The document outlines the development of a cloud-based intrusion detection system (IDS) analysis system, named ICAS, aimed at improving alert management and analysis efficiency. It proposes using cloud computing technologies, such as Apache Hadoop and HBase, to process and store large volumes of IDS alerts, addressing issues like false positives and negative alerts. The conclusions suggest that ICAS provides a scalable and economical solution but highlights the need for further development and larger experiments.

1. Building a Cloud Computing Analysis System for Intrusion Detection System DATE:4/14/09 Wei-Yu Chen, Yao-Tsung Wang National Center for High-Performance Computing, Taiwan {waue,jazz}@nchc.org.tw

2. Taiwan Introduction

3. NCHC Introduction The NCHC is responsible for Taiwan’s cyberinfrasructure, R&D in HPC and networking applications 3 Business Units located in science parks to support local high tech industry The NCHC integrates information, engineering and scientific disciplines The NCHC provides computing, networking and storage services National Center for High-performance Computing

4. Outline Motivation The IDEA Architecture Procedure Results Pros and Cons Conclusions

5. Current Situation of IDS Intrusion Detection System IDS (IDS) Detecting unwanted attempts at accessing, manipulating or disabling of computer systems through Internet. IDS Detect Rate “ false positive” “ false negative” Accuracy = top mission of IDS ?

6. Alerts Alert is produced when IDS detect something as malicious. Two method of alert storage A Text Log -> terrible In Database -> mostly

7. What’s the problem about Alert ? Enormous Data  less Efficient Ignore the crucial information easily !!! Got Nothing if the database were crash

8. Our Motivation To resolve above problems come with huge amount of anomaly information generated by IDS

9. Our IDEA - ICAS ICAS, IDS Cloud Analysis System Applying Cloud Computing technique Improve higher performance of analysis reducing redundancy Merge relation

10. System Architecture ICAS Overview

11. System Architecture SNORT is an open source network intrusion prevention and detection system The most widely deployed intrusion detection Snort

12. System Architecture Apache Hadoop Core is a software platform that lets one easily write and run applications that process vast amounts of data. Inspired by Google's MapReduce and Google File System (GFS) papers Implements MapReduce and Hadoop Distributed File System (HDFS) Operates <key, value> pairs Hadoop

13. System Architecture HBase is the Hadoop database An open-source, distributed, column-oriented store modeled after the Google paper, BigTable HBase

14. System Architecture Regular Parser Parsing original snort log and transfer to HDFS (hadoop file system) Analysis Procedure Dispatch job if pool is not empty and insert the result into database Data Mapper <key, value> mapping Data Reducer <“key1”, value1…valueN> <“key2”, value1…valueN> Four Components

15. Program Procedure

16. Alert Integration Procedure

17. Key - Values The victim IP addresses A unique ID used to identify attack method in Snort rules The time when the attack was launghed TCP/IP protocol Attack was lunched from this port Victim ports The IP address where malicious one launghed attack

18. Alert Merge Example 6007,6008 5002 4077,5002 T5 T4 T1,T2,T3 53 443 80,443 tcp, udp tcp tcp Sip3,Sip4,Sip5 ,Sip6 D.D.O.S. Host_3 Sip1 Trojan Host_2 Sip1,Sip2 Trojan Host_1 Values Key T5 tcp 6008 53 Sip6 D.D.O.S Host_3 T5 udp 6007 53 Sip5 D.D.O.S Host_3 T5 tcp 6008 53 Sip4 D.D.O.S Host_3 T5 udp 6007 53 Sip3 D.D.O.S Host_3 T4 tcp 5002 443 Sip1 Trojan Host_2 T3 tcp 5002 443 Sip1 Trojan Host_1 T2 tcp 4077 80 Sip2 Trojan Host_1 T1 tcp 4077 80 Sip1 Trojan Host_1 Timestamp Packet Protocol Source Port Destination Port Source IP Attack Signature Destination IP

19. Experiment Environment Machine: CPU : Intel quad-core, Memory : 2g, OS : Linux : Ubuntu 8.04 server Software Hadoop : core 0.16.4 Hbase : 0.1.3 Java : 6 Alerts Data Sets MIT Lincoln Laboratory, Lincoln Lab Data Sets Computer Security group at UCDavis, tcpdump file

20. Experimental Result The Consuming Time of Each Number of Data Sets

21. Experimental Result Throughput Data Overall

22. Pros & Cons Legible Efficient Scalable Economical Reliable Non-realtime Latency immature

23. Conclusions v.s. Future Works ICAS supplies a efficient way to analyze and merge huge number of alerts based on cloud platform. Including more IDS logs The best final result is graphical Prepare more large-scale and complete experiment

24. Thank You ! & Question ? DATE:4/14/09