MLSecScan - in a very simple and short sentence , this is a vu

项目说明

MLSecScan

MLSecScan is an ad web application scanner that combines machine learning with traditional security testing techniques. It provides real-time scanning capabilities, intelligent detection, and a modern web dashboard for monitoring scan progress and results.

Table of Contents

Features

Installation

Quick Start

Usage

Web Dashboard

Configuration

Security Considerations

Contributing

License

Disclaimer

Features

Core Capabilities

Intelligent Crawling

- Ad URL discovery
- Smart filtering and prioritization
- Depth-controlled crawling
- Resource-aware scanning

ML-Based Detection

- Anomaly detection models
- Pattern recognition
- Adapti
- False positi

Real-Time Dashboard

- Li monitoring
- Interacti
- Dynamic updates
- Performance metrics

Security Testing

Comprehensi

- SQL Injection detection
- Error-based detection
- Time-based detection
- Boolean/Union-based detection
- Cross-Site Scripting (XSS) detection
- Custom signature support
- Path tra detection
- File inclusion

Security Features

- Tor proxy support for anonymous scanning
- Rate limiting and request throttling
- SSL options
- Cookie handling and session management
- Request randomization
- User-agent rotation

Analytics and Reporting

Ad Analytics

- distribution
- Response time analysis
- Error rate tracking
- Custom signature matching
- Parameter-based grouping
- Sorted reporting

Enhanced Reporting

- Parameter-based organization
- Sorted counts by parameter
- Detailed grouping
- Interacti charts
- Exportable HTML reports
- Customizable report formats

Configuration Options

Flexible Configuration

- Customizable scan depth
- Adjustable thread count
- Configurable timeouts
- Custom payload support
- Memory usage optimization
- Batch processing options

Installation

Prerequisites

Python 3.8 or higher

Tor service (optional, for anonymous scanning)

Git

pip (Python package manager)

System Requirements

Linux/Unix-based system (recommended)

Minimum 4GB RAM

2GB free disk space

Network connectivity

Installation Steps

1. Clone the repository:

2. Install dependencies:

3. Start Tor service (optional):

Quick Start

Run a basic scan:

Access the dashboard at to monitor the scan progress.

Usage

Basic Usage

Command Line Options

#### Essential Options

--url: Target URL to scan

--file: File containing URLs to scan

--depth: Maximum crawl depth (default: 2)

--threads: Number of concurrent threads (default: 3)

#### Security Options

--no-tor: Disable Tor proxy

--: Enable SSL

--max-errors: Maximum errors per URL before skipping (default: 5)

#### Output Options

--output-dir: Directory for output files

--custom-config: Path to custom configuration file

#### ML Options

--ml-model: Path to custom ML model file

--no-ml: Disable ML-based detection

Ad Options

--add-signature: Add custom signature

--list-signatures: List all custom signatures

Usage Examples

1. Basic scan with default settings:

2. Deep scan with multiple threads:

3. Scan with custom ML model:

4. Scan multiple URLs from file:

Web Dashboard

The web dashboard provides real-time monitoring of the scan progress and results. Access it at:

Dashboard Features

Li tracking

statistics

Response time analysis

Error rate monitoring

Interacti

Export capabilities

Parameter-based grouping

Sorted counts

Detailed reports

Enhanced Reporting Features

The scanner provides comprehensi capabilities:

Parameter-Based Grouping

- grouped by parameters
- Hierarchical organization
- Quick identification of critical issues

Detailed Information

- Parameter name
- type
- Affected URL
- Detailed description
- Se level
- Remediation suggestions

Interacti

- distribution charts
- Response time graphs
- Error rate analysis
- Custom chart generation

Export Options

- HTML report generation
- Custom report formats
- Data export capabilities
- Report customization

Configuration

Default Configuration

The default configuration is stored in config.json. You can modify:

Scan parameters

ML model settings

Dashboard options

Proxy settings

Rate limiting rules

Custom Signatures

Add custom signatures:

Security Considerations

Always obtain permission before scanning websites

Use responsibly and ethically

Consider rate limiting and resource usage

Follow security best practices

Keep the tool and dependencies updated

Monitor system resource usage

Implement proper error handling

Use secure configurations

License

This project is licensed under the MIT License - see the LICENSE file for details.

De by Otsmane Ahmed

项目信息

开发语言：Python

Star 数：12

技术要点

该项目属于安全软件方向的开源资源，代码结构清晰，适合安全研究人员学习参考。项目围绕in a very simple and short sentence , this is a vulnerability scanner that uses machine learning to detect any sort of vulnerability on a website展开，提供了可运行的实现，对理解相关安全机制、工具原理与攻防技术具有较高的研究价值。

从技术实现角度看，此类项目的核心在于对底层原理的深入理解与工程化落地。开发者在研究过程中可以结合官方文档与社区讨论，梳理项目的初始化流程、核心模块职责与数据流转，逐步掌握其中的技术细节与设计思路。

使用与学习建议

1. 下载源码压缩包，解压到本地环境。

2. 根据项目类型准备对应的运行环境与依赖。

3. 按照 README 中的步骤完成配置与部署。

4. 在隔离的测试环境中运行验证，避免影响生产系统。

免责声明

本资源来源于开源社区，仅供安全研究、技术学习与合法授权范围内的攻防演练使用，请勿用于任何非法或侵害他人权益的用途。使用本项目产生的一切后果由使用者自行承担。