13 skills found
ahlashkari / CICFlowMeterCICFlowmeter-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).
hieulw / CicflowmeterCICFlowmeter written in python for easy to try out
datthinh1801 / CicflowmeterThis is a Python version of CICFlowmeter-V4.0 (formerly known as ISCXFlowMeter) - an Ethernet traffic Bi-flow generator and analyzer for anomaly detection.
zeek-flowmeter / Zeek FlowmeterA Zeek script to generate features based on timing, volume and metadata for traffic classification.
iPAS / TCPDUMP And CICFlowMeterThese scripts conduct TCPDUMP in harmony with CICFlowMeter to operation real-time traffic capturing and converting in csv file.
wilfred-wulbou / Intrusion Detection SystemAn intrusion detection system (IDS) based on machine learning technique, specifically the anomaly detection algorithm.
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 Our evaluations of the existing eleven datasets since 1998 show that most are out of date and unreliable.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.
QiuZYin / CICFlowMeterThis is a Python version of CICFlowmeter-V4.0.
hamelin / Cicflowmeter DockerDockerization of CICFlowMeter, with an entry point to accept command line parameters when running the container.
ronghuihu / CICFLOWMETERNo description available
YichengGuoo / Cicflowmeter Gp一种了多视角网络流量特征提取工具，满足提取认证模式、流量封装、连接管理以及流量混淆四类多视角特征的需求
Bi9River / GoflowmeterGoFlowMeter acts as the Go-implementation of the CICFlowMeter to extract features from network flow.
jsrojas / NtopngDataEditorA java application that loads 3 csv files obtained from ntopng, CICFlowmeter, and nDPI. It compares the flows statistics obtained from pcap files with CICFlowmeter and ntopng and once a match is found that flow is labeled with the Layer 7 protocol obtained with the nDPI library within ntopng. The application delivers a csv file with all the flows labeled with the layer 7 protocol
