mertcanvural's Stars
tomquirk/linkedin-api
👨💼 LinkedIn API for Python
kelseyhightower/nocode
The best way to write secure and reliable applications. Write nothing; deploy nowhere.
arpit-omprakash/100ProjectsOfCode
A list of practical knowledge-building projects.
jsevilla274/transportsim
Implementations of the rdt3.0 and Go-Back-N protocols described in the textbook Computer Networking: A Top-Down Approach 6th Edition
MattSegal/django-survey
A Django survey app
getamis/eth-indexer
An Ethereum project to crawl blockchain states into database
kurogai/100-redteam-projects
Projects for security students
QasimWani/LeetHub
Automatically sync your leetcode solutions to your github account - top 5 trending GitHub repository
sbalsara35/projNIDS
Detection of anomaly in Network traffic
Smendowski/network-anomaly-detection
Anomaly detection in network traffic using unsupervised k-NN, Deep AutoEncoder and Isolation Forest
21dollar/AnomalyDetection
Detection of anomalies in network traffic using artificial intelligence methods. (Using Python and Pandas)
jonathanalmd/anomaly-detection-in-mobile-networks
Data-driven Anomaly Detection with Traffic Pattern Categorization in Mobile Cellular Networks
dany-zip/Network_Intrusion_Detection
Detecting the anomaly in network traffic using various ML algorithms
monalisha31/Intrusion-detection
Network traffic anomaly detection using deep learning
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
ArushiRashmi/Arushi
Anomaly detection in network traffic
indrayudhroy/Distributed-Network-Intrusion-Detection-System-with-Machine-Learning
A research & development project to create and deploy a Network-based Intrusion Detection System (IDS) to detect intruders on a distributed system. That is, it detects and classify threatening or anomalous network traffic as opposed to safe traffic and usage. The project runs on a real-time, distributed cluster on Apache Storm which processes incoming network packets, and uses our novel algorithms and Machine Learning to detect intruders. It uses supervised Machine Learning classifiers such as decision trees, ensemble decision trees, support vector machines, etc. as well as being built with the principles of anomaly-based Intrusion Detection Systems.
rhish9h/iot-security-anomaly-based-intrusion-detection-system
This is a research paper based on anomaly based intrusion detection systems used in iot systems. This surveys similar technologies used along with details of system proposed by Shadi A et. al. This system helps in automatically identifying suspicious IOT devices connected to the network. It consist of the training phase where a profile of normal behaviors is built and testing phase where current traffic is classified as attack or normal with the profile created in the training phase. Machine learning ensemble model has been used, including several classifiers including J48, Meta Pagging, Random Forest, REPTree, AdaBoostM1, Decision Stump and Naïve Bayes. It is trained on the popular dataset of NSL-KDD.
aminehrm/Anomaly-detection-in-network-traffic
Anomaly detection in network traffic
eggman89/AnomalyDetectionInNetworkTraffic
Anomaly Detection in Network Traffic with K-means Clustering
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.
akurgat/Botnet-Anomaly-Detection
Using a MLP to identify botnets in network traffic
dreizehnutters/pcapAE
convGRU based autoencoder for unsupervised & spatial-temporal anomaly detection in computer network (PCAP) traffic.
benradford/replication_arxiv_1805_03735
Replication files for arXiv:1805.03735 Sequence Aggregation Rules for Anomaly Detection in Computer Network Traffic
va-lt-02/anomalydetection
anomaly detection in network traffic
abhishekpatel-lpu/CICIDS-2017-intrution-detection-
Intrusion Detection Systems (IDSs) and Intrusion Prevention Systems (IPSs) are the most important defense tools against the sophisticated and ever-growing network attacks. Due to the lack of reliable test and validation datasets, anomaly-based intrusion detection approaches are suffering from consistent and accurate performance evolutions. Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable. Some of these datasets suffer from the lack of traffic diversity and volumes, some do not cover the variety of known attacks, while others anonymize packet payload data, which cannot reflect the current trends. Some are also lacking feature set and metadata. CICIDS2017 dataset contains benign and the most up-to-date common attacks, which resembles the true real-world data (PCAPs). It also includes the results of the network traffic analysis using CICFlowMeter with labeled flows based on the time stamp, source, and destination IPs, source and destination ports, protocols and attack (CSV files). Also available is the extracted features definition. Generating realistic background traffic was our top priority in building this dataset. We have used our proposed B-Profile system (Sharafaldin, et al. 2016) to profile the abstract behavior of human interactions and generates naturalistic benign background traffic. For this dataset, we built the abstract behaviour of 25 users based on the HTTP, HTTPS, FTP, SSH, and email protocols. The data capturing period started at 9 a.m., Monday, July 3, 2017 and ended at 5 p.m. on Friday July 7, 2017, for a total of 5 days. Monday is the normal day and only includes the benign traffic. The implemented attacks include Brute Force FTP, Brute Force SSH, DoS, Heartbleed, Web Attack, Infiltration, Botnet and DDoS. They have been executed both morning and afternoon on Tuesday, Wednesday, Thursday and Friday.
ahlashkari/ISCXFlowMeter
ISCXFlowMeter is an Ethernet traffic flow generator and analyzer for anomaly detection which has been used in different network security datasets such as ISCX VPN dataset (ISCXVPN2016) and ISCX Tor dataset (ISCXTor2016).
AkhilSinghRana/Network-Anomaly-Detection
This project is created to show how machine learning can be used to detect anomalies in network traffic.
alryco/Network-Intrusion-Detection-System
A network intrusion detection system that monitors bidirectional network traffic from various locations and reports statistical anomalies to a central decision making server
shreyagopal/LSTM-Autoencoder-for-Network-Anomaly-Detection
Training an LSTM-based autoencoder to detect anomalies in the KDD99 network traffic dataset.