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Towards a High speed Router based Anomaly/Intrusion detection System Yan Gao & Zhichun Li.

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Presentation on theme: "Towards a High speed Router based Anomaly/Intrusion detection System Yan Gao & Zhichun Li."— Presentation transcript:

1 Towards a High speed Router based Anomaly/Intrusion detection System Yan Gao & Zhichun Li

2 Outline Motivation Related works Our approach Elementary evaluation result Ongoing work

3 Motivation attacks are commonplace, identifying them rapidly and accurately is critical Existing network intrusion detection systems (IDS) are insufficient for the following two reasons. First, they are mostly host-based or located on low-end routers, and not scalable to high-speed networks. Second, most of the existing approaches are signature based, which cannot detect unknown network attacks.

4 Motivation (cont.) Statistical IDS Most of them based on overall traffic, cannot do further migration even they find some anomalies. Most of them cannot detect SYN flooding and different types of port scans simultaneously Call for High Speed network detection solution (DARPA research agenda)

5 Related works SYN flooding detection Change point monitoring (CPM) Port Scan detection Threshold Random work (TRW) Very Fast Containment of Scanning Worms Both Partial Completion Filters (PCF)

6 Our approach – threat model Target SYN flooding Port scan Horizontal scan Vertical scan Metrics SYN – SYN/ACK SYN – FIN IP header fields DIP, SIP, Dport, Sport

7 Our approach – System design Reversible Sketch Forecast model (EMWA, Holt-winter) 2D Sketch

8 Our approach – reversible sketch RS(Key, value) 1 j H 01K-1 … … … hj(k)hj(k) hH(k)hH(k) h1(k)h1(k)

9 Our approach – detection algorithm RS((DIP, Dport), SYN-SYN/ACK) RS((SIP, DIP), SYN-SYN/ACK) RS((SIP, Dport), SYN-SYN/ACK) Attack typesRS((DIP, Dport), SYN-SYN/ACK) RS((SIP, DIP), SYN-SYN/ACK) RS((SIP, Dport), SYN-SYN/ACK) SYN floodingYes Vertical scansNoYesNo Horizontal scansNo Yes

10 2D sketch Structure of 2D sketch Example UPDATE

11 Elementary evaluation result Threshold – Knee point Data Set – NU data 536M flow (3.48TB) Sketch vs. Non-Sketch (FP=2.34%, FN=0.5%) Speed test (11M insertion/seconds) Memory consumption (9MB) Validation with other approaches

12 Ongoing work IP Mangling of RevSketch Reduce false positive Validation with other approaches Manual validation Holt-Winter result


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