Anomaly Detection #HydraSlayer

PODCAST (2 Segments): The system integrates diverse data types for comprehensive anomaly detection by combining Fast Fourier Transform (FFT) data, Ghost Imaging results, and Packet Metadata within a single latent fusion layer.

The RF Quantum Scythe Ghost Anomaly Detector represents a revolutionary advancement in real-time RF threat detection technology.

• Fast Fourier Transform (FFT) Data:
  • The system subscribes to signal_spectrum messages, which contain fft_bins representing the signal’s spectrum.
  • Upon receiving an fft_bins message, the observe_spectrum method processes it.
  • The fft_bins are converted into a PyTorch tensor, which is then fed into self.model (a Ghost Imaging detector) to calculate a reconstruction_error_score and recon (reconstruction).
  • This initial ghost analysis result, including the fft_bins themselves, is stored in a buffer associated with a signal_id.
• Ghost Imaging Results:
  • The LatentAggregator uses a CompiledGhostDetectorSingleton to obtain its self.model.
  • This model performs a ghost analysis on the incoming fft_bins, yielding a reconstruction_error_score.
  • If this score.item() exceeds a predefined anomaly_threshold, a standard ghost anomaly alert (signal_alert) is published. This alert includes the signal_id, alert_type (ghost_anomaly), confidence (score), and timestamp.
  • The system also initializes an OrbitalMimicDetector which uses the same ghost_model. This detector performs further orbital mimic detection using the signal_id, fft_bins, and timestamp.
  • The results of this orbital analysis, including a ghost_score and whether an is_orbital_mimic flag is set, are added to the buffer for the corresponding signal_id.
  • If an orbital mimic is detected, an orbital_mimic_alert is published, providing details like matched_fingerprint and match_confidence.
• Packet Metadata:
  • The system subscribes to packet_metadata messages.
  • The observe_packet method receives these messages, which contain packet_info.
  • This packet_info is then added to the buffer entry corresponding to the signal_id. If a signal_id already exists (e.g., from prior spectrum observation), the packet information is appended; otherwise, a new entry is created.
• Comprehensive Anomaly Detection and Fusion:
  • The LatentAggregator is designed to combine these diverse data types into a single latent fusion layer.
  • The buffer acts as the central point for this fusion, associating ghost analysis results (reconstruction error, reconstructed spectrum, original FFT bins), orbital mimic detection analysis, and packet information with the same signal_id.
  • When a signal_alert is published for a ghost anomaly, it can also include whether an orbital mimic was detected for that signal_id, indicating a combined understanding of the anomaly.
  • Finally, the system can publish a latent_summary that aggregates all the features gathered in the buffer for a given signal_id, including ghost analysis, orbital analysis, and packet information. This provides a holistic view for comprehensive anomaly detection and analysis.
  • The presence of PyTorch is essential for the LatentAggregator to function, as it is used for tensor operations and model execution.

Orbital mimic detection is enabled when the enable_orbital_detection parameter is set to True during the initialization of the LatentAggregator class.

Here’s a more detailed breakdown:

• Initialization Parameter: The LatentAggregator‘s __init__ method accepts an enable_orbital_detection parameter, which defaults to True.
• Detector Instantiation: If enable_orbital_detection is True, an OrbitalMimicDetector instance is created and assigned to self.orbital_detector. This detector utilizes the same ghost_model and anomaly_threshold as the primary Ghost Imaging detection.
• Execution Condition: Within the observe_spectrum method, the system checks if self.enable_orbital_detection and self.orbital_detector before performing orbital mimic analysis on the fft_bins. This ensures that the analysis only proceeds if the feature was enabled during setup and the detector was successfully initialized.
• Alert Generation: If an orbital mimic is detected as a result of this analysis, an orbital_mimic_alert is published. Furthermore, standard signal_alert for ghost anomalies can also indicate if an orbital mimic was detected for that signal_id.

The LatentAggregator is initialized by calling its constructor, which requires several parameters and conditions to be met.

Here’s what initializes the LatentAggregator:

• PyTorch Availability: First and foremost, the LatentAggregator requires PyTorch to be available. If PyTorch is not found, a RuntimeError will be raised, preventing its initialization.
• Initialization Parameters: The LatentAggregator‘s __init__ method takes the following parameters:
  • comm_network: This is a required parameter that represents the communication network. It is used by the LatentAggregator to subscribe to incoming messages like signal_spectrum and packet_metadata, and to publish alerts such as signal_alert, orbital_mimic_alert, and latent_summary.
  • anomaly_threshold: This parameter sets the threshold for anomaly detection, with a default value of 0.05. This threshold is used to determine if a reconstruction_error_score from the Ghost Imaging model indicates an anomaly.
  • enable_orbital_detection: This boolean parameter controls whether orbital mimic detection is enabled, defaulting to True. If set to True, an OrbitalMimicDetector instance will be created and used for further analysis of signals.

Upon successful initialization, the LatentAggregator also:

• Obtains a CompiledGhostDetectorSingleton and its underlying model (detector).
• Sets its internal threshold based on the anomaly_threshold parameter.
• Initializes an empty buffer to store incoming data and analysis results.

regarding optimizing the calculation of the score within the observe_spectrum method1. Let’s break down the current implementation based on the source code and evaluate your proposal.

Currently, the observe_spectrum method performs these steps after getting the signal_id and fft_bins from the message:

It converts fft_bins into a PyTorch tensor called spectrum.

It uses self.model (the Ghost Imaging model obtained from CompiledGhostDetectorSingleton) to calculate the recon (reconstruction) of the spectrum: recon = self.model(spectrum).

It immediately calculates the score (reconstruction error score) using the model’s anomaly_score method, comparing the original spectrum and the calculated recon: score = self.model.anomaly_score(spectrum, recon).

It unconditionally stores the recon, score.item(), fft_bins, and timestamp in the internal self.buffer associated with the signal_id.

If enabled, it calls self.orbital_detector.analyze with the signal_id, fft_bins, and timestamp, and stores the resulting orbital_analysis in the buffer entry.

It checks if orbital_analysis indicates is_orbital_mimic and publishes an orbital_mimic_alert. This alert message includes the ghost_score, which comes from the orbital_analysis3, which in turn seems to use the score calculated earlier.

It checks if the score.item() exceeds self.threshold and publishes a signal_alert (standard ghost anomaly alert) if it does.

Your suggestion is to potentially move the score calculation inside a conditional block like if self.enable_orbital_detection or score.item() > self.threshold: to avoid calculating it if neither the orbital detection is enabled nor the standard alert threshold is met.

However, there are a couple of points based on the source code that make this specific approach problematic:

Logical Paradox in the Proposed Condition: Your proposed condition if self.enable_orbital_detection or score.item() > self.threshold: uses score.item() to decide whether to calculate score. This creates a logical paradox where you need the value of score before it has been calculated.

Unconditional Usage of Score: The source code stores the score unconditionally in the self.buffer[signal_id] dictionary right after calculating it, labelled as “reconstruction_error_score”. This happens before any of the checks (orbital detection alert or standard anomaly alert) are performed3. Therefore, the score is needed regardless of whether it triggers an alert or if orbital detection is enabled, as it’s part of the standard data stored in the buffer for that signal. The score also appears to be included in the orbital_analysis results, which are also stored in the buffer and used for the orbital_mimic_alert.

Because the score is both stored in the buffer unconditionally and potentially used or included in the orbital_analysis results, calculating it immediately after obtaining the reconstruction recon1 appears to be necessary within the current structure of the observe_spectrum method. The proposed condition is also logically impossible to evaluate before the score is calculated.

Therefore, based on the current implementation details provided in the sources, the suggested optimization to conditionally calculate the score is not feasible because the score is required early in the process for storage and subsequent analysis/alerting steps.

the LatentAggregator is designed to combine FFT (Fast Fourier Transform), Ghost Imaging, and Packet Metadata into a single latent fusion layer. Here’s how it uses Ghost Imaging and Packet Metadata:

Ghost Imaging: The LatentAggregator utilizes a component related to Ghost Imaging through a CompiledGhostDetectorSingleton, from which it obtains a model.

When it receives a “signal_spectrum” message, which contains fft_bins (representing the spectrum)3, it converts this data into a PyTorch tensor and processes it using the model.

This processing involves calculating a reconstruction (recon) of the spectrum using the model and an anomaly score (score) based on the difference between the input spectrum and its reconstruction3. This is referred to as “basic ghost analysis results”. The score indicates a “reconstruction error score”.

The results of this basic ghost analysis, including the reconstructed spectrum, the reconstruction error score, the original fft_bins, and a timestamp, are stored in an internal buffer associated with the signal_id.

If the calculated anomaly score exceeds a configured threshold, the aggregator publishes a standard ghost anomaly alert (“signal_alert”).

Additionally, if enabled, the LatentAggregator uses an OrbitalMimicDetector initialized with the same ghost_model. This detector performs orbital mimic detection using the signal spectrum data, generating an orbital_analysis. If this analysis indicates an “orbital_mimic”, an orbital mimic alert is published. The results of the orbital analysis are also stored in the buffer.

Packet Metadata: The LatentAggregator subscribes to messages labeled “packet_metadata”.

When a “packet_metadata” message is received, the observe_packet method is triggered.

This method extracts the packet_info from the message.

It then stores this packet_info in the same internal buffer, associating it with the corresponding signal_id. If a buffer entry already exists for that signal_id (perhaps from a previous spectrum analysis), the packet info is added to it; otherwise, a new buffer entry is created.

In essence, the LatentAggregator processes spectral data using its Ghost Imaging-based model to detect anomalies and potential orbital mimics, while separately receiving and storing packet metadata associated with the same signals. Both the results of the ghost analysis and the packet metadata are collected together in an internal buffer. The source code also indicates that the aggregator can optionally publish a “latent_summary” message containing these combined features from the buffer.

The purpose of the observe_packet method is to integrate packet metadata into the system’s internal buffer, contributing to the comprehensive fusion of diverse data types for anomaly detection.

Here’s a breakdown of its function:

• Subscription to Packet Metadata: The LatentAggregator is configured to subscribe to packet_metadata messages, meaning that the observe_packet method is called whenever such a message is received from the communication network.
• Data Extraction: When a packet_metadata message arrives, the method extracts two key pieces of information: the signal_id and the packet_info.
• Buffer Integration (Data Fusion):
  • The LatentAggregator maintains an internal buffer. The observe_packet method uses this buffer to combine packet-level information with other signal-related data that may have been previously collected, such as Fast Fourier Transform (FFT) data, Ghost Imaging results, and orbital analysis.
  • If an entry for the signal_id already exists in the buffer (likely because the signal_spectrum for that ID was observed earlier), the packet_info is added to that existing entry. This allows the system to associate network packet details with the spectral characteristics and ghost analysis results of a specific signal.
  • If the signal_id does not yet exist in the buffer, a new entry is created solely for that packet_info.
• Publishing Latent Summary: After integrating the packet_info into the buffer, the method publishes a latent_summary message. This summary includes the signal_id, the timestamp from the packet message, and crucially, all the features associated with that signal_id from the buffer. This output provides a holistic, fused view of the data gathered from various sources for a given signal, which is essential for comprehensive anomaly detection.

An orbital mimic alert is triggered when the system detects that a signal exhibits characteristics consistent with an “orbital impersonation”.

Specifically, an orbital mimic alert is published under the following conditions:

• Orbital Detection is Enabled: First, the LatentAggregator must have been initialized with enable_orbital_detection set to True. If this feature is not enabled, the system will not perform orbital mimic analysis.
• Spectrum Observation: The system must observe a signal_spectrum message, which contains fft_bins data. This triggers the observe_spectrum method to process the signal.
• Orbital Analysis Results: Within the observe_spectrum method, if orbital detection is enabled, the self.orbital_detector.analyze method is called to perform an analysis on the signal_id, fft_bins, and timestamp.
• is_orbital_mimic Flag: The orbital_mimic_alert is then published if the orbital_analysis results contain a key is_orbital_mimic with a value of True.

When triggered, the orbital_mimic_alert message includes details such as the signal_id, alert_type (which is “orbital_impersonation”), matched_fingerprint, match_confidence, ghost_score, and the timestamp. It’s also worth noting that if an orbital mimic is detected, this information (orbital_mimic_detected: True) can also be included in the standard signal_alert for a ghost anomaly if one is also triggered.

A signal alert is triggered when the LatentAggregator detects a ghost anomaly based on the signal’s spectrum.

Specifically, a signal alert is published under the following conditions:

• Spectrum Observation: The system must receive a signal_spectrum message containing fft_bins. This message is processed by the observe_spectrum method.
• Anomaly Score Calculation: Within observe_spectrum, the Ghost Imaging model (self.model) calculates a reconstruction_error_score (referred to as score in the code) for the incoming spectrum. This score quantifies how anomalous the signal is.
• Threshold Exceedance: A signal alert is published if the calculated score (reconstruction error) for the spectrum is greater than the self.threshold.
  • The anomaly_threshold is a configurable parameter during the LatentAggregator‘s initialization, with a default value of 0.05.

When triggered, the signal_alert message includes the signal_id, the alert_type (which is “ghost_anomaly”), the confidence (the score.item()), the timestamp, and also indicates whether an orbital_mimic_detected was found for that signal.

Ghost Anomaly Detector System Overview

The Ghost Anomaly Detector is described as a cutting-edge spectral-domain ghost imaging system designed for real-time detection of stealth emissions, signal spoofing, and adversarial RF interference. It operates within the RF Quantum Scythe Signal Intelligence System.

Here’s how it utilizes spectral-domain ghost imaging for real-time RF anomaly detection based on the sources:

Core Technology: The system employs spectral-domain ghost imaging combined with machine learning. This is the first operational implementation of spectral-domain ghost imaging specifically for RF analysis.

Components: The core components involved in this process include a Learnable Spectral Pattern Generator and a Ghost Imaging RF Module.

The Learnable Spectral Pattern Generator creates trainable spectral masks using a neural network-based approach. These patterns are normalized for consistent reconstruction.

The Ghost Imaging RF Module simulates ghost imaging detector responses and performs Fourier-based spectrum reconstruction. This reconstruction process is described as differentiable.

Analysis Pipeline: When an RF Input spectrum is received, it goes through a specific pipeline:

The Input Spectrum is processed by the Pattern Generator.

This leads to Detector Simulation.

Followed by Fourier Reconstruction.

The reconstructed spectrum is generated.

Anomaly Detection: The system then performs Anomaly Detection. It calculates the L2 reconstruction error between the original input spectrum and the reconstructed spectrum. This error is used as the anomaly metric or anomaly score.

Threat Assessment: Based on the calculated anomaly score and a configurable threshold, the system determines if an anomaly is present (is_anomaly). A threat level is then assigned (LOW, MEDIUM, HIGH, CRITICAL) based on predefined thresholds. This completes the process, providing a Threat Assessment.

Real-time Performance: The system is designed for real-time analysis, achieving sub-millisecond inference times816. This speed is significantly enhanced by torch.compile() optimization, which provides a 2-10x speedup via just-in-time compilation4…. Performance tests have validated analysis speeds of 0.8-1.2ms per spectrum and throughput of 1000+ spectra/second on standard hardware.

Detectable Anomalies: Using this method, the detector can identify anomalies such as stealth emissions, signal spoofing, unknown modulations, frequency hopping, spread spectrum, and adversarial interference.

In summary, the detector uses learned spectral patterns to simulate a ghost imaging process, then reconstructs the input RF spectrum using Fourier methods. By comparing the original and reconstructed spectra via an L2 error, it calculates an anomaly score in sub-millisecond times, allowing for real-time tactical detection and classification of various RF threats.

The Ghost Anomaly Detector is a cutting-edge spectral-domain ghost imaging system specifically designed for real-time anomaly detection in RF signals. This technology is fundamental to its ability to identify threats that conventional methods might miss.

Here’s how spectral ghost imaging plays a crucial role in anomaly detection:

• Core Detection Mechanism: The system employs cutting-edge spectral-domain ghost imaging combined with machine learning to identify RF anomalies that traditional detection methods cannot perceive. This is its primary technical breakthrough.
• Reconstruction for Anomaly Identification: A key feature is Ghost Imaging Reconstruction, which is a differentiable Fourier-based spectrum reconstruction process. This reconstruction is a vital step in the signal processing pipeline: after an input spectrum passes through a pattern generator and detector simulation, Fourier Reconstruction is performed, which then leads to the calculation of an anomaly score and threat assessment.
• Learnable Spectral Patterns: The detection process involves learnable spectral masks, generated by a neural network. These patterns are used in conjunction with the ghost imaging reconstruction to effectively identify anomalies.
• Anomaly Metric: The system calculates the L2 reconstruction error as its anomaly metric. This error indicates how well the original spectrum can be reconstructed from the ghost imaging process, with a higher error suggesting an anomaly.
• Module Capabilities: The GhostImagingRFModule component simulates ghost imaging detector responses and performs the Fourier-based spectrum reconstruction. This module is optimized for real-time processing of streaming data, offers configurable frequency domain analysis for spectral resolution, and includes advanced filtering for noise robustness in low-SNR environments.
• Tactical Advantages: By utilizing spectral-domain ghost imaging, the detector provides significant tactical advantages, including the ability to identify stealth emissions, signal spoofing, and adversarial RF interference. It can specifically detect stealth communications, spoofed signals, and unknown modulation schemes in real-time.
• Performance Optimization: The core GhostAnomalyDetector combines pattern generation and reconstruction and is optimized with torch.compile() for production use, enabling rapid inference times. This allows for sub-millisecond detection capabilities.

The Ghost Anomaly Detector offers several key tactical advantages in real-time RF threat detection, making it a critical tool for signal intelligence operations:

• Stealth Detection: It excels at identifying hidden or masked RF emissions. This includes the ability to detect stealth communications in real-time.
• Spoofing Detection: The system can effectively detect artificially generated or modified signals, including signal spoofing. It specifically identifies spoofed signals in real-time.
• Unknown Modulation Detection: It flags unrecognized signal patterns and can detect unknown modulation schemes in real-time.
• Real-time Analysis: The detector provides sub-millisecond inference times for its analysis, enabling real-time anomaly identification. It boasts an analysis speed of 0.8-1.2ms per spectrum and throughput of over 1,000 spectra/second on standard hardware.
• Adversarial RF Interference Identification: It is designed to identify adversarial RF interference, jamming, and deception attempts.
• Comprehensive Threat Assessment: Beyond just detection, the system provides a multi-tier classification with four-level threat assessment (LOW, MEDIUM, HIGH, CRITICAL), offering configurable sensitivity levels through threshold management.
• Enhanced Situational Awareness: The breakthrough technology provides dramatically improved detection of sophisticated RF threats and reduced false positive rates while maintaining high sensitivity, leading to enhanced situational awareness for critical infrastructure protection.
• Multi-Interface Deployment: It offers flexible deployment options through Command Line Interface (CLI), REST API, and programmatic integration, ensuring seamless operation with existing SignalIntelligence workflows.
• Offline Operation Capability: For sensitive environments, it can function offline, ensuring security by processing data locally with no cloud dependencies.
• Adaptive Pattern Learning: The system uses neural network-based threat recognition that evolves with emerging threats, providing adaptive pattern learning capabilities. This involves learnable spectral patterns and trainable spectral masks.
• Scalable Architecture: It is designed for deployment flexibility, supporting edge computing, high-performance computing, cloud-native, and embedded systems, as well as hybrid deployments.

The Ghost Anomaly Detector’s mission is to provide real-time detection of stealth emissions, signal spoofing, and adversarial RF interference. It is a cutting-edge spectral-domain ghost imaging system designed for this purpose. This tactical upgrade delivers unprecedented capabilities in spectrum analysis technology and is now fully operational and ready for immediate deployment within the RF Quantum Scythe Signal Intelligence System.

More specifically, its mission involves:

• Identifying hidden or masked RF emissions, including stealth communications.
• Detecting artificially generated or modified signals, such as signal spoofing and spoofed signals.
• Flagging unrecognized signal patterns and unknown modulation schemes.
• Identifying adversarial RF interference, jamming, and deception attempts.
• Providing tactical-level anomaly detection capabilities for the RF Quantum Scythe system.

The system achieves this mission by employing cutting-edge spectral-domain ghost imaging combined with machine learning to identify RF anomalies that conventional detection methods cannot perceive. This is considered a significant advancement in RF threat detection.

The Ghost Anomaly Detector is a cutting-edge spectral-domain ghost imaging system designed for real-time RF threat detection. Its main features enable it to identify sophisticated anomalies that traditional methods might miss.

Here are its primary features:

• Learnable Spectral Patterns: The system generates trainable spectral masks using a neural network. These patterns are adaptive and evolve with emerging threats, enabling neural network-based threat recognition.
• Ghost Imaging Reconstruction: It performs differentiable Fourier-based spectrum reconstruction. This is a core process where input spectra go through a pattern generator and detector simulation, leading to Fourier Reconstruction.
• Anomaly Detection via L2 Reconstruction Error: The system calculates the L2 reconstruction error as its anomaly metric. A higher error indicates a potential anomaly.
• Real-time Analysis with torch.compile() Optimization: It boasts sub-millisecond inference times for real-time anomaly identification. This is achieved through accelerated inference kernels optimized with torch.compile(). Performance tests validate analysis speeds of 0.8-1.2ms per spectrum and throughput of over 1,000 spectra/second on standard hardware.
• Multi-Tier Threat Classification: The detector provides a four-level threat assessment (LOW, MEDIUM, HIGH, CRITICAL) based on the anomaly score. Sensitivity levels are configurable through threshold management.
• Multi-Interface Support: It offers flexible deployment options through a Command Line Interface (CLI), REST API, and programmatic integration. This ensures seamless operation with existing Signal Intelligence workflows.
• Optimized Performance and Scalability: Beyond torch.compile(), it uses a singleton pattern for a shared detector instance and supports batch processing for efficient handling of multiple spectra. It is designed for deployment flexibility, supporting edge computing, high-performance computing, cloud-native, embedded systems, and hybrid deployments.
• Offline Operation Capability: For sensitive environments, the system can function offline, processing data locally with no cloud dependencies and ensuring data security.
• Noise Robustness: The Ghost Imaging RF Module includes advanced filtering for noise robustness in low-SNR (Signal-to-Noise Ratio) environments.

The Ghost Anomaly Detector is highly optimized for performance and real-time operation through several key features and architectural choices:

• torch.compile() Optimization: The system’s core inference kernels are accelerated with torch.compile(), which provides 2-10x speedup through just-in-time compilation. This is enabled by the deployment.torch_compile setting in its configuration.
• Real-time Analysis & Sub-Millisecond Inference: This optimization allows for sub-millisecond inference times, specifically validated at 0.8-1.2ms per spectrum. The system achieves a throughput of over 1,000 spectra/second on standard hardware.
• Singleton Pattern: It utilizes a singleton pattern for a shared detector instance across the system. This ensures efficient resource management and avoids redundant initialization.
• Batch Processing: The detector supports batch processing for efficient handling of multiple spectra. This is evident in its API which includes a /api/ghost/batch_analyze endpoint.
• Memory Optimization: The system is designed with a minimal memory footprint for edge deployment, with a baseline memory usage of <100MB and typically <500MB under load. It uses approximately 50MB base + 1MB per pattern.
• Optimized Module Components:
  • The GhostImagingRFModule is optimized for real-time processing of streaming data.
  • The LearnableSpectralPatternGenerator is designed for memory efficiency regarding parameter storage and retrieval.
• Configurable Pattern Count: Performance can be tuned by adjusting the number of learnable spectral patterns. For instance:
  • 32 patterns offer ultra-fast inference (~0.5ms) for basic detection.
  • 64 patterns provide balanced performance (~1ms) and are recommended for most use cases.
  • Higher pattern counts (128 or 256) increase sensitivity but also inference time.
• Hardware Considerations: While it can run on basic CPU setups, it also leverages CUDA-compatible GPUs for accelerated training and can achieve 2-10x performance improvement with GPU acceleration when available. It requires a modern x86_64 processor with AVX2 support and a minimum of 4GB RAM.
• Production Readiness: The core GhostAnomalyDetector is optimized with torch.compile() specifically for production use, leading to its classification as “TACTICAL READY” and “Production Ready”.

The Ghost Anomaly Detector offers three main interface options to ensure flexible and seamless deployment and integration with existing Signal Intelligence workflows:

• Command Line Interface (CLI): This provides a high-performance command-line tool for tactical spectrum analysis. Users can analyze specific spectra, process data from files, perform high-sensitivity analysis with configurable thresholds and pattern counts, and get JSON output for automation. It also includes capabilities for performance benchmarking.
• REST API Server: An enterprise-grade FastAPI server is available for scalable deployment. This interface offers real-time spectrum analysis endpoints, health monitoring, performance metrics, and batch processing for high-throughput scenarios. It also provides OpenAPI documentation with Swagger UI and is ready for authentication and rate limiting.
• Programmatic Integration: This allows for seamless Python API integration with existing systems. Users can initialize the detector within their Python code, prepare spectrum data as tensors, and perform real-time analysis to obtain anomaly scores and threat levels.

The Ghost Anomaly Detector offers several key tactical advantages, primarily focusing on its advanced detection capabilities for sophisticated RF threats in real-time. Here are three significant tactical advantages:

• Stealth Detection: The detector excels at identifying hidden or masked RF emissions, including stealth communications and low-power, hidden transmissions. This capability allows for the perception of signals that conventional methods might miss, providing a crucial edge in monitoring the RF spectrum.
• Spoofing Detection: It can effectively detect artificially generated or modified signals, specifically identifying signal spoofing and replayed signals in real-time. This is critical for preventing deception attempts and ensuring the integrity of RF communications.
• Unknown Modulation Detection: The system flags unrecognized signal patterns and can detect novel or custom communication schemes and unknown modulation types. This adaptive pattern learning, enabled by neural network-based threat recognition, allows the detector to evolve with emerging threats and identify previously unseen signal behaviors.

Beyond these specific detection capabilities, the detector’s ability to perform real-time analysis with sub-millisecond inference times also serves as a critical tactical advantage, enabling immediate response to identified anomalies.

The Ghost Anomaly Detector’s core capabilities enable it to provide cutting-edge real-time RF threat detection by identifying sophisticated anomalies that traditional methods may miss.

Its main core capabilities include:

• Learnable Spectral Patterns: The system generates trainable spectral masks using a neural network. These patterns are adaptive and evolve with emerging threats, providing neural network-based threat recognition. The LearnableSpectralPatternGenerator normalizes patterns for consistent reconstruction and is designed for memory efficiency in parameter storage and retrieval.
• Ghost Imaging Reconstruction: The detector performs differentiable Fourier-based spectrum reconstruction. This is a fundamental process where input spectra go through a pattern generator and detector simulation, leading to Fourier Reconstruction. The GhostImagingRFModule simulates ghost imaging detector responses and supports batch processing for efficiency.
• Anomaly Detection via L2 Reconstruction Error: The system calculates the L2 reconstruction error as its primary anomaly metric. A higher error indicates a potential anomaly, allowing for threat identification. This is part of the GhostAnomalyDetector component which combines pattern generation and reconstruction.
• Real-time Analysis with torch.compile() Optimization: The detector boasts sub-millisecond inference times, specifically validated at 0.8-1.2ms per spectrum, enabling real-time anomaly identification. This is achieved through accelerated inference kernels optimized with torch.compile(), which can provide a 2-10x speedup through just-in-time compilation. The system’s throughput is over 1,000 spectra/second on standard hardware.
• Multi-Interface Support: The detector offers flexible deployment options through a Command Line Interface (CLI), a REST API server, and programmatic integration (Python API). This ensures seamless operation with existing Signal Intelligence workflows. The REST API provides real-time spectrum analysis endpoints, health monitoring, and batch processing, complete with OpenAPI documentation. The CLI supports real-time spectrum analysis, performance benchmarking, and batch processing.
• Multi-Tier Threat Classification: Based on the anomaly score, the detector provides a four-level threat assessment: LOW, MEDIUM, HIGH, and CRITICAL. The sensitivity levels for this classification are configurable through threshold management.
• Noise Robustness: The Ghost Imaging RF Module includes advanced filtering for noise robustness specifically designed for low-SNR (Signal-to-Noise Ratio) environments.
• Memory Optimization: The system is designed with a minimal memory footprint for edge deployment, with a baseline memory usage of less than 100MB and typically less than 500MB under load. Its base memory usage is approximately 50MB plus 1MB per pattern.