(NAAI)

Network Traffic Anomaly Detection Author: Gerard King - Cyber Security Analyst Language: R R Script: # Load required libraries library(dplyr) library(ggplot2) # Specify the path to the network traffic data file (CSV format) data_file_path <- "network_traffic_data.csv" # Read the network traffic data network_data <- read.csv(data_file_path, stringsAsFactors = FALSE) # Convert the timestamp column to a datetime format (assuming it's named "timestamp") network_data$timestamp <- as.POSIXct(network_data$timestamp, format = "%Y-%m-%d %H:%M:%S") # Extract date and time components from the timestamp network_data$date <- as.Date(network_data$timestamp) network_data$hour <- hour(network_data$timestamp) # Group data by date and hour, calculate the total bytes transferred traffic_summary <- network_data %>% group_by(date, hour) %>% summarise(total_bytes = sum(bytes)) # Detect unusual spikes in network traffic (adjust the threshold as needed) threshold <- 2 * quantile(traffic_summary$total_bytes, probs = 0.75) # Example threshold: 2 times the 75th percentile unusual_traffic_spikes <- traffic_summary %>% filter(total_bytes > threshold) # Print dates and hours with unusual traffic spikes cat("Dates and hours with unusual traffic spikes:\n") print(unusual_traffic_spikes) # Plot the network traffic over time ggplot(traffic_summary, aes(x = hour, y = total_bytes)) + geom_line() + labs(title = "Network Traffic Over Time", x = "Hour of the Day", y = "Total Bytes Transferred") # Save the plot as an image (optional) ggsave("network_traffic_over_time.png", plot = last_plot(), width = 8, height = 4) © 2023 Gerard King. Leading the Charge Towards a Cyber-secure Financial Future.

- **User Prompt**: "How can I detect unusual spikes in my network traffic data?"

- **User Prompt**: "What do typical anomalies in network traffic indicate?"

- **User Prompt**: "How can I visually represent network traffic data to spot anomalies?"

- **User Prompt**: "What should I focus on when analyzing network traffic in a retail environment?"