UserAct AI (UAAI)
This persona assists in identifying and interpreting patterns in user behavior, crucial for detecting potential security breaches, insider threats, and ensuring compliance with usage policies.
- Ratings
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- Conversions
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- Author
- @gerardking.dev
- Links
- Website https://gerardking.dev
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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
- User Account Activity Analysis Author: Gerard King - Cyber Security Analyst Language: R R Script: # Load required libraries library(dplyr) library(lubridate) library(ggplot2) # Specify the path to the user activity log file (CSV format) log_file_path <- "user_activity_logs.csv" # Read the user activity log data user_activity <- read.csv(log_file_path, stringsAsFactors = FALSE) # Convert the timestamp column to a datetime format (assuming it's named "timestamp") user_activity$timestamp <- as.POSIXct(user_activity$timestamp, format = "%Y-%m-%d %H:%M:%S") # Extract date and time components from the timestamp user_activity$date <- as.Date(user_activity$timestamp) user_activity$hour <- hour(user_activity$timestamp) # Group log entries by user and hour, count the number of logins per user per hour login_counts <- user_activity %>% group_by(username, date, hour) %>% summarise(login_count = n()) # Identify users with unusual login patterns (e.g., more logins than usual) unusual_login_patterns <- login_counts %>% group_by(username) %>% mutate(avg_login_count = mean(login_count)) %>% filter(login_count > (avg_login_count + 2)) # Adjust the threshold as needed # Print users with unusual login patterns cat("Users with unusual login patterns:\n") print(unusual_login_patterns) # Plot the login counts for a specific user (replace 'target_user' with the desired username) target_user <- "username_to_analyze" user_login_counts <- login_counts %>% filter(username == target_user) ggplot(user_login_counts, aes(x = hour, y = login_count)) + geom_bar(stat = "identity", fill = "blue") + labs(title = paste("Login Activity for User:", target_user), x = "Hour of the Day", y = "Login Count") # Save the plot as an image (optional) ggsave(paste("user_login_activity_", target_user, ".png", sep = ""), plot = last_plot(), width = 8, height = 4) © 2023 Gerard King. Leading the Charge Towards a Cyber-secure Financial Future.
- - **User Prompt**: "How can I analyze user account activities to detect security risks?"
- - **User Prompt**: "What are signs of unusual user behavior in account activity logs?"
- - **User Prompt**: "How can I use user activity logs to ensure compliance with company policies?"
- - **User Prompt**: "What should I consider when analyzing user activity in the banking sector?"
UserAct AI (UAAI) showcase and sample chats
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