Project Case Study

Machine Learning-Based Cyber Attack Detection in Simulated Enterprise Networks

A cybersecurity engineering project that combines network simulation, packet analysis, machine learning, and real-time visualization to detect malicious activities such as port scanning, brute-force attempts, and denial-of-service attacks within a simulated enterprise environment.

April 2026 Imran Sarwar
Cybersecurity Machine Learning Intrusion Detection Network Security Python Flask GNS3 Wireshark Threat Detection Security Analytics
Machine Learning-Based Cyber Attack Detection in Simulated Enterprise Networks

ML-Powered Cyber Attack Detection

A practical cybersecurity engineering project designed to simulate real-world attack scenarios, capture network traffic, extract behavioral features, and apply machine learning techniques to identify malicious activities in near real-time.

  • Enterprise Network Simulation using GNS3
  • Real Traffic Capture & Packet Analysis via Wireshark/Tshark
  • Machine Learning-Based Attack Classification
  • Flask Dashboard for Threat Visualization
Machine Learning Security Dashboard Interface

The Detection Challenge

Traditional security monitoring solutions often depend on static signatures and predefined rules. As cyber threats continue evolving, organizations require more intelligent detection mechanisms capable of identifying abnormal behavior patterns within network traffic. Detecting suspicious activity before it impacts critical systems remains one of the biggest challenges for modern security teams.

The Engineered Solution

I designed a simulated enterprise network environment where both legitimate and malicious traffic could be generated, captured, analyzed, and classified. Using machine learning models and traffic feature extraction techniques, the platform was able to distinguish normal network activity from cyber attacks and present the results through a real-time monitoring dashboard.

Simulated Scenarios & Functional Capabilities

Attack Vector Coverage

  • Port Scanning: Simulated reconnaissance attempts used to identify open ports and exposed services.
  • Brute Force Attacks: Repeated authentication attempts generated against target infrastructure systems.
  • Denial of Service (DoS): High-volume traffic patterns generated to simulate network resource exhaustion attacks.
  • Suspicious Access Attempts: Unauthorized cross-zone traffic flows introduced to evaluate anomaly detection capability.

Intelligent Detection Pipeline

Traffic Generation (Legitimate & Kali Linux Vectors)
Packet Capture (Live Wire Interfaces via GNS3/Tshark)
Feature Extraction (Flow Profiles & Protocol Metadata)
Dataset Serialization (Pandas Matrix & CSV Buffering)
Machine Learning Analysis (Scikit-Learn Classification Models)
Live Threat Visualization (Flask Dashboard Interface)

Technical Design Ecosystem

Network Emulator GNS3 (Enterprise Lab Virtualization)
Attack Platform Kali Linux (Malicious Traffic Generation)
Monitoring Platform Ubuntu Linux (IDS Node Engine)
Packet Analysis Wireshark, Tshark Engine
Programming Language Python 3
Machine Learning Scikit-Learn (Random Forest, SVM Algorithms)
Dashboard Framework Flask (Real-Time Web Interface UI)
Data Analysis Pandas, NumPy Arrays

Security Engineering Perspective

This project demonstrates the convergence of networking, cybersecurity, machine learning, and software development. Beyond simply identifying attacks, it explores how behavioral analysis and intelligent monitoring can enhance security visibility and support modern SOC, detection engineering, and threat hunting operations.

AI-Powered IDS

Intelligent Threat Detection

Project Assets & Demonstration

Review the project source code, machine learning implementation, and live attack detection demonstrations.

View GitHub Repository Watch Demonstration

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