🛡️ AETHER: AI-Enhanced Threat Hunting & Execution Response

Advanced AI-Powered Malware Detection Framework

A next-gen, AI-augmented malware detection system combining static, behavioral, and heuristic analysis with deep learning for high-fidelity threat classification.

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Project Overview

AETHER is a comprehensive malware detection framework that leverages cutting-edge techniques including deep static code analysis, sandbox-based dynamic inspection, memory forensics, and heuristic profiling. Enhanced by a fine-tuned LLaMA 3.2 model specialized in cybersecurity, AETHER is engineered to detect and classify both known and emerging threats.

This is not just an antivirus — it's a threat hunting platform, crafted for researchers, analysts, and defenders.

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Key Capabilities

Capability	Description
Static Analysis	Disassembles APK/PE files, extracts opcodes, detects obfuscation, analyzes permissions
Behavioral Sandboxing	Executes binaries in Docker-based sandbox; traces syscalls, file/net activity
AI Threat Classification	LLaMA 3.2 classifies malware families, scores severity, and detects anomalies
Memory Forensics	Captures volatile memory, extracts shellcode, injected DLLs, encrypted strings
Heuristic Profiling	Custom TTP rules aligned with MITRE ATT&CK, detects anti-VM/debug behavior
Real-Time Threat Feeds	Syncs with MalwareBazaar, VirusTotal, and Hybrid Analysis for latest IOCs
Automated Reports	Generates detailed downloadable reports with logs, scores, graphs

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Project Structure

malware-detection/
│── src/main/java/com/malwaredetection
│   ├── entity/                  # POJOs for DB entities
│   ├── validation/              # Input validation logic
│   ├── dao/                     # Database access layer
│   ├── service/                 # Core logic for file scanning and orchestration
│   ├── userinterface/           # JavaFX and REST controllers
│   ├── utils/                   # File handling, hash generation, helpers
│   ├── config/                  # Config for DB, AI, external APIs
│   ├── exception/              # User auth and permissions
│   ├── analysis/
│   │   ├── staticanalysis/      # Opcode and disassembly based analysis
│   │   ├── behavioralanalysis/   # TTP rules, anomaly checks
│   │   ├── ai/                  # AI/LLM-based threat classification
|── src/main/resources/         # Scripts, configs, trained AI models
│── pom.xml                     # Maven dependencies
│── README.md                   # Project documentation

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Tech Stack

• Core Language: Java
• Frontend: JavaFX
• AI Model: Fine-Tuned LLaMA 3.2
• Containerization: Docker (for behavioral sandboxing)
• Threat Feeds: MalwareBazaar, VirusTotal, Hybrid Analysis APIs
• Supported Formats: .apk, .exe, .dll

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Features Breakdown

Static Code Analysis

• Opcode pattern recognition
• Obfuscation & packing detection
• Suspicious API imports & permissions analysis

Behavioral Sandboxing (Docker)

• System call tracing
• Process tree generation
• File/network/registry activity logs

AI Classification (LLaMA 3.2)

• Trained on malware logs, memory dumps
• Zero-day detection via anomaly scoring
• Explainable outputs for analysts

Memory Forensics

• In-memory shellcode & artifact extraction
• Runtime snapshots and analysis

Threat Intel Sync

• Automatic updates from OSINT APIs
• Integrated malware signature enrichment
• Self-updating threat DB

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Reporting & Dashboard

• Visualized findings (entropy, strings, import/export tables)
• Threat summaries and AI analysis
• Downloadable detailed PDF/HTML reports
• Interactive JavaFX-based UI

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Setup Instructions

Prerequisites

• Java 17+
• Maven 3+
• Docker installed and running
• Internet access for threat feed APIs

Running the Project

# Clone the repository
git clone https://github.com/pankhuriVarshney/malware-detection-software.git
cd malware-detection

# Build the project
mvn clean install

# Run the application
mvn javafx:run