Automated_Malware_Detection_Website_Plan.pptx
- 1. Website Development Plan: Automated Malware Detection PROJECT EXHIBITION REVIEW 2 PRESENTED BY: GROUP 62 SUPERVISOR: DR IRFLAM INSTITUTION: VIT BHOPAL UNIVERSITY DATE: 23RD FEBRUARY
- 2. Introduction ? What is Malware? ? Malicious software that harms websites and users. ? Why Build a Website for Malware Detection? ? To provide an online platform for scanning and analyzing websites for malicious activities. ? Project Goal: ? Develop an interactive website where users can submit URLs or files for malware detection.
- 3. Problem Statement ? - Increasing web-based cyber threats (malware injections, phishing, drive-by downloads). ? - Users lack easy-to-use tools for scanning websites for malware. ? - Need for a web-based solution that allows real- time detection and analysis.
- 4. Objectives ? - Develop a user-friendly **website for malware detection**. ? - Implement features for **URL and file scanning**. ? - Use machine learning to classify **malicious vs. benign content**. ? - Provide **real-time alerts and reports** to users.
- 5. Website Features ? 1. **User Dashboard** - Upload URLs or files for scanning. ? 2. **Real-Time Scanning** - Detect malware in website content. ? 3. **Machine Learning Integration** - AI-based classification of threats. ? 4. **Report Generation** - Detailed analysis of threats found. ? 5. **Admin Panel** - Monitor scans and update malware signatures.
- 6. Website Architecture ? - **Frontend:** React.js / HTML, CSS, JavaScript ? - **Backend:** Flask / Django (Python) ? - **Database:** MySQL / MongoDB ? - **Machine Learning Model:** TensorFlow / Scikit- Learn ? - **Cloud Deployment:** AWS / Google Cloud / Heroku
- 7. Workflow of Malware Detection ? 1. User submits a **URL or file**. ? 2. Website **extracts features** (HTML tags, JavaScript behavior, HTTP requests). ? 3. **ML model analyzes** the data and predicts if it's malicious. ? 4. Results are displayed with a **malware risk score**. ? 5. User receives a **detailed report and recommendations**.
- 8. Technologies Used ? - **Frontend:** React.js / Bootstrap ? - **Backend:** Flask (Python) / Django ? - **Database:** PostgreSQL / Firebase ? - **ML Model:** Scikit-Learn, TensorFlow ? - **APIs Used:** VirusTotal, Google Safe Browsing ? - **Cloud Hosting:** AWS, Heroku, DigitalOcean
- 9. Expected Outcomes ? - A **fully functional website** for malware detection. ? - **Accurate and real-time analysis** of website threats. ? - **User-friendly interface** for easy scanning and reporting. ? - **Scalable solution** for broader cybersecurity applications.
- 10. Challenges & Solutions ? - **Handling large-scale URL scans** → Optimized database queries & caching. ? - **False positives in malware detection** → Hybrid AI & heuristic analysis. ? - **Ensuring real-time performance** → Serverless architecture & cloud optimization. ? - **Data security & privacy** → Secure user data handling & encryption.
- 11. Future Scope ? - Integration with **SIEM solutions** for enterprise security. ? - Expanding detection to **network-based malware**. ? - Implementing **blockchain for secure malware logging**. ? - Developing a **mobile version** for real-time scanning.
- 12. Project Timeline ? 1. **Week 1-2:** Requirement analysis & research. ? 2. **Week 3-4:** Website frontend & backend setup. ? 3. **Week 5-6:** ML model integration & testing. ? 4. **Week 7:** Security & performance optimization. ? 5. **Week 8:** Final testing, deployment, and documentation.
- 13. Conclusion ? - Our project aims to provide an **AI-powered website for malware detection**. ? - This will help users scan and analyze website threats in **real-time**. ? - **Next Steps:** Develop, test, and deploy the website.
- 14. Q&A ? Open for any questions and feedback.