39 citations · 42 across the 6 of their papers we have counts for
7 papers
Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data
Shrijita Bhattacharya, Michael Kallitsis, Stilian Stoev
We introduce a trimmed version of the Hill estimator for the index of a heavy-tailed distribution, which is robust to perturbations in the extreme order statistics. In the ideal Pa…
Trimming the Hill estimator: robustness, optimality and adaptivity
Shrijita Bhattacharya, Michael Kallitsis, Stilian Stoev
We introduce a trimmed version of the Hill estimator for the index of a heavy-tailed distribution, which is robust to perturbations in the extreme order statistics. In the ideal Pa…
AMON: An Open Source Architecture for Online Monitoring, Statistical Analysis and Forensics of Multi-gigabit Streams
Michael Kallitsis, Stilian Stoev, Shrijita Bhattacharya +1
The Internet, as a global system of interconnected networks, carries an extensive array of information resources and services. Key requirements include good quality-of-service and…
Hashing Pursuit for Online Identification of Heavy-Hitters in High-Speed Network Streams
Michael Kallitsis, Stilian Stoev, George Michailidis
Distributed Denial of Service (DDoS) attacks have become more prominent recently, both in frequency of occurrence, as well as magnitude. Such attacks render key Internet resources…
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles
Ali Shojaie, Alexandra Jauhiainen, Michael Kallitsis +1
Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference c…
A State-Space Approach for Optimal Traffic Monitoring via Network Flow Sampling
Michael Kallitsis, Stilian Stoev, George Michailidis
The robustness and integrity of IP networks require efficient tools for traffic monitoring and analysis, which scale well with traffic volume and network size. We address the probl…