John Heidemann

ASM Rizvi, John Heidemann

Services on the public Internet are frequently scanned, then subject to brute-force and denial-of-service attacks. We would like to run such services stealthily, available to friends but hidden from adversaries. In this work, we propose a moving target defense named "Chhoyhopper" that utilizes the vast IPv6 address space to conceal publicly available services. The client and server to hop to different IPv6 addresses in a pattern based on a shared, pre-distributed secret and the time-of-day. By hopping over a /64 prefix, services cannot be found by active scanners, and passively observed information is useless after two minutes. We demonstrate our system with SSH, and show that it can be extended to other applications.

Lan Wei, John Heidemann

DNS is important in nearly all interactions on the Internet. All large DNS operators use IP anycast, announcing servers in BGP from multiple physical locations to reduce client latency and provide capacity. However, DNS is easy to spoof: third parties intercept and respond to queries for benign or malicious purposes. Spoofing is of particular risk for services using anycast, since service is already announced from multiple origins. In this paper, we describe methods to identify DNS spoofing, infer the mechanism being used, and identify organizations that spoof from historical data. Our methods detect overt spoofing and some covertly-delayed answers, although a very diligent adversarial spoofer can hide. We use these methods to study more than six years of data about root DNS servers from thousands of vantage points. We show that spoofing today is rare, occurring only in about 1.7% of observations. However, the rate of DNS spoofing has more than doubled in less than seven years, and it occurs globally. Finally, we use data from B-Root DNS to validate our methods for spoof detection, showing a true positive rate over 0.96. B-Root confirms that spoofing occurs with both DNS injection and proxies, but proxies account for nearly all spoofing we see.

Hang Guo, Xun Fan, Anh Cao et al.

Machine-learning-based anomaly detection (ML-based AD) has been successful at detecting DDoS events in the lab. However published evaluations of ML-based AD have used only limited data and provided minimal insight into why it works. To address limited evaluation against real-world data, we apply autoencoder, an existing ML-AD model, to 57 DDoS attack events captured at 5 cloud IPs from a major cloud provider. We show that our models detect nearly all malicious flows for 2 of the 4 cloud IPs under attack (at least 99.99%) and detect most malicious flows (94.75% and 91.37%) for the remaining 2 IPs. Our models also maintain near-zero false positives on benign flows to all 5 IPs. Our primary contribution is to improve our understanding for why ML-based AD works on some malicious flows but not others. We interpret our detection results with feature attribution and counterfactual explanation. We show that our models are better at detecting malicious flows with anomalies on allow-listed features (those with only a few benign values) than flows with anomalies on deny-listed features (those with mostly benign values) because our models are more likely to learn correct normality for allow-listed features. We then show that our models are better at detecting malicious flows with anomalies on unordered features (that have no ordering among their values) than flows with anomalies on ordered features because even with incomplete normality, our models could still detect anomalies on unordered feature with high recall. Lastly, we summarize the implications of what we learn on applying autoencoder-based AD in production: training with noisy real-world data is possible, autoencoder can reliably detect real-world anomalies on well-represented unordered features and combinations of autoencoder-based AD and heuristic-based filters can help both.