User Behavior Analysis for Detecting Unauthorized Access

Overview

Problem Statement

Unauthorized access to user accounts and sensitive information poses a significant security threat to organizations. Detecting and preventing unauthorized access is crucial for maintaining data security and integrity.

Solution

In this project, we aim to detect unauthorized access by analyzing user behavior patterns using machine learning techniques. We leverage a dataset from Kaggle, preprocess the data, and train a machine learning model to classify user behavior as normal or suspicious/attack.

Process

1. Dataset Selection: We chose the dataset from Kaggle, provided by Mohamed Saleh, which contains user behavior data.

2. Data Preprocessing: We performed data preprocessing tasks such as handling missing values, encoding categorical variables, scaling numerical features, and splitting the data into training and testing sets.

3. Feature Engineering: We extracted relevant features from the dataset to represent user behavior patterns effectively. This included feature selection, transformation, and creation of new features based on domain knowledge.

4. Model Training: We trained machine learning models using the preprocessed data to predict whether a user behavior is normal or suspicious/attack. We experimented with various algorithms such as decision trees, random forests, logistic regression, etc., and evaluated their performance using appropriate metrics.

5. Model Evaluation: We evaluated the trained models on the test dataset to assess their performance in detecting unauthorized access. We analyzed metrics such as accuracy, precision, recall, F1-score, and ROC-AUC to measure the model's effectiveness.

6. Deployment: We deployed the trained model in a production environment for real-time detection of unauthorized access. The model continuously analyzes user behavior data and alerts administrators whenever suspicious activity is detected.

Dataset

• Dataset Name: User Behavior Dataset
• Dataset Source: Kaggle - Mohamed Saleh
• Description: The dataset contains user behavior data collected from various sources, including login/logout activities, file access events, network connections, etc. It includes features such as user ID, timestamp, activity type, IP address, etc.

Usage

1. Clone the Repository: Clone the project repository to your local machine.

2. Use Jupyter Notebook: Using the given Dataset train the model.

Contributors

• Prince Kumar
• Piyush Kumar
• Hemant Singh
• Vaibhav Singh

Google Colab Link

https://colab.research.google.com/drive/1kS9fCwBEgAygPv1WmJxQijU6IH_XXESC?usp=sharing