mirror of
https://github.com/DaLaw2/NetGuardia.git
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ParrotXray
55edf01113
* wip: use tract-onnx * feat: Implement ML models loading * wip: adjust code * add: Add NetGuardia-FrontEnd as submodule * wip: make ml inference * wip: make ml inference * feat: Implement ml inference * feat: improvement ml inference * feat: Complete ml inference * refactor: Change the log! and usize method
78 lines
3.5 KiB
Rust
78 lines
3.5 KiB
Rust
use macros::loggable;
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use tracing;
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loggable! {
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MLLog {
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#[error("Initializing Machine Learning with inference URL: {url}")]
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Initializing { url: String } => tracing::Level::INFO,
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#[error("Continuing without Machine Learning detection")]
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Skiped => tracing::Level::WARN,
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#[error("Machine Learning detection is disabled (no ml_inference_url configured)")]
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Disabled => tracing::Level::INFO,
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#[error("Machine Learning detection starting")]
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Starting => tracing::Level::INFO,
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#[error("Machine Learning detection ready")]
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Ready => tracing::Level::INFO,
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#[error("Machine Learning detection shutdown")]
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Shutdown => tracing::Level::INFO,
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#[error("Machine Learning channel disconnected")]
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ChannelDisconnected => tracing::Level::WARN,
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#[error("Failed to forward packet: {error}")]
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ForwardPacketFailed { error: String } => tracing::Level::WARN,
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#[error("Attach XDP program success")]
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AttachProgramSuccess => tracing::Level::INFO,
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#[error("Queue initialization incomplete")]
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QueueInitIncomplete => tracing::Level::WARN,
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#[error("Queue refill incomplete")]
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QueueRefillIncomplete => tracing::Level::WARN,
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#[error("No frames submit to queue")]
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NoFrameSubmit => tracing::Level::WARN,
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#[error("Queue pair {queue_id} started successfully")]
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QueuePairStarted { queue_id: u32 } => tracing::Level::INFO,
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#[error("ML models loaded - {info}")]
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ModelsLoaded { info: String } => tracing::Level::INFO,
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#[error("Inference configuration loaded: {features} features, {attacks} attack types")]
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ConfigLoaded { features: usize, attacks: usize } => tracing::Level::INFO,
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#[error("ML Engine started: max_flows={max_flows}, min_packets={min_packets}, interval={interval_secs}s")]
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EngineStarted { max_flows: usize, min_packets: usize, interval_secs: u64 } => tracing::Level::INFO,
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#[error("Inference completed: {total_flows} flows ({anomaly} anomaly, {benign} benign) in {duration_ms}ms ({throughput:.1} flows/s)")]
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InferenceCompleted { total_flows: usize, anomaly: usize, benign: usize, duration_ms: u32, throughput: f32 } => tracing::Level::INFO,
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#[error("Inference skipped: {reason}")]
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InferenceSkipped { reason: String } => tracing::Level::INFO,
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#[error("Threat detected: {flow} -> {attack_type} (confidence: {confidence:.2}, ae_score: {ae_score:.4}, rf_score: {rf_score:.4}, ensemble: {ensemble_score:.4})")]
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ThreatDetected { flow: String, attack_type: String, confidence: f32, ae_score: f32, rf_score: f32, ensemble_score: f32 } => tracing::Level::WARN,
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#[error("Flow stats: total={total_flows}, qualified={flows_len}, min_packets={min_packets}, packet_counts: {counts}")]
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FlowStats { total_flows: usize, flows_len: usize, min_packets: usize, counts: String } => tracing::Level::INFO,
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#[error("Running inference on {size} flows")]
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RunningInference { size: usize } => tracing::Level::INFO,
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#[error("Inference returned fewer results: expected {size}, got {len}")]
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InferenceResults { size: usize, len: usize } => tracing::Level::INFO,
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#[error("{model} inference failed: {error}")]
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InferenceFailed { model: String, error: String } => tracing::Level::INFO,
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#[error("Failed to parse packet (length: {len})")]
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ParsePacketFailed { len: usize } => tracing::Level::INFO,
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}
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} |