arXiv subjects

John Heidemann

Publications and source records attributed to John Heidemann.

23 records · Page 2Linked to original sources

arXiv2022

Anycast Agility: Network Playbooks to Fight DDoS

IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may instead concentrate attackers in a few sites to preserve operation in others. Operators use these actions during attacks, but how to do so has not been described systematically or publicly. This paper describes several methods to use BGP to shift traffic when under DDoS, and shows that a response playbook can provide a menu of responses that are options during an attack. To choose an appropriate response from this playbook, we also describe a new method to estimate true attack size, even though the operator's view during the attack is incomplete. Finally, operator choices are constrained by distributed routing policies, and not all are helpful. We explore how specific anycast deployment can constrain options in this playbook, and are the first to measure how generally applicable they are across multiple anycast networks.

cs.NI↗

arXiv2021

Chhoyhopper: A Moving Target Defense with IPv6

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.

cs.CR↗

arXiv2021

Auditing for Discrimination in Algorithms Delivering Job Ads

Ad platforms such as Facebook, Google and LinkedIn promise value for advertisers through their targeted advertising. However, multiple studies have shown that ad delivery on such platforms can be skewed by gender or race due to hidden algorithmic optimization by the platforms, even when not requested by the advertisers. Building on prior work measuring skew in ad delivery, we develop a new methodology for black-box auditing of algorithms for discrimination in the delivery of job advertisements. Our first contribution is to identify the distinction between skew in ad delivery due to protected categories such as gender or race, from skew due to differences in qualification among people in the targeted audience. This distinction is important in U.S. law, where ads may be targeted based on qualifications, but not on protected categories. Second, we develop an auditing methodology that distinguishes between skew explainable by differences in qualifications from other factors, such as the ad platform's optimization for engagement or training its algorithms on biased data. Our method controls for job qualification by comparing ad delivery of two concurrent ads for similar jobs, but for a pair of companies with different de facto gender distributions of employees. We describe the careful statistical tests that establish evidence of non-qualification skew in the results. Third, we apply our proposed methodology to two prominent targeted advertising platforms for job ads: Facebook and LinkedIn. We confirm skew by gender in ad delivery on Facebook, and show that it cannot be justified by differences in qualifications. We fail to find skew in ad delivery on LinkedIn. Finally, we suggest improvements to ad platform practices that could make external auditing of their algorithms in the public interest more feasible and accurate.

cs.CY↗

arXiv2020

Whac-A-Mole: Six Years of DNS Spoofing

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.

cs.CR↗

arXiv2020

Peek Inside the Closed World: Evaluating Autoencoder-Based Detection of DDoS to Cloud

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.

cs.NI↗