FINAL PPT

Projectworkphase1(19AI702). AI Powered Advanced Web Application Firewall Submitted by: V.SRIRAM (212222103002) R.SUROTHAAMAN (212222103003) M.PAVITHRA (212222100032) 2022-2026 Batch TEAM NO: 58 Under the guidance of: V.SWEDHA NAME OF THE GUIDE V.SWEDHA Designation,Department of IT.

Agenda. Introduction Statement of the Problem Scope of the Project Methodology Architectural Diagram System Flow Algorithm Used Design Use Case Diagram Class Diagram Sequence Diagram Implementation Important Code Segments Output and Results Test Cases Conclusion Future Work References.

Introduction. AI-Powered Advanced Web Application Firewall (WAF) is a security system designed to protect web applications from cyber threats. With the rapid growth of web applications, attacks such as SQL Injection, Cross-Site Scripting (XSS), and DDoS have become increasingly common. Traditional WAFs rely on static, rule-based mechanisms, which are often ineffective against evolving or unknown attacks. Artificial Intelligence (AI) and Machine Learning (ML) enable intelligent traffic analysis and real-time threat detection. This project integrates AI/ML techniques to create an adaptive and intelligent WAF that improves security dynamically..

Statement of the Problem. Develop an AI-powered WAF to protect web applications from cyber threats. Use machine learning models to detect malicious web traffic. Provide a real-time monitoring dashboard with alerts. Improve accuracy and efficiency of threat detection. Target Audience: Web developers, IT security teams, enterprises. Deliverables: Trained AI/ML model for traffic analysis. Functional WAF system with monitoring dashboard. Documentation of architecture, model, and performance..

Scope of the project. Focus: AI-powered Web Application Firewall (WAF) Real-time detection and blocking of cyber threats Dashboard for monitoring traffic and alerts Target Audience: Web developers & administrators Enterprises needing web security IT security professionals Deliverables: AI/ML models detecting SQL Injection, XSS, DDoS Functional WAF with monitoring & alerts Documentation & scalable deployment.

Scope of the project. Inclusions: Collect and preprocess web traffic logs. Extract features from HTTP requests and patterns. Build AI/ML detection models. Develop a user-friendly dashboard. Implement real-time threat blocking and alerts. Exclusions: No hardware-level network security. Not for non-web or offline applications. No integration with third-party cloud security services..

Scope of the project. Data Collection: Use web traffic logs with labeled normal and malicious requests. Preprocessing: Clean data and extract relevant features for ML analysis. Limitations: Dataset may not include all types of web attacks. Real-time performance may depend on server capacity. AI model accuracy may vary with unseen traffic patterns. Continuous updates needed for evolving threats..

Methodology. Data Collection: Gather web traffic logs (normal and malicious requests). Data Preprocessing: Clean data and extract features from HTTP requests and headers. AI/ML Model Development: Train models to classify requests as safe or malicious. Web Dashboard: Build a user-friendly interface for monitoring traffic and alerts. Integration & Testing: Combine AI model with dashboard; validate accuracy and performance..

Methodology. Traffic Analysis & AI Integration: Collect Traffic Data: Gather normal and malicious web requests. Preprocess Data: Clean, normalize, and extract features from HTTP requests. Develop AI/ML Model: Train models to classify requests as safe or malicious. Web Dashboard: Create a user-friendly interface for monitoring and alerts. Integration & Testing: Combine AI model with dashboard; test for accuracy and performance..

Architecture Diagram/Flow. Client Request O Incoming Traffic Nginx Load B41ancer Traffic Analysis Q) WAF Processing Nodes O ML Evaluation A1 / ML Engine O Decision Engine API Gateway Clean Response Protected Web Application - -Y MongoDB Redis ELK Stack Monitoring & Logs Dashboard & Alerts.

Design-Use Case Diagram. [image] Administrator Monitor Web Traffic Detect Threats A - Powered Advanced Web Application Firewall View Dashboard & Reports Generate Alerts View Dashboard & Reports Manage A1 Models Security Analyst.

Design-Class Diagram. [image] User - userlD: int - name: string - role: string - login(email: string, password; string): bool + logout(): void Alert - alertlD: int - threatlD: int - userlD; int - alertTimestamp: + sendAlert(): void Web Request - requestlD: int - sourcelP: string - destinationIJRL: Strng - timestamp: datetime Threat A1-powered Advanced Web Application Firewall - threatlD: int - requestlD: int - threatType: string - severity: string + classifyThreat(model: AIModel): bool AIM Odel - modellD: int - name: string - version: string - trainingDate: date - accuracy: float + trainModel(data: Dataset): void + pred ictThr eat (reqtBt: WebRequest): string Log - loglD: int - requestlD: int - threatlD: int - actionTaken: string recordAction(action: string): void.

Sequence Diagram. [image] Traffic Monitoring Module preprocessing Ai.Based Threat Module Web Client Logging & Alert Module 4. Evaluate Request Das hboard Module 1. HTTP/HTTPS Request ( ) 7. Response (Allow/Block) 2. Incoming Request : 7. Response 3. Analyzed Features I 6. Record Action & Notify ) 7. Request ( ) Update Analytics :.

Algorithms used. Machine Learning–Based Threat Detection Model: The Machine Learning–based Threat Detection Model is used to analyze incoming web traffic in real time. It classifies HTTP requests as normal or malicious based on extracted features from headers, URLs, and payloads. It is trained using labeled web traffic data containing both legitimate and attack requests. It improves detection accuracy without increasing response latency. It enables adaptive learning by identifying new attack patterns and reducing false positives during request filtering..

Algorithms used. Anomaly Detection and Dynamic WAF Rule Generation Algorithm: This algorithm identifies abnormal behavior by comparing incoming traffic patterns with normal baseline traffic. It detects attacks such as SQL Injection, Cross-Site Scripting (XSS), and brute-force attempts. Based on detected threats, it dynamically generates and updates Web Application Firewall rules. It automatically blocks malicious requests and IP addresses in real time. It ensures continuous protection by adapting firewall rules according to evolving attack behaviors..

Hardware and software selection. Hardware: The project uses the following hardware components: Server or cloud system for hosting the WAF Load balancer (Nginx) for traffic distribution Storage for logs and ML models Network infrastructure for HTTP/HTTPS traffic Software: The project uses the following software components: Python programming language Flask / FastAPI framework Machine learning using Scikit-learn Nginx web server and load balancer Docker and Docker Compose Prometheus and Grafana for monitoring Linux operating system.

Implementation. The system is implemented using Python-based web technologies and containerized services. Nginx is used as a load balancer to route incoming traffic to multiple WAF nodes. HTTP requests are intercepted and analyzed using a security middleware. Relevant features are extracted and passed to the machine learning engine. The ML model classifies requests as normal or malicious. Based on the prediction, dynamic WAF rules are generated to allow or block traffic. All security events and metrics are logged and visualized using monitoring tools..

Important Code segments. Dynamic WAF Rule Generation (AI Driven) def generate_rules_from_threats(self, threats): if len(threat_list) >= 3: rule = self._generate_rule_for_threat_type() Description: Uses ML threat predictions to create dynamic WAF rules Generates rules for SQL Injection, XSS, Bot attacks Automatically blocks IPs, URLs, User-Agents Prevents duplicate rules and manages rule expiry.