Struct MLConfig 

pub struct MLConfig {
    pub anomaly_threshold: f64,
    pub n_trees: usize,
    pub sample_size: usize,
    pub enable_behavioral_analysis: bool,
    pub enable_pattern_recognition: bool,
    pub enable_zero_day_detection: bool,
    pub min_training_samples: usize,
    pub max_features: usize,
    pub random_seed: Option<u64>,
}

Configuration for ML analysis

Fields

anomaly_threshold: f64

Anomaly score threshold (0.0-1.0) Higher = fewer false positives, might miss some anomalies

n_trees: usize

Number of trees for Isolation Forest

sample_size: usize

Sample size for each tree

enable_behavioral_analysis: bool

Enable behavioral baseline learning

enable_pattern_recognition: bool

Enable pattern recognition

enable_zero_day_detection: bool

Enable zero-day detection

min_training_samples: usize

Minimum training samples required

max_features: usize

Maximum features to consider

random_seed: Option<u64>

Random seed for reproducibility

Trait Implementations

impl Clone for MLConfig

fn clone(&self) -> MLConfig

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for MLConfig

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for MLConfig

fn default() -> Self

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for MLConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for MLConfig

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.