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Type: Organization
Type: Organization
A curated list of amazingly awesome Cybersecurity datasets
CICFlowmeter-V4.0 (formerly known as ISCXFlowMeter) is an Ethernet traffic Bi-flow generator and analyzer for anomaly detection that has been used in many Cybersecurity datsets such as Android Adware-General Malware dataset (CICAAGM2017), IPS/IDS dataset (CICIDS2017), Android Malware dataset (CICAndMal2017) and Distributed Denial of Service (CICDDoS2019).
The IDS Analysis Project
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 Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable.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.
CICIDS2017 dataset
The purpose of this repository is to demonstrate the steps of processing CICIDS2017 dataset using machine learning algorithms.
DNN-Ensemble IDS is a machine learning based classification model for intrusion detection exploiting ensembles of classifiers.
Intrusion Detection System using machine learning
An Anomaly based Intrusion Detection System: A Robust Machine Learning Approach
Network Intrusion Detection System on CSE-CIC-IDS2018 using ML classifiers and DNN ( ANN , CNN , RNN ) | Hyper-parameter Optimization { learning rate, epochs, network architectures, regularisation } | Adversarial Attacks - Label flip , Adversarial samples , KNN (defence)
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 advances in deep learning such as initialising the parameters through the unsupervised pre-training clustering ensemble, therefore improving the detection accuracy. We hope our results will show that the proposed approach can provide a real-time response to the attack with a greatly increased detection ratio for false flags.
Implement and train a machine-learning based Security Intrusion Detection model using Python.
Autoencoder based intrusion detection system trained and tested with the CICIDS2017 data set.
A package for capturing and analyzing network flow data and intraflow data, for network research, forensics, and security monitoring.
Sample Codes for Machine Learning for Network Security
Network Intrusion Detection System
A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach
A network Intrusion Detection System (IDS) based on Self-Organizing Neural Networks (SOINN).
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