40 skills found · Page 1 of 2
floodlight / FloodlightFloodlight SDN OpenFlow Controller
floodlight-sports / FloodlightPython package for streamlined analysis of sports data.
nayanseth / Sdn LoadbalancingTo perform load balancing on fat tree topology using SDN Controller i.e. Floodlight and OpenDaylight.
n0rt0nthec4t / Homebridge Nest AccfactoryHomebridge support for Nest/Google devices including HomeKit Secure Video (HKSV) support for doorbells and cameras
floodlight / Floodlight WebuiWeb user interface for Floodlight
pualien / TrackieA Chrome extension to enhance debugging of some frequently-used tag management platforms (Google Tag Manager, Tealium, Commanders Act, DTM) in combination with some frequently-used tags (Google Analytics, Google Analytics 4, GA Audiences, Ddm, Criteo, Adobe Analytics/Omniture, Floodlight, Comscore, Facebook, Bluekai, Youbora, Kinesis, Webtrekk, Segment)
wallnerryan / Floodlight Qos BetaFloodlight with QoS module and tools to manage QoS state in an OF network
Chentingz / TrafficMonitor4Floodlight添加了流量监控模块的Floodlight控制器
Sovietaced / AviorFloodlight Network Management and Testing tool
xuraylei / Floodlight With TopoguardNo description available
ZhiYiFang / Intrusion Prevention System Based On Floodlight实现了snort和floodlight控制器的联动从而实现了基于floodlight的入侵防御系统
GlobalNOC / FlowSpaceFirewallFlowSpace Firewall Application a floodlight based controller allowing multiple controllers to talk to a single switch, but can not interact with each others flow space (hence FlowSpace Firewall)
rajnandan1 / FloodlightjsEasily create key board shortcuts for your JS functions. Built using JS only with no other dependency. Inspired from MacOS spotlight
JianqingJiang / QoS FloodlightBased floodlight official website qos project,my env is sdnhub vm with ovs 2.1.0 version and openflow1.3 and it works
Keridos / FloodLightsMinecraft Mod for Flood Lights (alternative for GregsLighting)
denverfoundation / StorybaseThe code behind Floodlight
google / Floodlight AuditNo description available
hknakcay / Floodlight WebUISDN Controller New WebUI
Crisis incidents caused by rebel groups create a negative influence on the political and economic situation of a country. However, information about rebel group activities has always been limited. Sometimes these groups do not take responsibility for their actions, sometimes they falsely claim responsibility for other rebel group’s actions. This has made identifying the rebel group responsible for a crisis incident a significant challenge. Project Floodlight aims to utilize different machine learning techniques to understand and analyze activity patterns of 17 major rebel groups in Asia (including Taliban, Islamic State, and Al Qaeda). It uses classification algorithms such as Random Forest and XGBoost to predict the rebel group responsible for organizing a crisis event based on 14 different characteristics including number of fatalities, location, event type, and actor influenced. The dataset used comes from the Armed Conflict Location & Event Data Project (ACLED) which is a disaggregated data collection, analysis and crisis mapping project. The dataset contains information on more than 78000 incidents caused by rebel groups that took place in Asia from 2017 to 2019. Roughly 48000 of these observations were randomly selected and used to develop and train the model. The final model had an accuracy score of 84% and an F1 Score of 82% on testing dataset of about 30000 new observations that the algorithm had never seen. The project was programmed using Object Oriented Programming in Python in order to make it scalable. Project Floodlight can be further expended to understand other crisis events in Asia and Africa such as protests, riots, or violence against women.
aishwaryasabane / Load Balancing Using SDNWorked on Floodlight Controller and Mininet to implement the load balancer in python using Dijkstra’s shortest path algorithm, increased bandwidth and efficiency of network by 300%.