Popular GitHub Repositories for Bro Ids

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.

Code for IDS-ML: intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)

[Pytorch] Generative retrieval model using semantic IDs from "Recommender Systems with Generative Retrieval"

Cursor IDE tried banning your country? This tool laughs at restrictions - resets Machine IDs, bypasses token limits, unlocks Pro features, and generates disposable emails with auto verification. Because AI should be for everyone, not just the "approved" regions.

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

Code for intrusion detection system (IDS) development using CNN models and transfer learning

Open-source authentication protocol for agentic interactions. Let agents collaborate with Authed

An intrusion detection system (IDS) based on machine learning technique, specifically the anomaly detection algorithm.

This tool parses log data and allows to define analysis pipelines for anomaly detection. It was designed to run the analysis with limited resources and lowest possible permissions to make it suitable for production server use.

Flash-IDS is an open-source system developed by the DART Laboratory for advanced intrusion detection using provenance graph representation learning. It implements the techniques presented in our IEEE S&P 2024 paper, "FLASH: A Comprehensive Approach to Intrusion Detection via Provenance Graph Representation Learning.

This clustering based anomaly detection project implements unsupervised clustering algorithms on the NSL-KDD and IDS 2017 datasets

This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.

Machine Learning Based - Intrusion Detection System

Privacy-preserving federated learning is distributed machine learning where multiple collaborators train a model through protected gradients. To achieve robustness to users dropping out, existing practical privacy-preserving federated learning schemes are based on (t, N)-threshold secret sharing. Such schemes rely on a strong assumption to guarantee security: the threshold t must be greater than half of the number of users. The assumption is so rigorous that in some scenarios the schemes may not be appropriate. Motivated by the issue, we first introduce membership proof for federated learning, which leverages cryptographic accumulators to generate membership proofs by accumulating users IDs. The proofs are issued in a public blockchain for users to verify. With membership proof, we propose a privacy-preserving federated learning

:shield: A GRU deep learning system against attacks in Software Defined Networks (SDN).

This repository includes code for the paper "Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection" accepted in AutonomousCyber, ACM CCS, 2024.

Adversarial Machine Learning applications on network-based Intrusion Detection System (IDS).

Network related services, programs and applications are developing greatly, however, network security breaches are also developing with them. Network security is an evolving, challenging and a critical task. It is essential that there is a system in place to identify any harmful movement happening in network. An Intrusion detection system (IDS) has become the prerequisite software addressing cyber security in the modern era. Especially, with the greater complexity of advanced cyber-attacks and as such the uncertainty surrounding the detection of the types of attacks. This thesis proposes a novel approach using an ensemble of K-Means and Gaussian Mixture clustering combined with a deep neural network (DNN) algorithm. When compared with traditional artificial neural network’s (ANN’s) used within an IDS, our approach implements modern advanc

Scripts to generate and analyze the AIT alert data set (AIT-ADS)

Sharkticon is an anomaly detection system, it analyzes your network using a Transformers model adapted to the anomaly detection.