diff options
Diffstat (limited to 'app.R')
| -rw-r--r-- | app.R | 106 |
1 files changed, 102 insertions, 4 deletions
@@ -12,7 +12,7 @@ library(DT) library(data.table) library(ggplot2) library(shinycssloaders) - +library(h2o) # Defining Non Changing Variables data <- fread("2020.10.01.csv") @@ -42,6 +42,9 @@ get_color <- function(a = 1) { return(alpha("#e95420", a)) } +# Load the models +model.dl = h2o.loadModel(dl_model) + # Define UI for application ui <- fluidPage( theme = shinytheme("united"), @@ -139,8 +142,7 @@ ui <- fluidPage( ), actionButton("plot", "Plot Graph", width = "100%", icon = icon("chart-line"), - style="color: #fff; background-color: #e95420; - outline: none") + class = "btn btn-primary") ), mainPanel( withSpinner( @@ -151,7 +153,57 @@ ui <- fluidPage( ) ), tabPanel( - "Compare Models" + "Predictions", + sidebarLayout( + sidebarPanel( + selectInput( + "modelType", + p("Choose a Model to Predict:"), + choices = c("Deep Learning" = "dl") + ), + numericInput("npin", "Number of inbound packets:", + 10, min = 0), + numericInput("npob", "Number of outbound packets:", + 10, min = 0), + numericInput("nbin", "Number of bytes in:", + 2000, min = 0), + numericInput("nbob", "Number of bytes out:", + 10000, min = 0), + numericInput("dprt", "Destination Port (1024 - 49151):", + 5234, min = 1024, max = 49151), + numericInput("tepy", "Total Entropy:", + 18000, min = 0), + actionButton("predictButton", "Predict", + width = "100%", icon = icon("think-peaks"), + class = "btn btn-primary") + ), + mainPanel( + tags$label(h3('Status/Output')), + verbatimTextOutput('contents'), + p(strong("Prediction Legend"), br(), br(), em("1.00 - 1.99"), + " - Benign", br(), em("2.00 - 2.99"), " - Malicious", + br(), em("3.00 - 3.99"), " - Outlier", + style="text-align:justify;color:black; + background-color:lavender;padding:15px;border-radius:10px"), + tableOutput('tabledata'), # Prediction results table + fluidRow( + column( + width = 6, + withSpinner( + plotOutput("varImpPlot"), + type = 6, color = "#e95420" + ) + ), + column( + width = 6, + withSpinner( + plotOutput("lcPlot"), + type = 6, color = "#e95420" + ) + ) + ) + ) + ) ) ) ) @@ -175,6 +227,52 @@ server <- function(input, output) { colnames = features) ) + datasetInput <- reactive({ + req(input$npin) + req(input$npob) + req(input$nbin) + req(input$nbob) + req(input$dprt) + req(input$tepy) + df <- data.frame( + Name = c("num_pkts_in", "bytes_in", "num_pkts_out", "bytes_out", + "dest_port", "total_entropy"), + Value = as.character(c(input$npin, input$nbin, input$npob, + input$nbob, input$dprt, input$tepy)), + stringsAsFactors = FALSE) + labels <- 0 + df <- rbind(df, labels) + input <- transpose(df) + write.table(input,"input.csv", sep=",", quote = FALSE, + row.names = FALSE, col.names = FALSE) + test <- read.csv(paste("input", ".csv", sep=""), header = TRUE) + prediction <- predict(model.dl, as.h2o(test)) + }) + + output$varImpPlot <- renderPlot({ + h2o.varimp_plot(dl) + }) + + output$lcPlot <- renderPlot({ + h2o.learning_curve_plot(dl) + }) + + # Status/Output Text Box + output$contents <- renderPrint({ + if (input$predictButton>0) { + isolate("Calculation complete.") + } else { + return("Server is ready for calculation.") + } + }) + + # Prediction results table + output$tabledata <- renderTable({ + if (input$predictButton>0) { + isolate(datasetInput()) + } + }) + output$secondSelection <- renderUI({ selectedFeature <- input$plotVariable1 selectInput( |