marty Roesch
      • An open source free network intrusion detection system
        • Signature-based, use a combination of rules and preprocessors
        • On many platforms, including UNIX and Windows
        • www.snort.org
      • Preprocessors
        • IP defragmentation, port-scan detection, web traffic normalization, TCP stream reassembly, …
        • Can analyze streams, not only a single packet at a time
    • Snort—Overview
      • Typical run from the command line
        • GUI available by IDScenter/Demarc/Puresecure
      • Modes
        • Sniff: dump sniffed traffic to the screen
        • Packet log: log the packets to the disk
        • NIDS: compare the network traffic with a preconfigured set of signatures
      • Output can be stored into spool files or a database
    • Snort Rules
      • Two parts
        • Rule header: define who must be involved
        • Rule options: define what must be involved (action)
      • The rule triggers when an outsider attempt to make an internal TCP connection
      • If both SYN and FIN are set, a message of “SYN-FIN scan” is reported with the alert
      (flags: SF; msg:”SYN-FIN scan;) alert tcp !1.2.3.0/24 any -> 1.2.3.0/24 any Rule options Rule header
    • Application of Snort Rules
      • A packet triggers the first rule that matches and does not examine the remainder
        • The ordering of rules is critical
      • Each Snort rule inspects only one packet
      • Use preprocessors such as IP defragmentation or TCP stream reassembly to handle a series of packets
    • Snort Rule Sets
      • Snort comes with a very large set of rules
        • Not recommended that all rules used on installation
      • New Snort rules are released as soon as hours after a new exploit is discovered
        • A new rule may not be a good rule
        • The attackers may change the signatures easily
    • Snort and Event Viewer on Snort and Event Viewer on NT
    • Problems in Signature-Based Intrusion Detection Systems
      • Many false positives: prone to generating alerts when there is no problem in fact
        • Signatures are not specific enough
        • A packet is not examined in context with those that precede it or those that follow
      • Cannot detect unknown intrusions
        • Rely on signatures extracted by human experts
    • Misuse vs. Anomaly Detection
      • Misuse detection : use patterns of well-known attacks to identify intrusions
        • Classification based on known intrusions
        • E.g., three consecutive login failures: password guessing.
      • Anomaly detection : use deviation from normal usage patterns to identify intrusions
        • Any significant deviations from the expected behavior are reported as possible attacks
    • Misuse vs. Anomaly Detection STAT [HLMS90]
      • Has to hand-coded known pattern.
      • Unable to detect any future intrusion
      matching the sequence of “signature actions” of known intrusion scenarios Misuse Detection Example Shortcoming Definition IDES [LTG+92]
      • Rely upon in selecting the system features.
      • Has to study sequential interrelation between transactions
      using statistical measure on system features Anomaly Detection
    • Host-based vs. Network-based
      • According to data sources
      • Host-based detection : the data is collected from an individual host
        • Directly monitor the host data files and OS processes
        • Can determine exactly which host resources are the targets of a particular attack
      • Network-based detection : the data is traffic across the network
        • A set of traffic sensors within the network
        • Can easily harder against attacks and hide from the attackers
    • OUTLINE
      • Intrusion detection and computer security
      • Current intrusion detection approaches
      • Data Mining Approaches for Intrusion Detection
      • Summary
    • Current Intrusion Detection Approaches—Misuse Detection
      • Misuse detection :
        • Record the specific patterns of intrusions
        • Monitor current audit trails (event sequences) and pattern matching
        • Report the matched events as intrusions
        • Representation models: expert rules, Colored Petri Net, and state transition diagrams, etc.
    • Misuse Detection Example
      • Expert systems: use a set of rules to describe attacks
        • IDES, ComputerWatch, NIDX, P-BEST, ISOA
      • Signature analysis: capture features of attacks in audit trail
        • Haystack, NetRanger, RealSecure, MuSig
      • State-transition analysis: use state-transition diagrams
        • STAT,USTAT and NetSTAT
      • Other approaches
        • Colored petri nets, e.g., IDIOT
        • Case-based reasoning, e.g., AUTOGUARD
    • Current Intrusion Detection Approaches—Anomaly Detection
      • Anomaly detection:
        • Establishing the normal behavior profiles
        • Observing and comparing current activities with the (normal) profiles
        • Reporting significant deviations as intrusions
        • Statistical measures as behavior profiles: ordinal and categorical (binary and linear)
    • Anomaly Detection Example
      • Statistical methods: multivariate, temporal analysis
        • IDES, NIDES, EMERALD
      • Expert systems
        • ComputerWatch, Wisdom & Sense
    • Problems of Current Intrusion Detection Approaches
      • Main problems: manual and ad-hoc
        • Misuse detection:
          • Known intrusion patterns have to be hand-coded
          • Unable to detect any new intrusions (that have no matched patterns recorded in the system)
        • Anomaly detection:
          • Selecting the right set of system features to be measured is ad hoc and based on experience
          • Unable to capture sequential interrelation between events
    • OUTLINE
      • Intrusion detection and computer security
      • Current intrusion detection approaches
      • Data Mining Approaches for Intrusion Detection
      • Summary
    • Data Mining Approaches for Intrusion Detection
      • A systematic framework
      • Why Can Data Mining Help?
      • Relevant data mining techniques
      • Building Classifiers for Intrusion Detection
      • Mining Patterns from Audit Data
    • A Systematic Framework—J.Stolfo et al.
      • Build good models:
        • select appropriate features of audit data to build intrusion detection models
      • Build better models:
        • architect a hierarchical detector system that combines multiple detection models
      • Build updated models:
        • dynamically update and deploy new detection system as needed
    • A Systematic Framework
      • Support for the feature selection and model construction:
        • Apply data mining algorithms to find consistent inter- and intra- audit record (event) patterns
        • Use the features and time windows in the discovered patterns to build detection models
        • A support environment to semi-automate this process
    • A Systematic Framework
      • Combining multiple detection models:
        • Each (base) detector model monitors one aspect of the system
        • They can employ different techniques and be independent of each other
        • The learned (meta) detector combines evidence from a number of base detectors
      • An intelligent agent-based architecture :
        • learning agents: continuously compute (learn) the detection models
        • detection agents: use the (updated) models to detect intrusions
    • A Systematic Framework
    • Data Mining Approaches for Intrusion Detection
      • A systematic framework
      • Why Can Data Mining Help?
      • Relevant data mining techniques
      • Building Classifiers for Intrusion Detection
      • Mining Patterns from Audit Data
    • Why Can Data Mining Help?
      • Data mining: applying specific algorithms to extract patterns from data
      • Normal and intrusive activities leave evidence in audit data
      • From the data-centric point view, intrusion detection is a data analysis process
    • Why Can Data Mining Help?
      • Successful applications in related domains, e.g., fraud detection, fault/alarm management
      • Learn from traffic data
        • Supervised learning: learn precise models from past intrusions
        • Unsupervised learning: identify suspicious activities
      • Maintain or update models on dynamic data
    • Data Mining Approaches for Intrusion Detection
      • A systematic framework
      • Why Can Data Mining Help?
      • Relevant data mining techniques
      • Building Classifiers for Intrusion Detection
      • Mining Patterns from Audit Data
    • Frequent Patterns
      • 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
    • Frequent Pattern Mining in MINDS
      • MINDS: a IDS using data mining techniques
        • University of Minnesota
      • Summarizing attacks using association rules
        • {Src IP=206.163.27.95, Dest Port=139, Bytes  [150, 200)}  {ATTACK}
    • Patterns About Alerts
      • Ning et al. CCS’02
      • Find correlated alerts – the frequent patterns of alerts
        • Attack scenarios – the logical connections between alerts
        • A hyper-alerts correlation graph approach
      • Use the correlation of intrusion alerts to identify high level attacks
    • Associate rules
      • Used for link analysis
      • E.g.:
        • If the number of failed login attempts ( num_failed_login_attempts ) and the network service on the destination ( service ) are features, an example of rule is:
        • num_failed_login_attempts = 6, service = FTP => attack = DoS [1, 0.28 ]
    • 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
    • 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
    • Classification Methods
      • Basic Algorithm ID3
      • Neural networks
      • Bayesian classification
        • Naïve Bayesian classification
        • Bayesian belief network
      • Support vector machines
    • Classification for Intrusion Detection
      • Misuse detection
        • Classification based on known intrusions
      • Example: Sinclair et al. “An application of machine learning to network intrusion detection”
        • Use decision trees and ID3 on host session data
        • Use genetic algorithms to generate rules
          • If <pattern> then <alert>
    • 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
    • Intrusion Detection by NN and SVM
      • S. Mukkamala et al., IEEE IJCNN May 2002
      • Discover useful patterns or features that describe user behavior on a system
      • Use the set of relevant features to build classifiers
      • SVMs have great potential to be used in place of NNs due to its scalability and faster training and running time
      • NNs are especially suited for multi-category classification
    • Clustering
      • Group data into clusters
      • What is a good clustering
        • High intra-class similarity and low inter-class similarity
          • Depending on the similarity measure
        • The ability to discover some or all of the hidden patterns
      • Clustering Approaches
        • K-means
        • Hierarchical Clustering
        • Density-based methods
        • Grid-based methods
        • Model-based
    • Clustering for Intrusion Detection
      • Anomaly detection
        • Any significant deviations from the expected behavior are reported as possible attacks
      • Build clusters as models for normal activities
      • “ A scalable clustering for intrusion signature recognition” by Ye and Li
        • Use description of clusters as signatures of intrusions
    • Alert Correlation
      • F. Cuppens and A. Miege, in IEEE S&P’02
      • Use clustering and merging functions to recognize alerts that correspond to the same occurrence of an attack
        • Create a new alert that merge data contained in these various alerts
      • Generate global and synthetic alerts to reduce the number of alerts further
    • Mining Data Streams
      • Continuous arrival data in multiple, rapid, time-varying, possibly unpredictable and unbounded streams
      • Many applications
        • Financial applications, network monitoring, security, telecommunications data management, web application, manufacturing, sensor networks, etc.
    • 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
    • Data Mining Approaches for Intrusion Detection
      • A systematic framework
      • Why Can Data Mining Help?
      • Relevant data mining techniques
      • Building Classifiers for Intrusion Detection
      • Mining Patterns from Audit Data
    • Building Classifiers for Intrusion Detection— J.Stolfo et al.
      • Experiments in constructing classification models for anomaly detection
      • Two experiments:
        • sendmail system call data
        • network tcpdump data
      • Use meta classifier to combine multiple classification models
    • Classification Models on sendmail
      • The data: sequence of system calls made by sendmail .
      • Classification models (rules): describe the “normal” patterns of the system call sequences.
      • The rule set is the normal profile of sendmail
      • Detection: calculate the deviation from the profile
        • large number/high scores of “violations” to the rules in a new trace suggests an exploit
    • Classification Models on sendmail
      • The sendmail data:
        • Each trace has two columns: the process ids and the system call numbers
        • Normal traces: sendmail and sendmail daemon
        • Abnormal traces: sunsendmailcap, syslog-remote, syslog-remote, decode, sm5x and sm56a attacks
    • Classification Models on sendmail
      • Lessons learned:
        • Normal behavior can be established and used to detect anomalous usage
        • Need to collect near “complete” normal data in order to build the “normal” model
        • But how do we know when to stop collecting?
        • Need tools to guide the audit data gathering process
    • Classification Models on tcpdump
      • The tcpdump data (part of a public data visualization contest):
        • Packets of incoming, out-going, and internal broadcast traffic
        • One trace of normal network traffic
        • Three traces of network intrusions
    • Data Preprocessing
      • Extract the “connection” level features:
        • Record connection attempts
        • Watch how connection is terminated
      • Each record has:
        • start time and duration
        • participating hosts and ports (applications)
        • statistics (e.g., # of bytes)
        • flag: normal or a connection/termination error
        • protocol: TCP or UDP
      • Divide connections into 3 types: incoming, out-going, and inter-lan
    • Building Classifier for Each Type of Connections
      • Use the destination service (port) as the class label
      • Training data: 80% of the normal connections
      • Testing data: 20% of the normal connections and connections in the 3 intrusion traces
      • Apply RIPPER to learn rules
    • Lessons Learned
      • Data preprocessing requires extensive domain knowledge
      • Adding temporal features improves classification accuracy
      • Need tools to guide (temporal) feature selection
    • Meta Classifier that Combines Evidence from Multiple Detection Models
      • Build base classifiers that each model one aspect of the system
      • The meta learning task:
        • each record has a collection of evidence from base classifiers, and a class label “normal”or “abnormal” on the state of the system
      • Apply a learning algorithm to produce the meta classifier
    • Data Mining Approaches for Intrusion Detection
      • A systematic framework
      • Why Can Data Mining Help?
      • Relevant data mining techniques
      • Building Classifiers for Intrusion Detection
      • Mining Patterns from Audit Data
    •  
    •  
    • Associate rules
      • Motivations
        • Audit data can be easily formatted into a database table
        • Program executions and user activities have frequent correlation among system features
        • Incremental updating of the rule set is easy
    • Frequent Episodes
      • frequent events occurring within a time window
      • X => Y, confidence, support, window:
        • X and Y are subsets of the attribute values in a record
        • support is the percentage of (sliding) windows that contain X and Y
    • Frequent Episodes
      • Motivation:
        • Sequence information needs to be included in a detection model
      • An example from a department’s web log:
        • home, research => theory, [0.2, 0.05], [30]
        • Meaning: 20% of the time, after home and research pages are visited (in that order), the theory is then visited within 30 seconds from when home is visited; and visiting these three pages constitutes 5% of all visits to the web site
    • Algorithm
      • Using the Axis Attribute(s):a form of item constraints, the essentialattribute(s) of a record (transaction).
      • Level-wise Approximate Mining
    •  
    • Using the Mined Patterns
      • Guide the audit data gathering process
        • Run a program under different settings
        • For each run, calculate the association rules and frequent episodes from its audit data
        • Merge them into an aggregate rule set
        • Stop gathering audit data when no rules can be added from a new run
      • Support the feature selection process
        • System features in the association rules and frequent episodes should be included in the classification models
        • Time window and features in the frequent episodes suggest additional temporal features should be considered
    • Adaptive Intrusion Detection System
      • Intrusion detection model based on data mining and fuzzy logic
      • Integration of fuzzy logic with data mining
      • Similarity function
      • Optimization of fuzzy membership function parameters
    • The framework of the Adaptive Intrusion Detection System
    • References
      • W. Lee et al. A data mining framework for building intrusion detection models. In Information and System Security, Vol. 3, No. 4, 2000.
      • C. Kruegel and G. Vigna. Anomaly detection of web-based attacks, in ACM CCS’03
      • S. Mukkamala et al., Intrusion detection using neural networks and support vector machines, in IEEE IJCNN (May 2002).
      • Bertrand Portier, Data Mining Techniques for Intrusion Detection
      • S. Axelsson, Intrusion Detection Systems: A Survey and Taxonomy
      • J. Allen et al., State of the Practice of Intrusion Detection Technologies
      • Susan M. Bridges et al. DATA MINING AND GENETIC ALGORITHMS APPLIED TO INTRUSION DETECTION
      • S. Mukkamala et al. Intrusion detection using neural networks and support vector machines, IEEE IJCNN (May 2002)
    •