Malware is one of the most serious security threats on the Internet today. This project aims to build an intelligent system capable of detecting and classifying various types of malware using machine learning techniques combined with both static and dynamic analysis.

Table of Content 🔖

Description 🖌️

This project utilizes machine learning techniques to build a robust malware detection system capable of analyzing files to identify various types of malware. By leveraging both static and dynamic analysis, the system can efficiently detect and classify malicious files even if they employ obfuscation techniques or have never been seen before.

Malware Types 🔍

Malware can be categorized into various types, each with unique characteristics and malicious intents. Some of the common types of malware analyzed by this system include:

  • Generic Malware: A broad category of malware that exhibits common malicious behavior or characteristics but doesn’t belong to a specific type.
  • Trojan: Malware disguised as legitimate software to trick users into installing it, often allowing attackers to gain unauthorized access to the system.
  • Ransomware: Malware that encrypts a victim’s files and demands ransom for the decryption key.
  • Worm: Self-replicating malware that spreads across networks without user interaction.
  • Backdoor: Allows attackers to bypass normal authentication and gain unauthorized access to a system.
  • Spyware: Secretly monitors user activity and gathers information, often used for stealing personal data.
  • Rootkit: Enables attackers to maintain persistent, undetectable access to a system by hiding processes, files, or data.
  • Encrypter: Encrypts files or network traffic, often used by ransomware or other malicious software.
  • Downloader: Downloads and installs additional malicious files onto the infected system.

Malware Analysis Mechanism 🛠️

Malware analysis is performed using two primary techniques: static and dynamic analysis.

Static Analysis

Purpose: Analyzing the file’s structure or code without executing it. This method is useful for detecting known malware patterns or suspicious attributes.

Steps for Feature Extraction:

  • Portable Executable (PE) Header Analysis: Extract information like the Import Table, Export Table, and Section Table.
  • Opcode Sequences: Extract opcode sequences from the binary code of the file to identify suspicious patterns.
  • Strings Extraction: Extract embedded strings to find indicators of malware, like URLs or registry keys.
  • Signature Matching: Compare file signatures with a database of known malware signatures using YARA rules.

Dynamic Analysis

Purpose: Executing the file in a controlled environment (sandbox or virtual machine) to observe its behavior. This method helps detect malicious activities such as:

  • Downloading additional malware.
  • Modifying the system registry.
  • Establishing unauthorized network connections.
  • Injecting code into other processes.

Project Implementation 💻

Dataset Preparation

The dataset used for this project includes various malware samples along with labels indicating their respective types and attributes. The dataset is preprocessed to ensure consistency and includes features extracted using static and dynamic analysis techniques.

Feature Extraction

Features are extracted using tools like:

  • pefile: For analyzing the structure of Portable Executable files.
  • lief: For parsing PE, ELF, and Mach-O files.
  • yara: For applying custom rules to identify malicious patterns in binary files.
  • capstone: For disassembling binary files and analyzing low-level instructions.

Model Training

A Random Forest Classifier model is trained on the extracted features to classify files into different malware categories based on their attributes. The model is evaluated on a separate validation set to ensure its accuracy and robustness.

Evaluation and Testing

The trained model is tested on unseen data to assess its performance. The evaluation metrics include accuracy, precision, recall, and F1-score for each malware type.

Getting Started 🚀