Traditional DDoS attack detection monitors volumetric traffic features to detect attack onset. To reduce false positives, such detection is often conservative---raising an alert only after a sustained period of observed anomalous behavior. However, contemporary attacks tend to be short, which combined with a long detection delay means that most of the attack still reaches and impacts the victim. We propose Xatu, a system that utilizes auxiliary signals to improve the accuracy and timeliness of existing DDoS detection systems. We explore two types of auxiliary signals, attack preparation signals and the history of prior attacks. These signals can be easily mined from existing traffic monitoring systems in many ISP networks. To leverage these auxiliary signals for attack detection, we propose a multi-timescale LSTM model, which derives both long-term and short-term patterns from diverse auxiliary signals. We then leverage survival analysis to quickly detect attacks when they occur while minimizing false positives and thus scrubbing costs. We evaluate Xatu on traffic from a large ISP, using commercial defense alert data to label prevalent attack events. Xatu would help the commercial defense scrub up to 44.1% additional anomalous traffic and would reduce its median detection delay by 9.5 minutes.1

Programmable switches are driving a massive increase in fine-grained measurements. This puts significant pressure on telemetry collectors that have to process reports from many switches. Past research acknowledged this problem by either improving collectors' stack performance or by limiting the amount of data sent from switches. In this paper, we take a different and radical approach: switches are responsible for directly inserting queryable telemetry data into the collectors' memory, bypassing their CPU, and thereby improving their collection scalability. We propose to use a method we call direct telemetry access, where switches jointly write telemetry reports directly into the same collector's memory region, without coordination. Our solution, DART, is probabilistic, trading memory redundancy and query success probability for CPU resources at collectors. We prototype DART using commodity hardware such as P4 switches and RDMA NICs and show that we get high query success rates with a reasonable memory overhead. For example, we can collect INT path tracing information on a fat tree topology without a collector's CPU involvement while achieving 99.9% query success probability and using just 300 bytes per flow.

Blocklists, consisting of known malicious IP addresses, can be used as a simple method to block malicious traffic. However, blocklists can potentially lead to unjust blocking of legitimate users due to IP address reuse, where more users could be blocked than intended. IP addresses can be reused either at the same time (Network Address Translation) or over time (dynamic addressing). We propose two new techniques to identify reused addresses. We built a crawler using the BitTorrent Distributed Hash Table to detect NATed addresses and use the RIPE Atlas measurement logs to detect dynamically allocated address spaces. We then analyze 151 publicly available IPv4 blocklists to show the implications of reused addresses and find that 53-60% of blocklists contain reused addresses having about 30.6K-45.1K listings of reused addresses. We also find that reused addresses can potentially affect as many as 78 legitimate users for as many as 44 days.

Commodity network devices support adding in-band telemetry measurements into data packets, enabling a wide range of applications, including network troubleshooting, congestion control, and path tracing. However, including such information on packets adds significant overhead that impacts both flow completion times and application-level performance.

Volumetric distributed denial-of-service (DDoS) attacks can bring any network to a halt. Because of their distributed nature and high volume, the victim often cannot handle these attacks alone and needs help from upstream ISPs. Today's Internet has no automated mechanism for victims to ask ISPs for help in attack handling and ISPs themselves do not offer such services. We propose SENSS, a security service for collaborative mitigation of volumetric DDoS attacks. SENSS enables the victim of an attack to request attack monitoring and filtering on demand, and to pay for the services rendered. Requests can be sent both to the immediate and to remote ISPs, in an automated and secure manner, and can be authenticated by these ISPs, without having prior trust with the victim. Simple and generic SENSS APIs enable victims to build custom detection and mitigation approaches against a variety of DDoS attacks. SENSS is deployable with today's infrastructure, and it has strong economic incentives both for ISPs and for the attack victims. It is also very effective in sparse deployment, offering full protection to direct customers of early adopters, and considerable protection to remote victims when deployed strategically. Deployment on the largest 1% of ISPs protects not just direct customers of these ISPs, but everyone on the Internet, from 90% of volumetric DDoS attacks.