为什么值得关注

在DRDO实习期间开发，通过实时演示提供端到端的机器学习流水线，并在标准网络安全数据集上比较DNN与基线模型。

适合谁使用

网络安全研究人员和分析师
探索安全威胁检测机器学习的科学家
学习应用机器学习的安全领域学生
需要演示型机器学习入侵检测系统的组织

典型使用场景

上传网络流量CSV文件以检测恶意连接（DoS、Probe、R2L、U2R）
将可执行文件特征分类为良性或恶意软件
比较深度神经网络和随机森林在同一数据上的性能
展示从数据预处理到部署的完整机器学习流程教学

项目优势

在单一应用中集成了两个独立的机器学习流水线（网络入侵+恶意软件）
提供可访问的实时演示，包括前端和API端点
包含基线比较（随机森林）以进行性能基准测试
覆盖完整机器学习工作流：数据预处理、模型训练、评估指标和部署

使用前须知

目前依赖静态数据集（NSL-KDD），而非实时数据包捕获
无身份验证或用户管理；适用于演示/研究，不适合生产环境
列出的未来改进（如可解释AI、多类恶意软件）表明当前存在缺口

README 快速开始

🛡️ AI-Based Cyber Threat Detection Framework

AI-powered cybersecurity platform for Network Intrusion Detection and Malware Classification

Developed during my internship at DRDO

🌐 Live Demo: https://cyber-threat-detection-backend.vercel.app/

⚙️ Backend API: https://cyber-threat-detection-backend-205p.onrender.com/

📖 Overview

Cyber threats continue to evolve rapidly, making traditional signature-based security solutions insufficient for detecting modern attacks. This project presents an AI-Based Cyber Threat Detection Framework that leverages Machine Learning to automatically identify malicious network activity and classify potentially harmful files.

The framework combines two independent machine learning pipelines into a single web application:

🌐 Network Intrusion Detection System (NIDS)
🦠 Malware Detection System

Both models are deployed behind a FastAPI backend and accessed through an interactive React dashboard, enabling users to upload datasets and receive real-time predictions.

🚨 Problem Statement

Organizations generate enormous amounts of network traffic every day.

Among millions of legitimate connections, attackers attempt to perform activities such as:

Denial of Service (DoS)
Network Probing
Unauthorized Access
Remote-to-Local Attacks
User-to-Root Exploits
Malware Distribution

Traditional antivirus software and Intrusion Detection Systems primarily rely on signature-based detection, which struggles to identify:

Zero-day attacks
Unknown malware variants
Modified attack patterns
Previously unseen threats

Machine Learning provides a data-driven approach by learning patterns from historical attack data instead of depending solely on predefined rules.

🎯 Objectives

This project aims to:

Detect malicious network traffic using Machine Learning
Classify files as Benign or Malware
Compare Deep Neural Networks against Random Forest baselines
Provide real-time predictions through REST APIs
Deploy an end-to-end AI-powered cybersecurity solution

🏗️ System Architecture

                     +----------------------+
                     |    User Uploads CSV  |
                     +----------+-----------+
                                |
                                v
                    +-----------------------+

Sentra