GPTs Works

Created at 7 months ago

Created by gerardking.dev

(NAAI)

What is (NAAI)

NetAnom AI is designed to help identify potential cybersecurity threats by analyzing traffic patterns and detecting unusual activity in network data.

Capabilities of (NAAI)

Web Browsing

DALL·E Image Generation

Code Interpreter

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(NAAI)

Preview (NAAI)

Prompt Starters of (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?"

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