early666

early666's Stars

• faizaanpatel/AWS-DWVA

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• PositiveTechnologies/seq2seq-web-attack-detection

  The implementation of the Seq2Seq model for web attack detection. The Seq2Seq model is usually used in Neural Machine Translation. The main goal of this project is to demonstrate the relevance of the NLP approach for web security.

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• XFR1998/CCF-BDCI2022-Web-Attack-Detection-and-Classification

  此仓库代码为本人参加的CCF-BDCI-2022 赛道：Web攻击检测与分类识别 (多分类任务)，比赛rank-23。队员：Furen Xu

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• vanderbilt-ml/51-boyce-mlproj-NIDS

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• ayeshasdina/Intrusion-Detection-IoT

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• Marko132001/ML-based-Intrusion-Detection-System

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• c2dc/fl-unsup-nids

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• liamdm/FlowTransformer

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• HILALOZTEMELLL/BernoulliRBM-Chi_Scores-AutoEncoder-with--NF-BoT-IoT-dataset

  Data mining

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• m1a1x1/IoT-botnet-DDoS-attack-detection-azure

  InnovateFPGA 2021. EM017. Azure IoT Control application for IoT botnet DDoS attack detection at the edge project.

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• ShashiKumarKadariMallikarjuna/ML_Project

  Machine Learning-Driven Network Security: IoT DDoS Attack Detection

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• emonnmoz011/Bot-IoT

  This project aims to characterize the incoming traffic in an IoT (Internet of Things) environment using Machine Learning and Deep Learning Techniques. We have utilized the Bot-IoT dataset created in the Cyber Range Lab of UNSW Canberra. There were four categories to characterize in the dataset. The traffic categories are (a) DDoS (b) DoS (c) Reconnaissance and (d) Normal. I implemented (a) SVM (b) LSTM and (c) Vanilla RNN to observe the classification results

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• Shobhan0304/DDoS_attack_prediction_CNN

  Predicting DDoS attacks using CNN on Bot_IOT dataset

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• nuttysunday/Protocol-Based-Deep-Intrusion-Detection-for-DoS-Normal-and-DDoS-Attacks

  Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets

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• DuckDuckBug/cnn_waf

  Web Attacks Detection based on CNN

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• selimceylan/ZeroDay_WebAttack_Detection

  Zero day attack detection AI model based on seq2seq autoencoder

  Language:Python92

• saptajitbanerjee/SQL-Injection-Detection

  My team built a Machine Learning model to detect SQL Injections. The dataset was prepared by capturing normal and malicious HTTP requests, extracting essential features for training the model effectively. It enhances web application security by accurately identifying and flagging SQL Injection attacks.

  Language:Python101