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Data Mining for Network Intrusion Detection

Data Mining for Network Intrusion Detection. Vipin Kumar Army High Performance Computing Research Center Department of Computer Science University of Minnesota http://www.cs.umn.edu/~kumar

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Data Mining for Network Intrusion Detection

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  1. Data Mining for Network Intrusion Detection Vipin Kumar Army High Performance Computing Research Center Department of Computer Science University of Minnesota http://www.cs.umn.edu/~kumar Project Participants: V. Kumar, A. Lazarevic, J. Srivastava P. Dokas, E. Eilertson, L. Ertoz, S. Iyer, S. Ketkar, P. Tan Research supported by AHPCRC/ARL

  2. Cyber Threat Analysis • As the cost of information processing and Internet accessibility falls, organizations are becoming increasingly vulnerable to potential cyber threats such as network intrusions • Intrusions are actions that attempt to bypass security mechanisms of computer systems • Intrusions are caused by: • Attackers accessing the system from Internet • Insider attackers - authorized users attempting to gain and misuse non-authorized privileges

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

  4. Data Mining for Intrusion Detection • Increased interest in data mining based IDS for detection • Attacks for which it is difficult to build signatures • Unforeseen/Unknown attacks • Emerging Threats • Data mining approaches for intrusion detection • Misuse detection • Building predictive models from labeled labeled data sets (instances are labeled as “normal” or “intrusive”) • Can only detect known attacks and their variations • High accuracy in detecting many kinds of known attacks • Anomaly detection • Able to detect novel attacks as deviations from “normal” behavior • Potential high false alarm rate - previously unseen (yet legitimate) system behaviors may also be recognized as anomalies

  5. Misuse Detection • Classification of intrusions • RIPPER [Madam ID @Columbia U], Bayesian classifier [ADAM @ George Mason U], fuzzy association rules [Bridges00], decision trees [ARL U Texas, Sinclair99], neural networks [Lippmann00, Ghosh99, Canady98], genetic algorithms [Bridges00, Sinclair99] • Association pattern analysis • Building normal profile [Barbara01, Manganaris99], frequent episodes for constructing features [Madam ID @ Columbia U] • Cost sensitive modeling • AdaCost[Fan99], MetaCost[Domingos99], [Ting00], [Karakoulas95] • Learning from rare class • [Kubat97, Fawcett97, Ling98, Provost01, Japkowicz01, Chawla01, Joshi01]

  6. Anomaly Detection • Statistical approaches • Finite mixture model [Yamanishi00], 2 based [Ye01] • Various anomaly detection • Temporal sequence learning [Lane98], neural networks [Ryan98], similarity tree [Kokkinaki97], generating artificial anomalies [Fan01], • Clustering [Madam ID, Eskin02], unsupervised SVM [Madam ID, Eskin02], • Outlier detection schemes • Nearest neighbor approaches [Knorr98, Jin01, Ramaswamy00, Aggarwal01], Density based [Breunig00], connectivity based [Tang01],Clustering based [Yu99]

  7. “Mining needle in a haystack. So much hay and so little time” Key Technical Challenges • Large data size • Millions of network connections are common for commercial network sites, … • High dimensionality • Hundreds of dimensions are possible • Temporal nature of the data • Data points close in time - highly correlated • Skewed class distribution • Interesting events are very rare  looking for the “needle in a haystack” • Data Preprocessing • Converting network traffic into data • High Performance Computing (HPC) can be critical for on-line analysis and scalability to very large data sets

  8. MINDS Project - Recent Accomplishments • MINDS – MINnesota INtrusion Detection System • Learning from Rare Class – Building rare class prediction models • Anomaly/outlier detection • Summarization of attacks using association pattern analysis

  9. 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 • Key results: • PNrule and related work [Joshi, Agarwal, Kumar, SIAM 2001, SIGMOD 2001, ICDM 2001, KDD 2002] • SMOTEBoost algorithm [Lazarevic, in review] • CREDOS algorithm [Joshi, Kumar, in review] • Classification based on association - add frequent items as “meta-features” to original data set

  10. MINDS - Anomaly and Outlier Detection • Approach • Detecting novel attacks/intrusions by identifying them as deviations from “normal” behavior • Goals: • Construct useful set of features for data mining algorithms • Identify novel intrusions using outlier detection schemes • Distance based techniques • Nearest neighbor approach • Mahalanobis-distance approach • Clustering based approaches • Density based schemes • Unsupervised Support Vector Machines (SVM)

  11. Experimental Evaluation • Publicly available data set • DARPA 1998 Intrusion Detection Evaluation Data Set • 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

  12. DARPA 1998 Data Set • DARPA 1998 data set (prepared and managed by MIT Lincoln Lab) includes a wide variety of intrusions simulated in a military network environment • 9 weeks of raw TCP dump data • 7 weeks for training (5 million connection records) • 2 weeks for training (2 million connection records) • Connections are labeled as normal or attacks (4 main categories of attacks - 38 attack types) • DOS - Denial Of Service • Probe - e.g. port scanning • U2R - unauthorized access to gain root privileges, • R2L - unauthorized remote login to machine, • Two types of attacks • Bursty attacks - involve multiple network connections • Non-bursty attacks - involve single network connections

  13. Feature construction • Three groups of features • Basic features of individual TCP connections: source & destination IP/port, protocol, number of bytes, duration, number of packets (used in SNORT only in stream builder) • Time based features • For the same source (destination) IP address, number of unique destination (source) IP addresses inside the network in last T seconds • Number of connections from source (destination) IP to the same destination (source) port in last T seconds • Connection based features • For the same source (destination) IP address, number of unique destination (source) IP addresses inside the network in last N connections • Number of connections from source (destination) IP to the same destination (source) port in last N connections

  14. MINDS Outlier Detection on DARPA’98 Data ROC curves for bursty attacks LOF approach is consistently better than other approaches Unsupervised SVMs are good but only for high false alarm (FA) rate NN approach is comparable to LOF for low FA rates, but detection rate decrease for high FA Mahalanobis-distance approach – poor due to multimodal normal behavior ROC curves for single-connection attacks LOF approach is superior to other outlier detection schemes Majority of single connection attacks are probably located close to the dense regions of the normal data

  15. Outlier Detection Recent Results(on DARPA’98 data) • Analyzing multi-connection attacks using the score values assigned to network connections • Detection rate is measured through number of connections that have score higher than 0.5 Low peaks due to occasional “reset” value for the feature called “connection status”

  16. Recently Detected Real-life Attacks • During the past few months various intrusive/suspicious activities were detected at the AHPCRC and at the U of Minnesota using MINDS • A sample of top ranked anomalies/attacks picked by MINDS • August 13, 2002 Detected scanning for Microsoft DS service on port 445/TCP (Ranked #1) • Reported by CERT as recent DoS attacks that needs further analysis(CERT August 9, 2002) • Undetected by SNORT since the scanning was non-sequential (very slow) Number of scanning activities on Microsoft DS service on port 445/TCP reported in the World (Source www.incidents.org)

  17. Recently Detected Real-life Attacks …(ctd) • A sample of top ranked anomalies/attacks picked by MINDS • August 13, 2002 • Detected scanning for Oracle server (Ranked #2) • Reported by CERT, June 13, 2002 • First detection of this attack type by our University • Undetected by SNORT because the scanning was hidden within another Web scanning • August 8, 2002 • Identified machine that was running Microsoft PPTP VPN server on non-standard ports, which is a policy violation (Ranked #1) • Undetected by SNORT since the collected GRE traffic was part of the normal traffic • October 30, 2002 • Identified compromised machines that were running FTP servers on non-standard ports, which is a policy violation (Ranked #1) • Anomaly detection identified this due to huge file transfer on a non-standard port • Undetectable by SNORT due to the fact there are no signatures for these activities

  18. Recently Detected Real-life Attacks …(ctd) • A sample of top ranked anomalies/attacks picked by MINDS • October 10, 2002 • Detected several instances of slapper worm that were not identified by SNORT since they were variations of existing warm code • Deteted by MINDS anomaly detection algorithm since source and destination ports are the same but non-standard, and slow scan-like behavior for the source port • Potentially detectable by SNORT using more general rules, but the false alarm rate will be too high Number of slapper worms on port 2002 reported in the World (Source www.incidents.org)

  19. Recently Detected Real-life Attacks …(ctd) • Top ranked anomalies/attacks picked by MINDS • October 10, 200 • Detected a distributed windows networking scan from two different source locations (Ranked #1) • Similar distributed scan from 100 machines scattered around the World happened at University of Auckland, New Zealand, on August 8, 2002 and it was reported by CERT, Insecure.org and other security organizations Attack sources Destination IPs Distributed scanning activity

  20. SNORT vs. MINDS Anomaly/Outlier • SNORT has static knowledge manually updated by human analysts • MINDS anomaly/outlier detection algorithms are adaptive in nature include infinite number of rules • MINDS anomaly/outlier detection algorithms san also be effective in detecting anomalous behavior originating from a compromised machine

  21. SNORT vs. MINDS Anomaly/Outlier • Content-based attacks (e.g. content of the packet) • SNORT is able to detect only those attacks with known signatures • Out of scope for MINDSanomaly/detection algorithms, since they do not use the content of the packets • Scanning activities • Same source sequential destination scans • SNORT is better than MINDS anomaly/outlier detection in identifying these attacks, since it is specifically designed for their detection • Scans with random destinations • MINDS anomaly/outlier detection algorithms discover them quicker than SNORT since SNORT has to increase time window (specifies the scanning threshold) which results in the large memory requirements • Slow scans • MINDS anomaly/outlier detection identifies them better than SNORT, since SNORT has to increase time window which increases processing requirements

  22. SNORT vs. MINDS Anomaly/Outlier • Policy violations (e.g. rogue and unauthorized services) • MINDS anomaly/outlier detection algorithms are successful in detecting policy violations, since they are looking for unusual and suspicious network behavior • To detect these attacks SNORT has to have a rule for each specific unauthorized activity, which causes increase in the number of rules and therefore the memory requirements

  23. MINDS - Framework for Mining Associations Ranked connections attack Discriminating Association Pattern Generator Anomaly Detection System normal update • Build normal profile • Study changes in normal behavior • Create attack summary • Detect misuse behavior • Understand nature of the attack R1: TCP, DstPort=1863  Attack … … … … R100: TCP, DstPort=80 Normal Knowledge Base

  24. Discovered Real-life Association Patterns • 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 • Rule 1: SrcIP=XXXX, DstPort=80, Protocol=TCP, Flag=SYN, NoPackets: 3, NoBytes:120…180 (c1=256, c2 = 1) • Rule 2: SrcIP=XXXX, DstIP=YYYY, DstPort=80, Protocol=TCP,Flag=SYN, NoPackets: 3, NoBytes: 120…180 (c1=177, c2 = 0)

  25. Discovered Real-life Association Patterns…(ctd) DstIP=ZZZZ, DstPort=8888, Protocol=TCP (c1=369, c2=0)DstIP=ZZZZ, DstPort=8888, Protocol=TCP, Flag=SYN (c1=291, c2=0) • 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

  26. Discovered Real-life Association Patterns…(ctd) SrcIP=XXXX, DstPort=27374, Protocol=TCP, Flag=SYN, NoPackets=4, NoBytes=189…200 (c1=582, c2=2) SrcIP=XXXX, DstPort=12345, NoPackets=4, NoBytes=189…200 (c1=580, c2=3) SrcIP=YYYY, DstPort=27374, Protocol=TCP, Flag=SYN, NoPackets=3, NoBytes=144 (c1=694, c2=3) …… • 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

  27. Discovered Real-life Association Patterns…(ctd) DstPort=6667, Protocol=TCP (c1=254, c2=1) • 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)

  28. Discovered Real-life Association Patterns…(ctd) DstPort=1863, Protocol=TCP, Flag=0, NoPackets=1, NoBytes<139 (c1=498, c2=6)DstPort=1863, Protocol=TCP, Flag=0 (c1=587, c2=6)DstPort=1863, Protocol=TCP (c1=606, c2=8) • 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 • Flag=0 is unusual for TCP traffic

  29. Conclusions • Rare class predictive models improve the detection of infrequent attack types • MINDS anomaly/outlier detection algorithms are successful in detection of intrusions that could not be picked by commercial “state of the art” IDS tools (SNORT) • Slow scans and random scans • Policy violations and unauthorized activities • MINDS association patterns can be useful in creating summaries of detected attacks and suggesting new signatures

  30. Future Work • On-line detection algorithms • Better characterization of “normal” behavior • Detection of distributed attacks • Insider attacks • Other applications of anomaly detection • Credit card fraud detection • Insurance fraud detection • Transient fault detection for industrial process control • Detecting individuals with rare medical syndromes (e.g. cardiac arrhythmia)

  31. Questions?

  32. y’ x’ * * * * * * * * * * * * * * * * * * * * * * p1 p2   Distance based Outlier Detection Schemes • Nearest Neighbor (NN) approach • For each point compute the distance to the k-th nearest neighbor dk • Outliers are points that have larger distance dk and therefore are located in the more sparse neighborhoods • Mahalanobis-distance based approach • Mahalanobis distance is more appropriate for computing distances with skewed distributions Back

  33. p2  p1  Density based Outlier Detection Schemes • Local Outlier Factor (LOF) approach • For each point compute the density of local neighborhood • Compute LOF of example p as the average of the ratios of the density of example p and the density of its nearest neighbors • Outliers are points with the largest LOF value In the NN approach, p2 is not considered as outlier, while the LOF approach find both p1 and p2 as outliers Back

  34. origin Unsupervised Support Vector Machines for Outlier Detection • Unsupervised SVMs attempt to separate the entire set of training data from the origin, i.e. to find a small region where most of the data lies and label data points in this region as one class • Parameters • Expected number of outliers • Variance of rbf kernel • As the variance of the rbf kernel gets smaller, the separating surface gets more complex push the hyper plane away from origin as much as possible Back

  35. SNORT signature based Network IDS • SNORT (www.snort.org) is an open source Network Intrusion Detection System (IDS) based on signatures • SNORT contains anomaly detector SPADE (Statistical Packet Anomaly Detection Engine) usually turned off due to high false alarm rate • SNORT may be configured in one of the following modes • sniffer mode – reads the packets from the network and displays them for you in a continuous stream on the console • packet logger mode – logs the packet to the disk • intrusion detection mode - analyzes network traffic for matches against a user defined rule set and perform several actions based upon what it sees. Back

  36. SPADE – SNORT Anomaly Detection • SPADE is a SNORT preprocessor plugin which sends alerts of anomalous packet through standard SNORT reporting mechanisms (the fewer times that a particular kind of packet has occurred in the past, the higher its anomaly score will be) • It is a part of SPICE (Stealthy Probing and Intrusion Correlation Engine) project at www.silicondefense.com • SPICE consists of two parts: • SPADE that act as an anomaly sensor engine and report anomalous events to event correlator • event correlator that groups these events together and send out reports of unusual activity (e.g., portscans) Back

  37. Recently detected real-life attacks • http://www.cert.org/current/current_activity.html#Microsoft-DS • Microsoft-DS (445/tcp) Activityupdated August 9    |    added August 9 • “We have received reports of widespread scanning and possible denial of service activity targeted at the Microsoft-DS service on port 445/tcp. We are interested in receiving reports of this activity from sites with detailed logs and evidence of an attack. Please send all reports to cert@cert.org” Back

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