CS419 Spring Computer Security. Vinod Ganapathy Lecture 13. Chapter 6: Intrusion Detection

Transcription

1 CS419 Spring 2010 Computer Security Vinod Ganapathy Lecture 13 Chapter 6: Intrusion Detection

2 Security Intrusion & Detection Security Intrusion a security event, or combination of multiple security events, that constitutes a security incident in which an intruder gains, or attempts to gain, access to a system (or system resource) without having authorization to do so. Intrusion Detection a security service that monitors and analyzes system events for the purpose of finding, and providing realtime or near real time warning of attempts to access system resources in an unauthorized manner.

3 Principles of Intrusion Detection Characteristics of systems not under attack User, process actions conform to statistically predictable pattern User, process actions do not include sequences of actions that subvert the security policy Process actions correspond to a set of specifications describing what the processes are allowed to do Systems under attack do not meet at least one of these

4 Example Goal: insert a back door into a system Intruder will modify system configuration file or program Requires privilege; attacker enters system as an unprivileged user and must acquire privilege Nonprivileged user may not normally acquire privilege (violates #1) Attacker may break in using sequence of commands that violate security policy (violates #2) Attacker may cause program to act in ways that violate program s specification

5 Goals of 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 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

6 Goals of IDS Present analysis in simple, easy tounderstand format Ideally a binary indicator Usually more complex, allowing analyst to examine suspected attack User interface critical, especially when monitoring many systems Be accurate Minimize false positives, false negatives Minimize time spent verifying attacks, looking for them

7 Intrusion Techniques objective to gain access or increase privileges initial attacks often exploit system or software vulnerabilities to execute code to get backdoor e.g. buffer overflow or to gain protected information e.g. password guessing or acquisition

8 Intrusion Detection Systems classify intrusion detection systems (IDSs) as: Host based IDS: monitor single host activity Network based IDS: monitor network traffic logical components: sensors collect data analyzers determine if intrusion has occurred user interface manage / direct / view IDS

9 Models of Intrusion Detection Anomaly detection What is usual, is known What is unusual, is bad Misuse detection What is bad, is known What is not bad, is good Specification based detection What is good, is known What is not good, is bad

10 IDS Principles assume intruder behavior differs from legitimate users expect overlap as shown observe deviations from past history problems of: false positives false negatives must compromise

11 IDS Requirements run continually be fault tolerant resist subversion impose a minimal overhead on system configured according to system security policies adapt to changes in systems and users scale to monitor large numbers of systems provide graceful degradation of service allow dynamic reconfiguration

12 IDS Architecture Basically, a sophisticated audit system Sensor: gathers data for analysis Analyzer: it analyzes data obtained from the sensor according to its internal rules Notifier obtains results from analyzer, and takes some action May simply notify security officer May reconfigure agents, director to alter collection, analysis methods May activate response mechanism

13 Sensors Obtains information and sends to analyzer May put information into another form Preprocessing of records to extract relevant parts May delete unneeded information Analyzer may request agent send other information

14 Example IDS uses failed login attempts in its analysis Sensor scans login log every 5 minutes, sends director for each new login attempt: Time of failed login Account name and entered password Analyzer requests all records of login (failed or not) for particular user Suspecting a brute force cracking attempt

15 Host Based Sensors Obtain information from logs May use many logs as sources May be security related or not May be virtual logs if agent is part of the kernel Very non portable Sensor generates its information Scans information needed by IDS, turns it into equivalent of log record Typically, check policy; may be very complex

16 Network Based Sensors Detects network oriented attacks Denial of service attack introduced by flooding a network Monitor traffic for a large number of hosts Examine the contents of the traffic itself Agent must have same view of traffic as destination TTL tricks, fragmentation may obscure this End to end encryption defeats content monitoring Not traffic analysis, though

17 Network Issues Network architecture dictates agent placement Ethernet or broadcast medium: one agent per subnet Point to point medium: one agent per connection, or agent at distribution/routing point Focus is usually on intruders entering network If few entry points, place network agents behind them Does not help if inside attacks to be monitored

18 Analyzer Reduces information from sensors Eliminates unnecessary, redundant records Analyzes remaining information to determine if attack under way Analysis engine can use a number of techniques, discussed before, to do this Usually run on separate system Does not impact performance of monitored systems Rules, profiles not available to ordinary users

19 Notifier Accepts information from director Takes appropriate action Notify system security officer Respond to attack Often GUIs Well designed ones use visualization to convey information

20 Example GUI D B A E C GUI showing the progress of a worm as it spreads through network Left is early in spread Right is later on

21 Host Based IDS specialized software to monitor system activity to detect suspicious behavior primary purpose is to detect intrusions, log suspicious events, and send alerts can detect both external and internal intrusions two approaches, often used in combination: anomaly detection defines normal/expected behavior threshold detection profile based signature detection defines (im)proper behavior

22 Audit Records a fundamental tool for intrusion detection two variants: native audit records provided by O/S always available but may not be optimum detection specific audit records IDS specific additional overhead but specific to IDS task often log individual elementary actions e.g. may contain fields for: subject, action, object, exception condition, resource usage, time stamp

23 Anomaly Detection threshold detection checks excessive event occurrences over time alone a crude and ineffective intruder detector must determine both thresholds and time intervals profile based characterize past behavior of users / groups then detect significant deviations based on analysis of audit records gather metrics: counter, guage, interval timer, resource utilization analyze: mean and standard deviation, multivariate, markov process, time series, operational model

24 Threshold Metrics Counts number of events that occur Between m and n events (inclusive) expected to occur If number falls outside this range, anomalous Example Windows: lock user out after k failed sequential login attempts. Range is (0, k 1). k or more failed logins deemed anomalous

25 Difficulties Appropriate threshold may depend on non obvious factors Typing skill of users If keyboards are US keyboards, and most users are French, typing errors very common Dvorak vs. non Dvorak within the US

26 Statistical Moments Analyzer computes standard deviation, other measures of correlation If measured values fall outside expected intervals, anomalous Potential problem Profile may evolve over time; solution is to weigh data appropriately or alter rules to take changes into account

27 Example: IDES Developed at SRI International Represent users, login session, other entities as ordered sequence of statistics <q 0,j,, q n,j > q i,j (statistic i for day j) is count or time interval Weighting favors recent behavior over past behavior A k,j sum of counts making up metric of kth statistic on jth day q k,l+1 = A k,l+1 A k,l + 2 rt q k,l where t is number of log entries/total time since start, r factor determined through experience

28 Potential Problems Assumes behavior of processes and users can be modeled statistically Ideal: matches a known distribution such as Gaussian or normal Otherwise, must use techniques like clustering to determine moments, characteristics that show anomalies, etc. Real time computation a problem too

29 Markov Model Past state affects current transition Anomalies based upon sequences of events, and not on occurrence of single event Problem: need to train system to establish valid sequences Use known, training data that is not anomalous The more training data, the better the model Training data should cover all possible normal uses of system

30 Example: TIM Time based Inductive Learning Sequence of events is abcdedeabcabc TIM derives following rules: R 1 : ab c (1.0)R 2 : c d (0.5)R 3 : c e (0.5) R 4 : d e (1.0) R 5 : e a (0.5) R 6 : e d (0.5) Seen: abd; triggers alert c always follows ab in rule set Seen: acf; no alert as multiple events can follow c May add rule R 7 : c f (0.33); adjust R 2, R 3

31 Misuse Detection observe events on system and applying a set of rules to decide if intruder approaches: rule based anomaly detection analyze historical audit records for expected behavior, then match with current behavior rule based penetration identification rules identify known penetrations / weaknesses often by analyzing attack scripts from Internet supplemented with rules from security experts

32 Misuse Modeling Determines whether a sequence of instructions being executed is known to violate the site security policy Descriptions of known or potential exploits grouped into rule sets IDS matches data against rule sets; on success, potential attack found Cannot detect attacks unknown to developers of rule sets No rules to cover them

33 Example: NFR Built to make adding new rules easily Architecture: Packet sucker: read packets from network Decision engine: uses filters to extract information Backend: write data generated by filters to disk Query backend allows administrators to extract raw, postprocessed data from this file Query backend is separate from NFR process

34 Domain specific Language Example: ignore all traffic not intended for 2 web servers: # list of my web servers my_web_servers = [ ] ; # we assume all HTTP traffic is on port 80 filter watch tcp ( client, dport:80 ) { if (ip.dest!= my_web_servers) return; # now process the packet; we just write out packet info record system.time, ip.src, ip.dest to } www_list = recorder( log )

35 Distributed Host Based IDS

36 Combining Sources: DIDS Neither network based nor host based monitoring sufficient to detect some attacks Attacker tries to telnet into system several times using different account names: network based IDS detects this, but not host based monitor Attacker tries to log into system using an account without password: host based IDS detects this, but not network based monitor DIDS uses agents on hosts being monitored, and a network monitor DIDS director uses expert system to analyze data

37 Attackers Moving in Network Intruder breaks into system A as alice Intruder goes from A to system B, and breaks into B s account bob Host based mechanisms cannot correlate these DIDS director could see bob logged in over alice s connection; expert system infers they are the same user Assigns network identification number NID to this user

38 Handling Distributed Data Agent analyzes logs to extract entries of interest Agent uses signatures to look for attacks Summaries sent to director Other events forwarded directly to director DIDS model has agents report: Events (information in log entries) Action, domain

39 Distributed Host Based IDS

40 Network Based IDS network based IDS (NIDS) monitor traffic at selected points on a network in (near) real time to detect intrusion patterns may examine network, transport and/or application level protocol activity directed toward systems comprises a number of sensors inline (possibly as part of other net device) passive (monitors copy of traffic)

41 NIDS Sensor Deployment

42 Intrusion Detection Techniques signature detection at application, transport, network layers; unexpected application services, policy violations anomaly detection of denial of service attacks, scanning, worms when potential violation detected sensor sends an alert and logs information used by analysis module to refine intrusion detection parameters and algorithms by security admin to improve protection

43 Intrusion Detection Exchange Format

44 Honeypots are decoy systems filled with fabricated info instrumented with monitors / event loggers divert and hold attacker to collect activity info without exposing production systems initially were single systems more recently are/emulate entire networks

45 Honeypot Deployment

46 SNORT lightweight IDS real time packet capture and rule analysis passive or inline

47 SNORT Rules use a simple, flexible rule definition language with fixed header and zero or more options header includes: action, protocol, source IP, source port, direction, dest IP, dest port many options example rule to detect TCP SYN FIN attack: Alert tcp $EXTERNAL_NET any > $HOME_NET any \ (msg: "SCAN SYN FIN"; flags: SF, 12; \ reference: arachnids, 198; classtype: attempted recon;)