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   Sohrabizadeh, Atefeh; Wang, Jie; Cong, Jason (February 2020, Proceedings of the 2020 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA ’20), February 23–25, 2020, Seaside, C)
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3. End-to-End Measurements of Email Spoofing Attacks

   Hu, Hang; Wang, Gang (August 2018, The 27th USENIX Security Symposium (USENIX Security'18))
4. Implementation of an end-to-end gradual verification system

   https://doi.org/10.1145/3484271.3484980  

   Gouni, Hemant; Zimmerman, Conrad (October 2021, SPLASH Companion ’21, October 17–22, 2021, Chicago, IL, USA)

   Static verification is used to ensure the correctness of programs. While useful in critical applications, the high overhead associated with writing specifications limits its general applicability. Similarly, the run-time costs introduced by dynamic verification limit its practicality. Gradual verification validates partially specified code statically where possible and dynamically where necessary. As a result, software developers gain granular control over the trade-offs between static and dynamic verification. This paper contains an end-to-end presentation of gradual verification in action, with a focus on applying it to 𝐶0 (a safe subset of C) and implementing the required dynamic verification. 

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5. Reassessing the Constancy of End-to-End Internet Latency

   Davisson, Lily; Jakovleski, Joakim; Ngo, Nhiem; Pham, Chau; Sommers, Joel (September 2021, IFIP Network Traffic Measurement and Analysis Conference)

   A paper by Zhang et al. in 2001, “On the Constancy of Internet Path Properties” [1] examined the constancy of end- to-end packet loss, latency, and throughput using a modest set of hosts deployed in the Internet. In the time since that work, the Internet has changed dramatically, including the flattening of the autonomous system hierarchy and increased deployment of IPv6, among other developments. In this paper, we investigate the constancy of end-to-end Internet latency, revisiting findings of the earlier study. We use latency measurements from RIPE Atlas, choosing a set of 124 anchors with broad geographic distribution and drawn from 112 distinct autonomous systems. The earlier work of Zhang et al. relies on changepoint detection methods to identify mathematically constant time periods. We reimplement the two methods described in that earlier work and use them on the RIPE Atlas latency measurements. We also use a recently- published method (HMM-HDP) that has direct support in a RIPE Atlas API. Comparing the three changepoint detection methods, we find that the two methods used in the earlier work may miss many changepoints caused by common level-shift events. Overall, we find that the recently proposed HMM-HDP method performs substantially better. Moreover, we find that delay spikes—as defined by the earlier work—are an order of magnitude less prevalent than 20 years ago. We also find that maximum change- free regions (CFRs) along paths that we observe in today’s Internet are substantially longer than what was observed in 2001, regardless of the changepoint detection method used. In particular, the 50th percentile maximum CFR was on the order of 30 minutes in the earlier study, but our analysis reveals it to be on the order of 3 days or longer. Moreover, we find that CFR durations appear to have steadily increased over the past 5 years. 

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