STATISTICAL TECHNIQUES FOR DETECTING TRAFFIC ANOMALIES THROUGH PACKET HEADER DATA

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ABSTRACT
This paper proposes a traffic anomaly detector, operated in postmortem and in real-time, by passively monitoring packet headers of traffic. The frequent attacks on network infrastructure, using various forms of denial of service attacks, have led to an increased need for developing techniques for analyzing network traffic. If efficient analysis tools were available, it could become possible to detect the attacks, anomalies and to take action to contain the attacks appropriately before they have had time to propagate across the network. In this paper, we suggest a technique for traffic anomaly detection based on analyzing correlation of destination IP addresses in outgoing traffic at an egress router. This address correlation data are transformed using discrete wavelet transform for effective detection of anomalies through statistical analysis. Results from trace-driven evaluation suggest that proposed approach could provide an effective means of detecting anomalies close to the source. We also present a multidimensional indicator using the correlation of port numbers and the number of flows as a means of detecting anomalies.

TABLE OF CONTENT
TITLE PAGE
CERTIFICATION
APPROVAL
DEDICATION
ACKNOWLEDGEMENT
ABSTRACT
TABLE OF CONTENT

CHAPTER ONE
1.0   INTRODUCTION
1.1   STATEMENT OF PROBLEM
1.2   PURPOSE OF STUDY
1.3   AIMS AND OBJECTIVES
1.4   SCOPE/DELIMITATIONS
1.5   LIMITATIONS/CONSTRAINTS
1.6   DEFINITION OF TERMS

CHAPTER TWO
2.0 LITERATURE REVIEW

CHAPTER THREE
3.0   METHODS FOR FACT FINDING AND DETAILED DISCUSSIONS OF THE SYSTEM
3.1    METHODOLOGIES FOR FACT-FINDING
3.2   DISCUSSIONS

CHAPTER FOUR
4.0   FUTURES, IMPLICATIONS AND CHALLENGES OF THE SYSTEM
4.1   FUTURES
4.2   IMPLICATIONS
4.3   CHALLENGES

CHAPTER FIVE
5.0   RECOMMENDATIONS, SUMMARY AND CONCLUSION
5.1   RECOMMENDATION
5.2   SUMMARY
5.3   CONCLUSION
5.4   REFERENCES

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STATISTICAL TECHNIQUES FOR DETECTING TRAFFIC ANOMALIES THROUGH PACKET HEADER DATA

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