| EONRaider/Packet-Sniffer |
548 |
|
0 |
0 |
almost 4 years ago |
4 |
August 19, 2022 |
0 |
agpl-3.0 |
Python |
| A Network Packet Sniffing tool developed in Python 3. |
| Open-Network-Insight/open-network-insight |
187 |
|
0 |
0 |
about 9 years ago |
0 |
|
31 |
apache-2.0 |
|
| ONI development has been moved into Apache Software Foundation - Apache Spot (incubating) http://spot.apache.org/ |
| DynamiteAI/dynamite-nsm |
128 |
|
0 |
0 |
about 4 years ago |
24 |
February 10, 2022 |
28 |
gpl-3.0 |
Python |
| DynamiteNSM is a free Network Security Monitor developed by Dynamite Analytics to enable network visibility and advanced cyber threat detection |
| AbertayMachineLearningGroup/network-threats-taxonomy |
70 |
|
0 |
0 |
almost 6 years ago |
0 |
|
0 |
gpl-3.0 |
TeX |
| Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies |
| donot-wong/SecAcademic |
64 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
|
|
| 记录我的安全学术学习 |
| StamusNetworks/gophercap |
48 |
|
0 |
0 |
over 3 years ago |
1 |
January 06, 2023 |
6 |
gpl-3.0 |
Go |
| Accurate, modular, scalable PCAP manipulation tool written in Go. |
| archienorman11/thesis-bitcoin-clustering |
31 |
|
0 |
0 |
about 3 years ago |
0 |
|
3 |
|
Jupyter Notebook |
| The Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy. |
| zacstewart/comm-gtk |
28 |
|
0 |
0 |
about 8 years ago |
0 |
|
5 |
mit |
Rust |
| CCXLabs/CCXDigger |
19 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
other |
|
| The CyberCX Digger project is designed to help Australian organisations determine if they have been impacted by certain high profile cyber security incidents. Digger provides threat hunting functionality packaged in a simple-to-use tool, allowing users to detect certain attacker activities; all for free. |
| abzcoding/aptdetector |
14 |
|
1 |
0 |
about 7 years ago |
3 |
February 14, 2016 |
1 |
other |
HTML |
| Advanced Persistent Threat Detection Using Network Analysis |
