Towards a collaborative, flow-based, distributed inter-domain Intrusion Detection System
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1 Towards a collaborative, flow-based, distributed inter-domain Intrusion Detection System Frank Tietze Institut für Technische Informatik Fakultät für Informatik frank.tietze@unibw.de 1
2 Structure Introduction Intrusion Detection on Flows Detectable attacks with knowledge-based s on Flows Research approach Hoped-for improvements Impact / Issues on RFC
3 Introduction Definition of Flow within our approach: See RFC 3954: NetFlow V9 An IP Flow, also called a Flow, is defined as a set of IP packets passing an Observation Point in the network during a certain time interval. All packets that belong to a particular Flow have a set of common properties derived from the data contained in the packet and from the packet treatment at the Observation Point. 3
4 Introduction Generation of Flow (classical architecture): See RFC 3917 / 5101: IPFIX and RFC 3954: NetFlow V9 Aspects important for ID: Reliability Overload Behavior Security (transfer) Sampling Time (stamps & sync) Anonymization / Pseudonymization Network Management OP and Exporter Collector Subsequent Activities Why Intrusion Detection on Flows? Encrypted Traffic ñ (Processability) Bandwidth ñ (Scalability) Complexity of attacks ñ (Detectability) Accounting Intrusion Detection 4
5 Intrusion Detection on Flows (I) What is Intrusion Detection? (NIST SP800-94) ID Management Security Policy RAW Data Event Generation Alert Generation & Correlation Countering Alerts Response (e.g. SNMP à FW-Ruleset) Event Events of interest (Alert + X) 5
6 Intrusion Detection on Flows (II) Intrusion Detection Flow-based Payload-based Knowledge-based Behavior-based + Good at known threats + Simple to configure - High false negative rate - Bad on multi-event attacks - Reactive method + Privacy due to no payload + easy to process - Loss of data (less information?) + Good at unknown / new threats + more sensitive in detection + Proactive method - High false positive rate - Hard to define normal -state + Privacy due to no payload + easy to process - Loss of data (less information?) + Good at known threats + Simple to configure - High false negative rate - Bad on multi-event attacks - Reactive method + No data is lost (more information) - Processing / performance - Complex algorithms + Good at unknown / new threats + more sensitive in detection + Proactive method - High false positive rate - Hard to define normal -state + No data is lost (more information) - Processing in learning phase - Complex algorithms 6
7 Detectable attacks with knowledge-based s on Flows DoS / DDoS Scans Worms Botnet-based Attacks data extraction via bots in internal network 7
8 Research Approach (I) Inter-domain between University of Twente (UT) and Universität der Bundeswehr München Exchange of domain-knowledge and Flows between partners (with comparable infrastructure / services) Integration of in Joint Security Labs (Flamingo + X) Combination of NetFlow- and IPFIX-components addressing shortcomings of RFC 3917 / 5101 IPFIX 8
9 Research Approach (II) Planned Architecture: Security Lab DMZ Security Lab IPSec-Tunnel (extern) (combined) NMS VPN-Proxy VPN-Proxy (configurable) OP and Collector Anonymizer Im- / Exporter Twente OP LAN Anonymizer Im- / Exporter NetFlow N Analyzer Munich OP and Collector (intern SecLab) NMN NMS 9
10 Research Approach (III) Planned Functionality: Security Lab Exchange NM- / Metering Data DMZ IPSec-Tunnel (extern) (combined) NMS VPN-Proxy VPN-Proxy (configurable) OP and Collector Anonymizer Im- / Exporter Twente Exchange Pseudonymized Exchange Flows pseudonymization profiles for services / devices OP LAN Anonymizer Im- / Exporter N Analyzer Munich OP and Collector (intern SecLab) NMN NMS 10
11 Research Approach (IV) Planned Functionality: Importer rewrites and filters external Flow - IPFIX Importer redirects external Flow to (extern) and Collector Collector redirects internal Flow to - NetFlow Collector combines internal and external Flow and redirects to (combined) - NetFlow s report alerts to N Analyzer N Analyzer reconfigures (configurable)- ruleset via NMS (configurable) analyzes internal Flow and alerts N Analyzer (Verfification of new ruleset) Anonymizer Im- / Exporter (extern) OP LAN (combined) OP and Collector (intern SecLab) N Analyzer Munich (configurable) NMN NMS 11
12 Research Approach (V) Possible results: Only internal reports alert: Ø Report alert with further information to UT Inbound: Attack in Munich! What about UT? Outbound: internal BotNet activity? C&C? P2P? Alert IDMEF - Alert N Analyzer Munich NMN NMS 12
13 Research Approach (VI) Possible results: Combined and extern reports alert: Ø Traffic from UT would have raised an alert in Munich Ø Report alert with further information to UT Ø Lower configurable detection rates Ø Analyze internal Flow at / around timeframe UT alert (extern) (combined) Inbound: Similar attack in Munich below radar? Outbound: internal BotNet activity? C&C? P2P? Alert (configurable) IDMEF - Alert N Analyzer Munich NMN NMS 13
14 Research Approach (VII) Possible results: Only combined reports alert: Ø Only combined Traffic raises an alert Ø Report alert with further information to UT Ø Tune configurable detection rates Ø Analyze internal Flow at timeframe alert (combined) Inbound: Verfication of the attack? Validation of the rulesets? Outbound: internal BotNet activity? (C&C? P2P?) Alert (configurable) IDMEF - Alert N Analyzer Munich NMN NMS 14
15 Hoped-for Improvements Lower false negative rates in knowledge-/flow-based concerning outbound-analysis while maintaining false positive rates Lower false negative rates in knowledge-/flow-based concerning inbound-analysis (!!! false positive rates!!!) Better Identification of Bots / Botnets (intern & extern) First step towards automated configuration over domainboundaries Better detection of Worm- and Botnet-Activity in European networks 15
16 Impact / Issues on RFC 3917 / 5101 Metering (5): 5.2. Sampling à Verification of output / Validation of input (Flow) 5.3. Overload Behavior à Due to NM- / Metering-data-Exchange for Infra 5.4. Timestamps à important for correlation frames (UTC) 5.5. Timesynchronization à important for correlation frames (UTC) Data Export (6): 6.1. Timestamps for first / last packet ICMP type & code IP / TCP header flags Confidentiality and Integrity 6.7. Anonymization / Pseudonomization Further Further Research: Verification / Validation of correlated inbound-flows Automated, loop-free, fail-save configuration (across domain-boundaries) Behavior-based inter-domain ID on Flows 16
17 17
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