Update README.md

README.md

@@ -14,6 +14,7 @@ tags:
 # XSS Payload Detector
 
 DistilBERT-based machine learning model for detecting XSS payloads.
+This project can be used either as a standalone CLI classifier or as a log-monitoring system that automatically analyzes web server requests and detects potential XSS attacks.
 
 ## Labels
 
@@ -30,39 +31,78 @@ pip install transformers
 pip install flask
 ```
 
-## Usage
+## Features
 
-Start the test server:
+This project supports two different usage modes.
+
+### 1. CLI Mode
+
+Run:
 
 ```bash
-python test_server.py
+python inference_bert_url.py
+```
+
+Enter a string or URL directly from the command line.
+
+The model will classify the input as:
+
+- NORMAL
+- XSS
+
+and display a confidence score.
+
+Example:
+
+```text
+Input:
+<script>alert(1)</script>
+
+Result:
+XSS
+Confidence:
+0.9998
 ```
 
-Start monitoring:
+---
+
+### 2. Log Monitoring Mode
+
+Run the monitoring service:
 
 ```bash
 python monitor.py
 ```
 
-Open browser:
+The monitor automatically reads web server access logs and analyzes incoming requests.
 
-```text
-http://127.0.0.1:8080/?q=abcde
+Detected XSS payloads are logged for further inspection.
+
+This allows the model to be integrated into a web application environment without manually entering payloads.
+
+---
+
+### Test Environment
+
+Run the example Flask server:
+
+```bash
+python test_server.py
 ```
 
-Example payload:
+Then open:
 
 ```text
-http://127.0.0.1:8080/?q=<img src='x' onerror='alert("xss")'>
+http://127.0.0.1:8080/?q=abcde
 ```
 
-## CLI Tool
+Example XSS payload:
 
-```bash
-python inference_bert_url.py
+```text
+http://127.0.0.1:8080/?q=<img src='x' onerror='alert("xss")'>
 ```
 
-This tool accepts user input and classifies it as NORMAL or XSS.
+The request will be reflected by the test page and analyzed by the monitoring service.
 
 ## Model Files