LubnaShireenR/cybershield-cybersecurity-risk-intelligence-dashboard

# CyberShield – Cybersecurity Risk Intelligence Dashboard ## Overview CyberShield is a cybersecurity risk intelligence dashboard developed using **Python, SQL, and Power BI**. The project analyzes cybersecurity events to identify attack patterns, monitor risk levels, evaluate incident response actions, and provide actionable insights through interactive visualizations and executive-level KPIs. The dashboard is designed to help security analysts and decision-makers understand cyber threats, assess network risks, and improve incident management strategies. ## Dashboard Preview --- ## Objectives - Analyze cyber attack trends and patterns. - Monitor attack severity levels. - Evaluate anomaly scores and network risks. - Assess incident response effectiveness. - Build an interactive dashboard for cybersecurity intelligence. ## Key Performance Indicators (KPIs) - Total Events - Critical Incidents - Average Risk Score - High Risk Events ## Dashboard Features ### Attack Type Analysis - Distribution of cyber attacks. - Comparison of DDoS, Malware, and Intrusion incidents. ### Severity Level Analysis - Breakdown of incidents by severity level. - High, Medium, and Low risk categorization. ### Monthly Threat Trend - Analysis of cyber events over time. - Identification of attack fluctuations and trends. ### Incident Response Analysis - Actions taken against detected threats. - Comparison of Blocked, Ignored, and Logged events. ### Network Segment Risk Assessment - Average anomaly scores across network segments. - Identification of higher-risk network zones. ### Interactive Filters - Attack Type - Severity Level - Month - Network Segment ## Tools & Technologies - Python - Pandas - SQLite - SQL - Power BI - Jupyter Notebook ## Project Workflow ### 1. Data Collection Imported and explored the cybersecurity attack dataset. ### 2. Data Cleaning - Removed duplicates - Handled missing values - Standardized data formats ### 3. Feature Engineering Created additional analytical fields including: - Year - Month - Day - Hour - High Risk Indicator - Critical Incident Indicator ### 4. SQL Analysis Performed exploratory analysis using SQL queries to identify: - Attack type distribution - Severity distribution - Network segment risks - Incident response patterns ### 5. Dashboard Development Designed an interactive Power BI dashboard with KPIs, charts, and slicers. ## Business Insights - Attack types are distributed across DDoS, Malware, and Intrusion categories. - Medium severity incidents represent a significant portion of total events. - Certain network segments exhibit higher average anomaly scores. - Incident response actions help identify operational security effectiveness. - Interactive filtering enables targeted investigation of cyber threats. ## Future Enhancements - Real-time threat monitoring integration. - Threat intelligence feed connectivity. - Predictive risk modeling using machine learning. - Advanced incident forecasting. - SOC (Security Operations Center) performance analytics. ## Author **Lubna Shireen R** ## License This project is licensed under the MIT License.