🛡️ SPECTRA-X: Tactical RF Evasion System

SPECTRA-X is an intelligent cyber-defense and telemetry platform designed to protect Wireless Sensor Networks (WSNs) and IoT devices against Broadband Jamming and Flood (Replay Packet) attacks. The system combines hardware-level threat detection, real-time spectrum monitoring, and Frequency Hopping Spread Spectrum (FHSS) capabilities to maintain communication reliability under hostile RF conditions.

By continuously analyzing RF activity, SPECTRA-X detects abnormal behavior, classifies threats, and automatically switches to a safer communication channel when necessary.

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Command & Control Center

Real-time spectrum analysis, waterfall heatmaps, and AI-assisted threat classification dashboard.

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Key Features

Multi-Layer Threat Detection

• Hardware-based hysteresis filtering on STM32
• Statistical variance analysis on the Python backend
• Noise and interference discrimination
• Real-time anomaly detection

Active Countermeasures (Evasion Mode)

• Manual frequency switching
• Autonomous FHSS support
• Secure channel migration
• Communication continuity during attacks

Real-Time RF Monitoring

• Live RSSI tracking
• Spectrum visualization
• Waterfall heatmap display
• Threat status indicators

Intelligent Threat Classification

• Continuous Wave (CW) Jamming detection
• Flood / Replay attack detection
• Statistical variance analysis
• Rule-based threat assessment

Data Logging

• CSV-based data recording
• Millisecond-resolution telemetry
• Machine learning dataset generation
• Historical attack analysis

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System Architecture

                 RF Environment
                        │
                        ▼
             ┌──────────────────┐
             │      CC1101      │
             │ RF Transceiver   │
             └────────┬─────────┘
                      │ SPI
                      ▼
             ┌──────────────────┐
             │ STM32F103C8T6    │
             │ Detection Layer  │
             └────────┬─────────┘
                      │ UART
                      ▼
       ┌─────────────────────────────────┐
       │ Python Command & Control Center │
       │ Threat Classification Engine    │
       │ Data Logger                     │
       │ Visualization System            │
       └────────────────┬────────────────┘
                        │
                        ▼
              Frequency Hopping
                 Countermeasure

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Hardware Architecture

Components Used

Component	Description
STM32F103C8T6	Main microcontroller
CC1101	RF transceiver
USB-to-TTL Adapter	PC communication
Breadboard	Rapid prototyping
3.3V Regulator	Power management

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Wiring Diagram

CC1101 Pin	STM32 Pin	Function
GND	GND	Ground
VCC	3.3V	Power
CSN	PA4	SPI Chip Select
SCK	PA5	SPI Clock
MOSI	PA7	SPI Master Out
MISO	PA6	SPI Master In

UART Communication

STM32	USB-TTL
PA9 (TX)	RX
GND	GND

Baud Rate: 115200

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Breadboard Prototype

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STM32CubeMX Configuration

The following settings were used:

• SPI1 Enabled
• Full Duplex Master Mode
• UART1 Enabled
• 115200 Baud Rate
• External HSE Crystal
• SWD Debug Interface

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Software Installation

Clone the Repository

git clone https://github.com/CinarSamet/spectra-x.git
cd spectra-x

Install Dependencies

pip install customtkinter matplotlib numpy pyserial

Configure Serial Port

Open config.py and update your serial port:

Windows:

SERIAL_PORT = "COM3"

Linux:

SERIAL_PORT = "/dev/ttyUSB0"

macOS:

SERIAL_PORT = "/dev/cu.usbserial"

Run the Dashboard

python main.py

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Detection Algorithm

Unlike traditional systems that rely solely on a fixed threshold, SPECTRA-X evaluates multiple RF characteristics:

• Average RSSI
• RSSI rise rate
• Moving-window variance
• Standard deviation (σ)

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Continuous Wave (CW) Jamming Detection

RSSI > Threshold
σ < 3.0

A high-power signal with very low variance is classified as:

Continuous Wave Jamming

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Flood / Replay Attack Detection

RSSI > Threshold
σ > 3.0

A high-power signal with irregular fluctuations is classified as:

Flood / Replay Attack

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Dataset Generation

The platform can generate labeled RF telemetry datasets in CSV format:

timestamp,rssi,variance,label
1250,47,1.2,NORMAL
1260,91,0.8,JAMMING
1270,88,8.4,FLOOD

These datasets can be used to train:

• Random Forest
• XGBoost
• Support Vector Machines (SVM)
• Neural Networks

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Roadmap

Version 1.0

• STM32 RF Telemetry
• Python Dashboard
• Waterfall Visualization
• CSV Data Logging
• Hysteresis-Based Detection

Version 2.0

• Random Forest Integration
• XGBoost Classifier
• Autonomous Frequency Hopping
• Threat History Analysis
• ML Training Interface

Version 3.0

• Multi-CC1101 Support
• Mesh Network Protection
• SDR Integration
• LoRa Support
• Edge AI Module

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Applications

• Wireless Sensor Networks (WSN)
• Industrial IoT Systems
• Smart Agriculture Infrastructure
• Critical Telemetry Links
• Drone Communication Networks
• RF Security Research
• Embedded Security Platforms
• Defense and Aerospace Communication Systems

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License

This project is licensed under the MIT License.

For more information, see:

LICENSE

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Developer

Samet Çınar

Computer Engineer

Embedded Systems • RF Security • IoT • Artificial Intelligence

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Detect. Analyze. Evade.

SPECTRA-X — Tactical RF Evasion System