Class: AiRootShield::AiBehavioralAnalyzer

Inherits:
Object
  • Object
show all
Defined in:
lib/ai_root_shield/ai_behavioral_analyzer.rb

Overview

AI-powered behavioral analysis using ONNX models

Constant Summary collapse

DEFAULT_MODEL_PATH = 
File.join(__dir__, "..", "..", "models", "behavioral_model.onnx")
FEATURE_INDICES =

Feature indices for the ML model

{
  file_access_entropy: 0,
  sensor_consistency_score: 1,
  hardware_fingerprint_score: 2,
  process_behavior_score: 3,
  network_pattern_score: 4,
  timing_analysis_score: 5,
  system_call_entropy: 6,
  memory_access_pattern: 7
}.freeze

Instance Method Summary collapse

Constructor Details

#initialize(model_path: nil) ⇒ AiBehavioralAnalyzer

Returns a new instance of AiBehavioralAnalyzer.



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# File 'lib/ai_root_shield/ai_behavioral_analyzer.rb', line 23

def initialize(model_path: nil)
  @model_path = model_path || DEFAULT_MODEL_PATH
  @model = nil
  @confidence_threshold = 0.7
  load_model if File.exist?(@model_path)
end

Instance Method Details

#analyze(device_data) ⇒ Hash

Perform AI behavioral analysis on device data

Parameters:

  • device_data (Hash) —

    Parsed device data

Returns:

  • (Hash) —

    Analysis result with AI confidence and behavioral factors



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# File 'lib/ai_root_shield/ai_behavioral_analyzer.rb', line 33

def analyze(device_data)
  return fallback_analysis(device_data) unless @model

  features = extract_behavioral_features(device_data)
  prediction = run_inference(features)
  
  {
    ai_confidence: prediction[:confidence],
    behavioral_risk_score: prediction[:risk_score],
    behavioral_factors: prediction[:factors],
    anomaly_indicators: detect_anomalies(device_data, features),
    ml_emulator_score: calculate_ml_emulator_score(features)
  }
end