Ahmed ElSheikh's starred repositories
system-design-101
Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.
Prompt-Engineering-Guide
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
awesome-streamlit
The purpose of this project is to share knowledge on how awesome Streamlit is and can be
CloakQuest3r
Uncover the true IP address of websites safeguarded by Cloudflare & Others
offensive-ai-compilation
A curated list of useful resources that cover Offensive AI.
Time-series-prediction
tfts: Time Series Deep Learning Models in TensorFlow
StratosphereLinuxIPS
Slips, a free software behavioral Python intrusion prevention system (IDS/IPS) that uses machine learning to detect malicious behaviors in the network traffic. Stratosphere Laboratory, AIC, FEL, CVUT in Prague.
auto-attack
Code relative to "Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks"
mtad-gat-pytorch
PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).
streamlit-example
Example Streamlit app that you can fork to test out share.streamlit.io
SAITS
The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-series imputation (impute multivariate incomplete time series containing NaN missing data/values with machine learning). https://arxiv.org/abs/2202.08516
Kitsune-py
A network intrusion detection system based on incremental statistics (AfterImage) and an ensemble of autoencoders (KitNET)
TimeEval-algorithms
Time series anomaly detection algorithm implementations for TimeEval (Docker-based)
SpectralAdversarialDefense
Detecting adversarial attacks uses Fourier analysis.
dpa-autoencoders
Repository containing the code used to perform experiments shown in the paper "Data Poisoning Attacks to Anomaly Detection Models: a Robustness Analysis"