Internet Anomaly Detection based on Complex Network Path

TL;DR

This paper introduces a novel Internet anomaly detection method based on complex network path analysis, utilizing the NPCC metric and a Fibonacci-inspired decision function to identify large-scale network failures and attacks with high accuracy.

Contribution

The paper proposes a new anomaly detection approach using NPCC and a Fibonacci-based decision function, improving detection accuracy for large-scale Internet anomalies.

Findings

Detection accuracy over 97% for tested events

Detection precision ranges from 66.67% to 90.24%

Better performance observed with larger k values

Abstract

Detecting the anomaly behaviors such as network failure or Internet intentional attack in the large-scale Internet is a vital but challenging task. While numerous techniques have been developed based on Internet traffic in past years, anomaly detection for structured datasets by complex network have just been of focus recently. In this paper, a anomaly detection method for large-scale Internet topology is proposed by considering the changes of network crashes. In order to quantify the dynamic changes of Internet topology, the network path changes coefficient(NPCC) is put forward which will highlight the Internet abnormal state after it is attacked continuously. Furthermore we proposed the decision function which is inspired by Fibonacci Sequence to determine whether the Internet is abnormal or not. That is the current Internet is abnormal if its NPCC is beyond the normal domain which structured by the previous k NPCCs of Internet topology. Finally the new Internet anomaly detection method was tested over the topology data of three Internet anomaly events. The results show that the detection accuracy of all events are over 97%, the detection precision of each event are 90.24%, 83.33% and 66.67%, when k = 36. According to the experimental values of the index F_1, we found the the better the detection performance is, the bigger the k is, and our method has better performance for the anomaly behaviors caused by network failure than that caused by intentional attack. Compared with traditional anomaly detection, our work may be more simple and powerful for the government or organization in items of detecting large-scale abnormal events.