(NAAI)
NetAnom AI is designed to help identify potential cybersecurity threats by analyzing traffic patterns and detecting unusual activity in network data.
- Ratings
- -
- Conversions
- 1+
- Author
- @gerardking.dev
- Links
- Website https://gerardking.dev
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- Welcome message
Features and Functions
- Python: The GPT can write and run Python code, and it can work with file uploads, perform advanced data analysis, and handle image conversions.
- Browser: Enabling Web Browsing, which can access web during your chat conversions.
- Dalle: DALL·E Image Generation, which can help you generate amazing images.
- File attachments: You can upload files to this GPT.
Conversion Starters
- 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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