[NeurIPS'23] H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models.

Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html

Reinforcement Learning (RL)-based routing algorithm for SDN networks created from scratch using Python.

Heavy-Hitter detection in P4 switch ASIC using Inter-Packet Gap

Sliding-window and regular top-K sketches, based on HeavyKeeper

Implementation for - Mitigating DNS random subdomain DDoS attacks by distinct heavy hitters sketches

Mastic: Private Weighted Heavy-Hitters and Attribute-Based Metrics

A novel approach based on Random Forests that quickly and accurately identifies elephant flows in programmable Data Planes.

Cuckoo Heavy Keeper and the balancing act of maintaining heavy hitters in stream processing

Source code of "Network-Wide Routing-Oblivious Heavy Hitters" paper by Ran Ben Basat, Gil Einziger, Shir Landau Feibish, Jalil Moraney, and Danny Raz (ACM/IEEE ANCS 2018).

Code for "PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries"

Efficient Flow Recording with InheritSketch on Programmable Switches (ICDCS2023)

Demo app for the https://github.com/keilerkonzept/topk sliding top-k sketch library

Implementation of streaming algorithms (Misra-Gries & Lossy Counting) for getting frequent items from data streams.

Comparing the Double Space Saving DS to the Heavy Keeper DS for the real world input streams

Implements and evaluates TrieHH and SFP heavy-hitter algorithms under local differential privacy, including full data preprocessing, simulation, and F1-score analysis on Wikipedia Clickstream. Designed for reproducible experiments and privacy–utility benchmarking.

PoC (Proof of Concept) caching optimization algorithm for graphical tree environment, written entirely in pure C++.

A scalable and efficient real-time IP anomaly detection system leveraging Bloom Filters and the Misra-Gries Algorithm to identify malicious traffic, detect heavy hitters, and dynamically update blacklists with minimal memory usage

Python pipeline for analyzing firewall/IDS CSV logs: core traffic stats, sublinear-space estimators (distinct IPs, heavy hitters, Bloom filters), and anomaly/threat detection. Compares approximate vs exact baselines with target ≤10% error, plus risk reporting and visuals.

My Thesis on the data structure Count-Min Sketch and it's applications.