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Network Security Lab

Explore innovative methods like DefCOM and WIN for detecting and mitigating Distributed Denial of Service attacks, simulating Internet Worms, and implementing cooperative defense mechanisms in network security research. Learn to differentiate attack traffic, predict routing changes, and summarize firewall logs to enhance cybersecurity protocols.

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Network Security Lab

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  1. Network Security Lab Jelena Mirkovicsunshine@cis.udel.edu Sig NewGrad presentantion

  2. Main Research Areas • Distributed Denial of Service • Distributed defense: DefCOM • Internet Worms • Worm simulation: PAWS • Cooperative defense: WIN • Detecting new malicious executables • Application-level Honeynets, summarizing firewall logs, predicting routing changes …

  3. Distributed Denial of Service

  4. Distributed Denial of Service

  5. Distributed Denial of Service Ideal solution! Too much traffic Attack traffic looks likelegitimate

  6. Distributed Denial of Service Stop attack Detect attack Differentiate betweenattack and legitimate traffic

  7. DefCOM • Distributed defense against DDoS • Combines nodes at: • Victim – Alert generators: detect attack and alert other nodes • Core – Rate limiters: stop attack by dropping traffic • Source – Classifiers: differentiate between legitimate and attack traffic • Nodes cooperate through an overlay

  8. DefCOM C RL Attack! AG RL C 1. Attack detection

  9. DefCOM I see mark 5! mark = 5 mark 56 C RL I see marks 12 and 56! AG I see mark 3! RL mark = 12 C mark = 3 2. Forming the traffic tree

  10. DefCOM C RL AG RL C 2. Forming the traffic tree

  11. 50Mbps 50Mbps 50Mbps 50Mbps DefCOM C RL AG 100Mbps RL C 3. Distributed rate-limiting

  12. 50Mbps 50Mbps 50Mbps 50Mbps DefCOM L=6 M=20 L=4 M=25 C RL AG 100Mbps RL C L=33 M=17 L=76 M=43 4. Traffic differentiation

  13. 50Mbps 50Mbps 50Mbps 50Mbps DefCOM L=6 M=20 L=4 M=25 C RL AG 100Mbps RL C L=33 M=17 L=76 M=43 4. Traffic differentiation

  14. Internet Worms • A program that: • Scans network for vulnerable machines • Breaks into machines by exploiting the found vulnerability • Installs some piece of malicious code – backdoor, DDoS tool • Moves on • Don’t need any user action to spread • Spread very fast!

  15. PAWS • Parallel worm simulator • Runs on multiple machines – gain memory and CPU resources • Can simulate greater detail than single-node simulators • Can simulate various defenses • Machines synchronize with network messages

  16. WIN • Worm information network • We need fast, automatic response to stop worms • How can we detect worms • How can we devise signatures quickly and automatically • How can we share signatures with other networks • How can we accept signatures from others and be sure we won’t filter out legitimate traffic

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