Despite the robust structure of the Internet, it is still susceptible to disruptive routing updates that prevent network traffic from reaching its destination. In this work, we propose a method for early detection of large-scale disruptions based on the analysis of bursty BGP announcements. We hypothesize that the occurrence of large-scale disruptions is preceded by bursty announcements. Our method is grounded in analysis of changes in the inter-arrival times of announcements. BGP announcements that are associated with disruptive updates tend to occur in groups of relatively high frequency, followed by periods of infrequent activity. To test our hypothesis, we quantify the burstiness of inter-arrival times around the date and times of three large-scale incidents: the Indosat hijacking event in April 2014, the Telecom Malaysia leak in June 2015, and the Bharti Airtel Ltd. hijack in November 2015. We show that we can detect these events several hours prior to when they were originally detected. We propose an algorithm that leverages the burstiness of disruptive updates to provide early detection of large-scale malicious incidents using local collector data. We describe limitations, open challenges, and how this method can be used for large-scale routing anomaly detection.
Border Gateway Protocol (BGP) plays a critical role in the Internet inter-domain routing reliability. Invalid routes generated by mis-configurations or forged by malicious attacks may hijack the traffic and devastate the Internet routing system, but it is unlikely that a secure BGP can be deployed in the near future to completely prevent them. Although many hijacking detection systems have been developed, they more or less have weaknesses such as long detection delay, high false alarm rate and deployment difficulty, and no systematic detection results have been studied. This paper proposes Argus, an agile system that can accurately detect prefix hijackings and deduce the underlying cause of route anomalies in a very fast way. Argus is based on correlating the control and data plane information closely and pervasively, and has been continuously monitoring the Internet for more than one year. During this period, around 40K routing anomalies were detected, from which 220 stable prefix hijackings were identified. Our analysis on these events shows that, hijackings that have only been theoretically studied before do exist in the Internet. Although the frequency of new hijackings is nearly stable, more specific prefixes are hijacked more frequently. Around 20 of the hijackings last less than ten minutes, and some can pollute 90 of the Internet in less than two minutes. These characteristics make especially useful in practice. We further analyze some representative cases in detail to help increase the understanding of prefix hijackings in the Internet. We present novel and practical techniques to accurately detect IP prefix hijacking attacks in real time to facilitate mitigation. Attacks may hijack victim's address space to disrupt network services or perpetrate malicious activities such as spamming and DoS attacks without disclosing identity. We propose novel ways to significantly improve the detection accuracy by combining analysis of passively collected BGP routing updates with data plane fingerprints of suspicious prefixes. The key insight is to use data plane information in the form of edge network fingerprinting to disambiguate suspect IP hijacking incidences based on routing anomaly detection. Conflicts in data plane fingerprints provide much more definitive evidence of successful IP prefix hijacking. Utilizing multiple real-time BGP feeds, we demonstrate the ability of our system to distinguish between legitimate routing changes and actual attacks. Strong correlation with addresses that originate spam emails from a spam honeypot confirms the accuracy of our techniques. 