lifecherrys

lifecherrys's Stars

• dendyikbc/kdd99-cnn-1

  Intrusion Detection Based on Convolutional Neural Network with kdd99 data set

  Language:Python5113

• Jehuty4949/NSL_KDD

  NSL-KDD Dataset

  168115

• arjunbahuguna/nsl-kdd

  Network Security Analysis using Machine Learning on the NSL-KDD dataset from the KDD Cup 1999

  Language:Python215

• vvsotnikov/LSTM-IDS

  Network data classifier based on the recurrent neural network.

  Language:Python225

• rachitb99/Traffic-Attack-Analysis

  Language:Python1

• itsnotfree/multi-SAE

  a program deal with the Encryption-Attack-Traffic dataset using MLP and multi-stacked-autoencoder

  Language:Python2

• irijije/InverseNeuralNetwork

  An implementation of inverse neural network for evasion attack on deep learning based intrusion detection system.

  Language:Python1

• milindKamath/Intrusion-Detection-System-using-Neural-Network

  Developed by me and my friend Uddesh Karda, this project classifies the incoming packet as either normal or an attack

  Language:Python1

• ImSourin/Network-Intrusion-Detection

  This project aims to detect Intrusions with a network using deep learning. The network traffic data is converted to multi channel RGB images, that are passed through CNNs to detect features useful to intrusion detection. (Additionally, we also experimented with dense SIFT based feature description. To discuss more on it, feel free to reach out.)

  Language:Python111

• morganrog123/ML-Network-Traffic

  Machine learning solution to detect malicious attacks from network activity captured by Wireshark

  Language:Python3

• hsuck/Malicious-Traffic-Classifier

  Language:Python1

• kasangeri/Bot-net-Detection

  Abot-netis a network of infected hosts (bots) that works independently under the control of aBotmaster(Bot herder), which issues commands to bots usingcommand and control (C&C)servers. Traditionally, bot-nets used acentralized client-server architecturewhich had a single point of failure but with the advent of peer-to-peer technology, the problem of single point of failure seems to have been resolved. Gaining advantage of the decentralized nature of the P2P architecture, botmasters started using P2P based communication mechanism.P2P bot-netsare highly resilient against detection even after some bots are identified or taken down. P2P bot-nets provide central frameworks for different cyber-crimes which include DDoS (Distributed Denial of Service), email spam, phishing, password sniffing, etc. So, the objective is to develop a tool for identifying P2P bot-nets using network traffic analysis.Also, the developers should detect the hosts involved in P2P traffic and then the detected hosts are further analyzed to detect bot-nets.

  Language:Python13

• Ramneet-Singh/BotNet-Detection-ML

  A Machine Learning based tool for identifying P2P (Peer To Peer) Bot-Nets using network traffic analysis, as well as detect the hosts involved in P2P traffic.

  Language:Python83

• bayramkotan/Network-Traffic-Classification-Analysis-With-Multi-Layer-Perceptron

  Language:Python1

• DevasenaInupakutika/PCAP_Analysis_K-Means

  Analyzing Network traffic data using parallel k-means clustering

  Language:Python1

• EngiN33R/neurotech-nids

  DNN-powered network traffic analysis

  Language:Python11

• Rajlaxmi04/Network-Traffic-Anomaly-Detection-using-PCA-and-BiGAN

  Anomaly Detection using PCA and BiGAN

  Language:Python131

• angeliki-c/anomaly_detection_in_network_traffic

  Language:Python2

• Senpaixyz/Network-Traffic-Anomaly-Detection-Simulator

  Language:Python1

• WyxJsdf/semi-supervised-network-anomaly-detection

  unsupervised&semi-supervised Anomaly Detection methods for Network Traffic

  Language:Python81

• minhyukko/gmu-firewall

  The primary objective of this project is to create an application that will detect anomalies and attacks in network traffic, create new firewall rules, and feed back the new firewall rules to the firewall.

  Language:Python1

• lavanpuri1999/Network-Intrusion-Detection

  Network Intrusion Detection Model using an RNN-CNN – we conducted the research in 2 parts, the first part included using various machine learning ensemble algorithms on the KDD (knowledge discovery dataset) and the second part was using the raw network traffic data provided by Palo alto networks, California. We converted the Raw network packet data into streams and fed it to the RNN to extract temporal features, which were in turn used to make images given to the CNN to detect anomalies

  Language:Python51

• AkashV420/Network-Intrusion-Detection-System

  Traffic flow classifier and a monitoring app that analyses all the flows passed through a switch and flags the one which seems to be an anomaly.

  Language:Python72

• Yogesh19921/GenSky

  Multi Objective Genetic Program to detect anomalies in Network Traffic

  Language:Python21

• akurgat/Botnet-Anomaly-Detection

  Using a MLP to identify botnets in network traffic

  Language:Python4

• alinuxr/detect_anomaly

  Detecting anomalies in network traffic using artificial intelligence techniques

  Language:Python1

• kushalchordiya216/Network-Anomaly-Detection

  A project using Django, sklearn and pandas to detect anomalies in network traffic using machine learning

  Language:Python349

• leopuglia-a/DL-network-security

  Language:Python1

• Xenia101/Network-Anomaly-Detection-System

  🌐 Flow Based netwrok anomaly detection system

  Language:Python92

• AhsanAyub/adversarial_ml_ids

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

  Language:Python178