1. Jennifer Rexford Fall 2010 (TTh 1:30-2:50 in COS 302) COS 561: Advanced Computer Networks http://www.cs.princeton.edu/courses/archive/fall10/cos561/ Detecting Abnormal Traffic

2. Intrusions –Actions that attempt to bypass security mechanisms –E.g., unauthorized access, inflicting harm, etc. Example intrusions –Denial-of-service attacks –Scans –Worms and viruses –Host compromises Intrusion detection –Monitoring and analyzing traffic –Identifying abnormal activities –Assessing severity and raising alarms 2

3. Where to Detect Intrusions? End host: OS or application –Includes logins, file I/O, program executions, etc. –Can work with encrypted traffic and at lower speeds –Avoid extra packet reassembly and ambiguity Network: at enterprise edge –Single location for detecting and blocking attacks –Avoid reliance on the end host, OS, user,.. –Reduce overhead on the end host and network Network: in the backbone –Multiple locations for detecting correlations in traffic –Too expensive to inspect the contents of every packet 3

4. What to Do After Detection? Passively log –Collect, analyze, and record data –Report results to system/network administrator –Allow human to drive any response –Slower response, but better handling of false positives Actively defend –Detect problems in real time –Automatically generate a response –E.g., drop the traffic, engage the adversary, fight back –Faster response, but worse handling of false positives 4

5. How to Detect Intrusions? The challenge –We don’t know all the bad things that could happen –And telling good from bad is fundamentally hard Anomaly detection –What is usual, is known –What is unusual, is bad Signature detection –What is bad, is know –What is not bad, is good 5

6. How to Detect Intrusions? Anomaly DetectionSignature Detection PatternsTrain to create a baseline of normal network traffic Codify patterns of known vulnerabilities or attacks DetectionDetect statistically significant deviations from normal Detect matches to the patterns in the signatures ProsCan detect novel (“zero day”) attacks Builds on past experiences ConsMay miss low-rate attacks; high rate of false alarms Misses novel attacks; requires continuous updates to signatures 6

7. Anomaly Detection Traffic volume –Detect deviations in bytes/sec or packets/sec over time –Not effective for detection low-volume attacks Traffic features –Detect changes in distributions of traffic characteristics –E.g., traffic distribution by IP address, port number, packet size, TCP flags, etc. –Aids in classifying the anomaly (e.g., DoS vs. port scan) Detection techniques –Statistical techniques –Machine learning –... 7

8. Signature Detection Examples –Excessive login attempts –TCP packet with both SYN and RST set –HTTP with “GET /cgi-bin/phf?” Packet processing –Deep-packet inspection –Regular expression matching 8 alert tcp $EXTERNAL any -> $HTTP_SERVERS $HTTP_PORTS (msg: “WEB-CGI phf access”; flow:to_server, established; uricontent: “\phf”; nocase; reference: bugtraq, 629; reference: arachnids, 128; reference: cve, CVE-1999-0067; classtype: web-application-activity; sid: 886; rev: 8;)

9. Hybrid Solution: Arbor Example 9 scrubber http://www.arbornetworks.com/en/stopping-ddos-attacks.html anomaly detection

10. Challenges Accuracy –False positives –False negatives High volume of data –Measuring the traffic at line rate –Real-time analysis of packet streams –Deep-packet inspection –Connection reassembly Attackers gaming the monitoring system –Splitting offending content across multiple packets –Overloading the monitor –Exploiting ambiguity in which packet the receiver uses 10

11. Discussion 11

12. Network Security Denial-of-service mitigation –Pushback and source quenching –Secure Overlay Services Spam mitigation –Network-based detection of spam Protecting against bogus DNS and certificates –Comparing results across multiple clients –DNS-SEC End-to-end encryption: SSL and IPSEC Clean-slate approaches –Self-certifying addresses –“Off by default” –Path verification 12

13. Traffic Measurement

14. Why is Traffic Measurement Important? Billing the customer –Measure usage on links to/from customers –Applying billing model to generate a bill Traffic engineering and capacity planning –Measure the traffic matrix (i.e., offered load) –Tune routing protocol or add new capacity Denial-of-service attack detection –Identify anomalies in the traffic –Configure routers to block the offending traffic Analyze application-level issues –Evaluate benefits of deploying a Web caching proxy –Quantify fraction of traffic that is P2P file sharing

15. Collecting Traffic Data: SNMP Simple Network Management Protocol –Standard Management Information Base (MIB) –Protocol for querying the MIBs Advantage: ubiquitous –Supported on all networking equipment –Multiple products for polling and analyzing data Disadvantages: dumb –Coarse granularity of the measurement data  E.g., number of byte/packet per interface per 5 minutes –Cannot express complex queries on the data –Unreliable delivery of the data using UDP

16. Collecting Traffic Data: Packet Monitoring Packet monitoring –Passively collecting IP packets on a link –Recording IP, TCP/UDP, or application-layer traces Advantages: details –Fine-grain timing information  E.g., can analyze the burstiness of the traffic –Fine-grain packet contents  Addresses, port numbers, TCP flags, URLs, etc. Disadvantages: overhead –Hard to keep up with high-speed links –Often requires a separate monitoring device

17. Collecting Traffic Data: Flow Statistics Flow monitoring (e.g., Cisco Netflow) –Statistics about groups of related packets (e.g., same IP/TCP headers and close in time) –Recording header information, counts, and time Advantages: detail with less overhead –Almost as good as packet monitoring, except no fine- grain timing information or packet contents –Often implemented directly on the interface card Disadvantages: trade-off detail and overhead –Less detail than packet monitoring –Less ubiquitous than SNMP statistics

18. Using the Traffic Data in Network Operations SNMP byte/packet counts: everywhere –Tracking link utilizations and detecting anomalies –Generating bills for traffic on customer links –Inference of the offered load (i.e., traffic matrix) Packet monitoring: selected locations –Analyzing the small time-scale behavior of traffic –Troubleshooting specific problems on demand Flow monitoring: selective, e.g,. network edge –Tracking the application mix –Direct computation of the traffic matrix –Input to denial-of-service attack detection

19. Flow Measurement

20. Flow Measurement: Outline Definition –Passively collecting statistics about groups of packets –Group packets based on headers and spacing in time –Essentially a way to aggregate packet measurement data Scope –Medium-grain information about user behavior –Passively monitoring the link or the interface/router –Helpful in characterizing, detecting, diagnosing, and fixing Outline –Definition of an IP “flow” (sequence of related packets) –Flow measurement data and its applications –Mechanics of collecting flow-level measurements –Reducing the overheads of flow-level measurement

21. flow 1flow 2flow 3 flow 4 IP Flows Set of packets that “belong together” –Source/destination IP addresses and port numbers –Same protocol, ToS bits, … –Same input/output interfaces at a router (if known) Packets that are “close” together in time –Maximum spacing between packets (e.g., 30 sec) –Example: flows 2 and 4 are different flows due to time

22. Flow Abstraction A flow is not exactly the same as a “session” –Sequence of related packets may be multiple flows (due to the “close together in time” requirement) –Sequence of related packets may not follow the same links (due to changes in IP routing) Motivation for this abstraction –As close to a “session” as possible from inside the network –Flow switching paradigm from IP-over-ATM technology –Router optimization for forwarding/ACL decisions (cache the result after the first packet in a flow) –… might as well throw in a few counters

23. Recording Traffic Statistics (e.g., Netflow) Packet header information (same for every packet) –Source and destination IP addresses –Source and destination TCP/UDP port numbers –Other IP/TCP/UDP header fields (protocol, ToS bits, etc.) Aggregate traffic information (summary of traffic) –Start/finish time of the flow (time of first & last packet) –Total number of bytes and number of packets in the flow –TCP flags (e.g., logical OR over sequence of packets) startfinish 4 packets 1436 bytes SYN, ACK, & FIN SYN ACK FIN

24. Recording Routing Info (e.g., Netflow) Input and output interfaces –Input interface is where the packets entered the router –Output interface is the “next hop” in the forwarding table Source and destination IP prefix (mask length) –Longest prefix match on the src and dest IP addresses Source and destination ASnumbers –Origin AS for src/dest prefix in the BGP routing table Switching Fabric Processor Line card BGP table forwarding table

25. Measuring Traffic as it Flows By input output source AS source prefix source dest AS dest prefix dest intermediate AS Source and destination: IP header Source and dest prefix: forwarding table or BGP table Source and destination AS: BGP table

26. Packet vs. Flow Measurement Basic statistics (available from both techniques) –Traffic mix by IP addresses, port numbers, and protocol –Average packet size Traffic over time –Both: traffic volumes on a medium-to-large time scale –Packet: burstiness of the traffic on a small time scale Statistics per TCP connection –Both: number of packets & bytes transferred over the link –Packet: frequency of lost or out-of-order packets, and the number of application-level bytes delivered Per-packet info (available only from packet traces) –TCP seq/ack #s, receiver window, per-packet flags, … –Probability distribution of packet sizes –Application-level header and body (full packet contents)

27. Collecting Flow Measurements Router A Route CPU that generates flow records …may degrade forwarding performance Router A Line card that generates flow records …more efficient to support measurement in each line card Router ARouter B Monitor Packet monitor that generates flow records …third party CPU

28. Router Collecting Flow Measurement Advantage –No need for separate measurement device(s) –Monitor traffic over all links in/out of router (parallelism) –Ease of providing routing information for each flow Disadvantage –Requirement for support in the router product(s) –Danger of competing with other 1st-order router features –Possible degradation of the throughput of the router –Difficulty of online analysis/aggregation of data on router Practical application –View from multiple vantage points (e.g., all edge links)

29. Packet Monitor Collecting Flow Records Advantages –No performance impact on packet forwarding –No dependence on support by router vendor –Possibility of customizing the thinning of the data Disadvantages –Overhead/cost of tapping a link & reconstructing packets –Cost of buying, deploying, and managing extra equipment –No access to routing info (input/output link, IP prefix, etc.) Practical application –Selective monitoring of a small number of links –Deployment in front of particular services or sites Packet monitor vendors support flow-level output

30. Mechanics: Flow Cache Maintain a cache of active flows –Storage of byte/packet counts, timestamps, etc. Compute a key per incoming packet –Concatenation of source, destination, port #s, etc. Index into the flow cache based on the key –Creation or updating of an entry in the flow cache #bytes, #packets, start, finish packet key heade r key

31. Mechanics: Evicting Cache Entries Flow timeout –Remove flows that have not received a packet recently –Periodic sequencing through the cache to time out flows –New packet triggers the creation of a new flow Cache replacement –Remove flow(s) when the flow cache is full –Evict existing flow(s) upon creating a new cache entry –Apply eviction policy (LRU, random flow, etc.) Long-lived flows –Remove flow(s) that persist for a long time (e.g., 30 min) –… otherwise flow statistics don’t become available –… and the byte and packet counters might overflow

32. Sampling: Packet Sampling Packet sampling before flow creation (Sampled Netflow) –1-out-of-m sampling of individual packets (e.g., m=100) –Create of flow records over the sampled packets Reducing overhead –Avoid per-packet overhead on (m-1)/m packets –Avoid creating records for a large number of small flows Increasing overhead (in some cases) –May split some long transfers into multiple flow records –… due to larger time gaps between successive packets time not sampled two flows timeout

33. Conclusions Flow measurement –Medium-grain view of traffic on one or more links Advantages –Lower measurement volume than full packet traces –Available on high-end line cards (Cisco Netflow) –Control over overhead via aggregation and sampling Disadvantages –Computation and memory requirements for flow cache –Loss of fine-grain timing and per-packet information –Not uniformly supported by router vendors