malware_modeler/

model.rs

// SPDX-License-Identifier: Apache-2.0

use crate::ftype::FileType;
use crate::{dataset::Dataset, Bytes};

use std::cmp::Ordering;
use std::collections::HashMap;
use std::path::Path;

use anyhow::{anyhow, ensure, Result};
use rand::Rng;
use rayon::prelude::*;
use serde::{Deserialize, Serialize};

// Adapted from https://github.com/ibnz36/lrclassifier/blob/06d136445028653af5d2061e002111ef11b14277/src/lib.rs
// Accessed 01 November 2025

/// The sigmoid function.
#[inline]
#[must_use]
fn sigmoid(x: f32) -> f32 {
    1.0 / (1.0 + (-x).exp())
}

/// Machine learning model using logistic regression
#[derive(Debug, Clone, Deserialize, Serialize, PartialEq)]
pub struct LogisticRegression {
    /// Learning rate
    pub learning_rate: f32,

    /// Bias term
    pub bias: f32,

    /// Model's weights
    pub weights: Vec<f32>,

    /// L1 LASSO regularization
    pub l1: f32,

    /// L2 Ridge regularization
    pub l2: f32,

    /// N-grams used to train the model
    #[serde(
        serialize_with = "crate::serde::serialize_hex_map",
        deserialize_with = "crate::serde::deserialize_hex_map"
    )]
    pub features: HashMap<Bytes, usize>,

    /// N-gram size
    n: usize,

    /// If the model has been trained
    pub trained: bool,

    /// Amount of n-grams originally used
    pub original_ngrams: u32,

    /// The type of file this model is trained on
    pub file_type: FileType,
}

impl LogisticRegression {
    /// New logistic regression model with random initial weights from given parameters.
    #[must_use]
    #[allow(clippy::cast_possible_truncation)]
    pub fn new(input_size: usize, learning_rate: f32, l1: f32, l2: f32) -> Self {
        let mut rng = rand::rng();

        Self {
            learning_rate,
            weights: (0..input_size)
                .map(|_| rng.random_range(-1.0..1.0))
                .collect(),
            l1,
            l2,
            n: 0,
            features: HashMap::new(),
            trained: false,
            bias: rng.random(),
            original_ngrams: input_size as u32,
            file_type: FileType::NotSet,
        }
    }

    /// Returns a trained logistic regression model from given parameters and dataset. Returns the model
    /// and the error value from the last epoch.
    ///
    /// # Panics
    ///
    /// This code won't panic, but [`LogisticRegression::train`] would panic if the data sizes were
    /// different, but that can't happen in this case since the same object is used in both places.
    #[must_use]
    pub fn new_from_dataset_and_train(
        dataset: &mut Dataset,
        epochs: u32,
        learning_rate: f32,
        l1: f32,
        l2: f32,
    ) -> (Self, f32) {
        let mut model = Self::new(dataset.data.len(), learning_rate, l1, l2);
        model.n = dataset.features[0].len();
        model.features = dataset
            .features
            .iter()
            .map(|f| (f.clone(), 0))
            .collect::<HashMap<_, _>>();
        let result = model.train(epochs, dataset).unwrap();
        model.file_type = dataset.ftype;
        (model, result)
    }

    /// Predicts the output for a given input vector.
    /// This will fail if the input vector isn't the same length as the weights vector.
    #[inline]
    #[must_use]
    pub fn predict(&self, input: &[f32]) -> f32 {
        let linear_model = input
            .iter()
            .zip(&self.weights)
            .map(|(x, w)| x * w)
            .sum::<f32>()
            + self.bias;
        sigmoid(linear_model)
    }

    /// Trains the classifier once with the given inputs and outputs.
    ///
    /// # Errors
    ///
    /// Returns an error if the data isn't the correct size or if labels are missing.
    #[allow(clippy::cast_precision_loss)]
    pub fn train(&mut self, epochs: u32, dataset: &mut Dataset) -> Result<f32, &'static str> {
        if dataset.labels.is_empty() {
            return Err("Dataset must have labels");
        }

        if !dataset.validate() {
            return Err("Dataset didn't pass validity check!");
        }

        if dataset.data[0].len() != self.weights.len() {
            return Err("Dataset feature length must equal the number of model weights");
        }

        let mut loss = 0.0;
        #[allow(unused)]
        for epoch in 0..epochs {
            loss = 0.0;
            dataset.shuffle();
            for (input, output) in dataset.data.iter().zip(&dataset.labels) {
                let prediction = self.predict(input);
                let error = prediction - output;
                let p = prediction.clamp(1e-8, 1.0 - 1e-8);
                loss += -output * p.ln() - (1.0 - output) * (1.0 - p).ln();

                self.weights
                    .par_iter_mut()
                    .enumerate()
                    .for_each(|(i, weight)| {
                        let l1r = self.l1 * weight.signum();
                        let l2r = self.l2 * *weight;
                        *weight -= self.learning_rate * (error * input[i] + l1r + l2r);
                    });
                self.bias -= self.learning_rate * error;
            }
            loss /= self.weights.len() as f32;

            #[cfg(debug_assertions)]
            println!("Epoch: {epoch}, Log loss: {loss}");

            if loss < 1e-6 {
                break;
            }
        }

        self.trained = true;
        self.file_type = dataset.ftype;
        self.n = dataset.features[0].len();
        Ok(loss)
    }

    /// Evaluate a dataset
    ///
    /// # Errors
    ///
    /// If the dataset doesn't match the model weight length or doesn't have labels
    pub fn evaluate_dataset<'a>(&self, dataset: &'a Dataset) -> Result<ConfusionMatrix<'a>> {
        ensure!(!dataset.is_empty(), "Dataset is empty");
        ensure!(!dataset.labels.is_empty(), "Dataset labels is empty");
        ensure!(
            dataset.data[0].len() == self.weights.len(),
            "Dataset length must equal the number of model weights"
        );
        ensure!(
            self.file_type == dataset.ftype,
            "Dataset file type must match model file type"
        );

        let mut tp_ = 0;
        let mut fp_ = 0;
        let mut tn_ = 0;
        let mut fn_ = 0;
        let mut predictions = Vec::with_capacity(dataset.labels.len());

        for index in 0..dataset.len() {
            let prediction = self.predict(&dataset.data[index]);
            if prediction >= 0.5 && dataset.labels[index] >= 0.9 {
                tp_ += 1;
            } else if prediction >= 0.5 && dataset.labels[index] < 0.5 {
                fp_ += 1;
            } else if prediction < 0.5 && dataset.labels[index] < 0.5 {
                tn_ += 1;
            } else {
                fn_ += 1;
            }
            predictions.push(prediction);
        }

        Ok(ConfusionMatrix {
            true_p: tp_,
            true_n: tn_,
            false_p: fp_,
            false_n: fn_,
            dataset,
            predictions,
        })
    }

    /// Evaluate a file
    ///
    /// # Errors
    ///
    /// Errors will result if the model doesn't have features or if the sample file can't be read
    #[allow(clippy::cast_sign_loss, clippy::cast_possible_truncation)]
    pub fn evaluate_file<P: AsRef<Path>>(&self, path: P) -> Result<(&'static str, f32, u32)> {
        ensure!(
            !self.features.is_empty(),
            "Features are required for file evaluation"
        );

        ensure!(
            self.file_type.matches_path(&path)?,
            "File type doesn't match model type"
        );

        let vector = crate::dataset::featurize_file(path, self.n, &self.features)?;
        let result = self.predict(&vector);
        let features = vector.iter().map(|v| *v as u32).sum();
        if result > 0.5 {
            Ok(("Malicious", result, features))
        } else {
            Ok(("Benign", result, features))
        }
    }

    /// Remove small weights (from regularization), returns the number of weights removed
    ///
    /// # Errors
    ///
    /// There's an error if the model is not trained
    #[allow(clippy::cast_precision_loss)]
    pub fn reduce(&mut self, dataset: &Dataset) -> Result<usize> {
        const THRESHOLD: f32 = 0.01;

        ensure!(
            self.trained,
            "Model must be trained before reducing weights"
        );
        ensure!(
            self.file_type == dataset.ftype,
            "Dataset file type must match model file type"
        );

        let mut average_features = Vec::with_capacity(self.weights.len());
        for (index, model_weight) in self.weights.iter().enumerate() {
            let col_iter = dataset
                .column_iter(index)
                .ok_or_else(|| anyhow!("Column index out of bounds"))?;
            average_features.push((
                index,
                (model_weight * col_iter.sum::<f32>() / dataset.len() as f32).abs(),
            ));
        }

        average_features.sort_by(|(_, a), (_, b)| {
            if a > b {
                Ordering::Greater
            } else if a < b {
                Ordering::Less
            } else {
                Ordering::Equal
            }
        });

        let mut removed = vec![];
        let mut running_sum = 0.0;
        for (index, weight) in average_features {
            running_sum += weight;
            if running_sum < THRESHOLD {
                removed.push(index);
            }
        }

        removed.sort_unstable();
        removed.reverse();
        let removed_len = removed.len();
        for to_remove in &removed {
            self.weights.remove(*to_remove);
        }

        if !self.features.is_empty() {
            let mut removed_features = Vec::with_capacity(removed_len);
            for index in &removed {
                for (feat, feat_index) in &self.features {
                    if index == feat_index {
                        removed_features.push(feat.clone());
                    }
                }
            }

            for removed_feature in removed_features {
                self.features.remove(&removed_feature);
            }
        }

        Ok(removed_len)
    }

    /// Set the features by adding to the struct
    ///
    /// # Errors
    ///
    /// Returns an error if the number of weights and features doesn't match
    pub fn set_features(&mut self, features: Vec<Bytes>) -> Result<()> {
        ensure!(
            features.len() == self.weights.len(),
            "Provided features length {} does not equal the number of model features length {}",
            features.len(),
            self.weights.len()
        );
        self.features = features
            .into_iter()
            .enumerate()
            .map(|(f, i)| (i, f))
            .collect::<HashMap<_, _>>();

        Ok(())
    }

    /// Convenience function to get the features from the dataset then reduce based on the dataset.
    ///
    /// # Errors
    ///
    /// An error occurs if the conditions below aren't met:
    /// * Ensures the number of model weights matches the dataset features
    /// * Ensures the file types match
    pub fn set_features_and_reduce(&mut self, dataset: &Dataset) -> Result<usize> {
        ensure!(
            self.file_type == dataset.ftype,
            "Dataset file type must match model file type"
        );
        ensure!(
            dataset.data[0].len() == self.weights.len(),
            "Dataset length must equal the number of model weights"
        );

        self.features = dataset
            .features
            .iter()
            .enumerate()
            .map(|(f, i)| (i.clone(), f))
            .collect::<HashMap<_, _>>();

        self.reduce(dataset)
    }

    /// Set the features by creating a new struct which has the features
    ///
    /// # Errors
    ///
    /// Returns an error if the number of weights and features doesn't match
    pub fn with_features(self, features: Vec<Bytes>) -> Result<Self> {
        ensure!(
            features.len() == self.weights.len(),
            "Provided features length {} does not equal the number of model features length {}",
            features.len(),
            self.weights.len()
        );

        Ok(Self {
            learning_rate: self.learning_rate,
            bias: self.bias,
            weights: self.weights,
            l1: self.l1,
            l2: self.l2,
            trained: self.trained,
            original_ngrams: self.original_ngrams,
            file_type: self.file_type,
            n: self.n,
            features: features
                .into_iter()
                .enumerate()
                .map(|(f, i)| (i, f))
                .collect::<HashMap<_, _>>(),
        })
    }
}

/// Confusion Matrix
#[derive(Debug, Clone, PartialEq)]
pub struct ConfusionMatrix<'a> {
    /// True positives
    pub true_p: u32,

    /// True negatives
    pub true_n: u32,

    /// False positives
    pub false_p: u32,

    /// False negatives
    pub false_n: u32,

    /// Original dataset reference
    dataset: &'a Dataset,

    /// Model's outputs
    predictions: Vec<f32>,
}

impl ConfusionMatrix<'_> {
    /// Accuracy as correct vs total
    #[inline]
    #[must_use]
    #[allow(clippy::cast_precision_loss)]
    pub fn accuracy(&self) -> f32 {
        (self.true_p + self.true_n) as f32 / self.total() as f32
    }

    /// Precision
    #[must_use]
    #[allow(clippy::cast_precision_loss)]
    pub fn precision(&self) -> f32 {
        self.true_p as f32 / (self.true_p + self.false_p) as f32
    }

    /// Recall
    #[must_use]
    #[allow(clippy::cast_precision_loss)]
    pub fn recall(&self) -> f32 {
        self.true_p as f32 / (self.true_p + self.false_n) as f32
    }

    /// F1 score
    #[must_use]
    #[allow(clippy::cast_precision_loss)]
    pub fn f1(&self) -> f32 {
        2.0 * (self.precision() * self.recall()) / (self.precision() + self.recall())
    }

    /// Total items evaluated
    #[inline]
    #[must_use]
    pub fn total(&self) -> u32 {
        self.true_p + self.true_n + self.false_p + self.false_n
    }

    /// Calculate the area under the curve (AUC)
    #[must_use]
    #[allow(clippy::float_cmp)]
    pub fn auc(&self) -> f32 {
        // Adapted from the AUC implementation by Maciej Kula <maciej.kula@gmail.com>
        // https://github.com/maciejkula/rustlearn/blob/2ac052559b04860c62d7ba34c563b57a02912e4d/src/metrics/ranking.rs
        // The last commit was June 2020 so used here directly instead of importing the crate.
        // This code was also Apache 2.0 licensed.

        // vector of pairs (score, label) - the order is switched with respect to function arguments
        let (mut true_positive_count, mut false_positive_count) = {
            let mut pairs: Vec<_> = self
                .predictions
                .iter()
                .copied()
                .zip(self.dataset.labels.iter().copied())
                .collect();

            // Sort by scores in descending order
            pairs.sort_by(|a, b| b.0.partial_cmp(&a.0).unwrap_or(Ordering::Equal));

            let mut score_prev = f32::NAN;
            // tp .. true positives, fp .. false positives
            let (mut tp, mut fp) = (0.0f32, 0.0f32);
            let (mut tps, mut fps) = (vec![], vec![]);
            for (score, label) in pairs {
                // `tp` and `fp` from the previous iteration are pushed onto the ROC curve only if
                // the `score` changed. This avoids errors due to arbitrary classification of points with
                // identical scores
                if score != score_prev {
                    tps.push(tp);
                    fps.push(fp);
                    score_prev = score;
                }
                tp += label;
                fp += 1.0 - label;
            }
            // Push the final point corresponding to the (1,1) ROC coordinates
            tps.push(tp);
            fps.push(fp);
            (tps, fps)
        };

        let true_positives = true_positive_count[true_positive_count.len() - 1];
        let false_positives = false_positive_count[false_positive_count.len() - 1];

        for (tp, fp) in true_positive_count
            .iter_mut()
            .zip(false_positive_count.iter_mut())
        {
            *tp /= true_positives;
            *fp /= false_positives;
        }

        let mut prev_x = false_positive_count[0];
        let mut prev_y = true_positive_count[0];
        let mut integral = 0.0;

        for (&x, &y) in false_positive_count
            .iter()
            .skip(1)
            .zip(true_positive_count.iter().skip(1))
        {
            integral += (x - prev_x) * (prev_y + y) / 2.0;

            prev_x = x;
            prev_y = y;
        }

        integral
    }
}

impl std::fmt::Display for ConfusionMatrix<'_> {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        const WIDTH: usize = 10;

        writeln!(f, "Result \\ Actual  |    Malicious   |  Benign")?;
        writeln!(
            f,
            "   Malicious     |     {:<WIDTH$} |    {:<WIDTH$}",
            self.true_p, self.false_p
        )?;
        writeln!(
            f,
            "   Benign        |     {:<WIDTH$} |    {:<WIDTH$}",
            self.false_n, self.true_n
        )
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::dataset::Dataset;

    #[test]
    fn xor() {
        let mut dataset = Dataset::from_csv_string(include_str!("../testdata/xor.csv"), 6).unwrap();
        let mut lr = LogisticRegression::new(6, 0.2, 0.0, 0.0);
        lr.train(100, &mut dataset).unwrap();

        let mut correct = 0u16;
        let mut incorrect = 0u16;

        for index in 0..dataset.data.len() {
            println!(
                "Predicted: {}, Expected: {}",
                lr.predict(&dataset.data[index]),
                dataset.labels[index]
            );
            if (lr.predict(&dataset.data[index]) >= 0.5 && dataset.labels[index] >= 0.99)
                || (lr.predict(&dataset.data[index]) < 0.5 && dataset.labels[index] < 0.1)
            {
                correct += 1;
            } else {
                incorrect += 1;
            }
        }

        println!("Correct: {correct}, Incorrect: {incorrect}");
        assert!(correct > incorrect);

        let result = lr.evaluate_dataset(&dataset).unwrap();
        println!("{result}");
        println!("Accuracy: {:.2}", result.accuracy());
        println!("Precision: {:.2}", result.precision());
        println!("Recall: {:.2}", result.recall());
        println!("F1: {:.2}", result.f1());
        println!("Auc: {:.2}", result.auc());
    }

    #[test]
    fn reduction() {
        const BOGUS_LEN: usize = 6;

        let mut dataset =
            Dataset::from_csv_string(include_str!("../testdata/bogus.csv"), BOGUS_LEN).unwrap();
        let mut lr = LogisticRegression::new(BOGUS_LEN, 0.2, 0.1, 0.1);
        lr.set_features(dataset.features.clone()).unwrap();
        lr.train(20, &mut dataset).unwrap();
        let cm = lr.evaluate_dataset(&dataset).unwrap();
        println!("{cm}");
        println!("Weights before reduction: {:?}", lr.weights);
        println!("Features before reduction: {:?}", lr.features);
        lr.reduce(&dataset).expect("Failed to reduce weights");
        println!("Weights after reduction: {:?}", lr.weights);
        println!("Features after reduction: {:?}", lr.features);
        println!("Weights from {BOGUS_LEN} to {}", lr.weights.len());
        assert!(
            lr.weights.len() < BOGUS_LEN,
            "** If this assertion fails, re-run the test once or twice. **"
        );
    }

    #[test]
    fn auc() {
        let y_true = vec![1.0, 1.0, 0.0, 0.0];
        let y_hat = vec![0.5, 0.2, 0.3, -1.0];

        let dataset = Dataset {
            data: vec![],
            labels: y_true,
            features: vec![],
            ftype: FileType::DOCFILE, // Doesn't matter for this test
        };

        let confusion_matrix = ConfusionMatrix {
            true_p: 0,
            true_n: 0,
            false_p: 0,
            false_n: 0,
            dataset: &dataset,
            predictions: y_hat,
        };

        let auc = confusion_matrix.auc();
        println!("Auc: {auc:.2}, expected 0.75");
        assert!((0.73..0.78).contains(&auc));
    }
}