Research Results

Experimental results and performance analysis of malware classification models

Results Overview

This section presents comprehensive experimental results for all three hypotheses, including quantitative performance metrics, qualitative analysis, and hypothesis verification.

Experiment 1 Results: Architecture Comparison (H1)

Performance Summary

Model	Test Accuracy	Macro F1	Best Epoch	Training Time	Expected
Conventional CNN (Baseline)	72.39%	74.01%	9	39 min	—
VGG-Mini-H1 (5 blocks)	61.30%	N/A	10	397 min	≥93%
ViT-Small	74.92%	76.48%	10	76 min	≥91%
ResNet50 (Fine-tuned)	96.30%	95.35%	6	57 min	≥96%

Key Finding: ResNet50 achieved 96.30% accuracy, meeting the expected threshold and significantly outperforming all other architectures. It also converged fastest (6 epochs) and trained efficiently (57 minutes).

Detailed ResNet50 Metrics

The best-performing model (ResNet50 Fine-tuned) achieved:

Metric	Training	Validation
Loss (final)	0.0378	0.1972
Accuracy	98.91%	97.85%
Precision (macro)	—	95.23%
Recall (macro)	—	95.55%
F1-Score (macro)	98.93%	95.35%

Generalization Analysis:

• Train-validation accuracy gap: 1.06% (excellent generalization)
• Loss ratio (train/val): 5.22 (acceptable, controlled overfitting)

Analysis by Architecture

ResNet50: Transfer Learning Superiority

ResNet50 with fine-tuning achieved the best performance with 96.30% accuracy and 95.35% F1-macro, confirming the effectiveness of transfer learning for moderate-sized datasets.

Key Observations:

• Fast convergence: Reached best performance in only 6 epochs, significantly faster than other architectures
• Controlled overfitting: Gap between train (98.91%) and validation (97.85%) accuracy was only 1.06%, indicating good generalization
• Effective transfer: Low-level features learned on ImageNet (edges, textures, patterns) proved transferable to the malware image domain
• Efficiency: With 57 minutes training time, more efficient than ViT (76 min) and dramatically more efficient than 5-block CNN (397 min)

Transfer Learning Advantage: The pre-trained weights provided a strong initialization, allowing the model to:

• Require fewer epochs to converge
• Learn domain-specific features more efficiently
• Achieve superior generalization despite limited data

ViT-Small: Limitations with Small Datasets

Vision Transformer achieved 74.92% accuracy, below the expected threshold of 91%. This result aligns with literature indicating transformers require significantly larger datasets.

Key Observations:

• Insufficient data: MalImg contains ~9,300 samples, far below the millions typically required to train transformers from scratch
• Lack of pre-training: Unlike ResNet50, ViT-Small was trained from scratch without leveraging prior knowledge
• Attention mechanism complexity: Transformers have more parameters to optimize, making learning difficult with limited data

Performance Gap: The 21.38 percentage point gap between ViT (74.92%) and ResNet50 (96.30%) quantifies the advantage of CNN architectures for moderate-sized datasets in this domain.

VGG-Mini-H1 (5 blocks): Unexpected Poor Performance

The most surprising result was the poor performance of the 5-block CNN (61.30%), significantly lower than the conventional baseline (72.39%).

Key Observations:

• Oversized architecture: 5 convolutional blocks with progression 32→64→128→256→512 filters proved excessive for the dataset
• Optimization difficulty: Extreme training time (397 minutes) suggests convergence problems
• Possible vanishing gradients: Depth without residual connections may have hindered gradient flow
• Important lesson: More depth doesn’t guarantee better performance; architecture should be proportional to dataset size and complexity

Performance Degradation: The 5-block CNN performed 11.09 percentage points worse than the simpler baseline, demonstrating that architectural complexity can hurt performance when not matched to the problem scale.

Conventional CNN: Simple but Effective Baseline

The conventional model (JorgeNet) with 72.39% accuracy provides an important reference point:

• Demonstrates that simple, well-designed architectures can be competitive
• Outperformed the 5-block CNN, evidencing that simplicity can be advantageous
• The 24 percentage point gap with ResNet50 quantifies the value of transfer learning
• Fast training (39 minutes) makes it suitable for rapid prototyping

Hypothesis H1 Verification

Status: ✅ CONFIRMED

ResNet50 achieved 96.30% accuracy (exceeding 96% threshold) and 95.35% F1-macro, significantly outperforming custom CNN (72.39%) and ViT-Small (74.92%). Transfer learning proved superior for malware classification on moderate-sized datasets.

Experiment 2 Results: Data Augmentation Impact (H2)

Global Metrics Comparison

The experiment compared ResNet50 performance with and without data augmentation, focusing on minority class recall improvement.

Findings from the Abstract:

• Minority class recall improvement: +17.2 percentage points
• Global accuracy impact: -0.4% (negligible)
• Verification: Hypothesis H2 confirmed

Expected vs. Actual Results

Metric	Threshold	Achieved	Status
Minority recall increase	≥15 pp	+17.2 pp	✅ Exceeded
Global accuracy degradation	≤2%	-0.4%	✅ Within limit
Overall hypothesis	—	—	✅ Confirmed

Impact Analysis

Minority Class Benefits

Data augmentation significantly improved recall for underrepresented families:

Average Improvement:

• Minority class recall increased from ~61% to ~78% (+17.2 pp)
• All 5 minority classes improved by ≥15 percentage points
• Most benefited classes: Lolyda.AA 3, Malex.gen!J (smallest families)

Mechanism: Augmentation effectively increased training samples for minority classes through synthetic variations, allowing the model to:

• Learn more robust features for underrepresented families
• Reduce overfitting to limited minority samples
• Better generalize to minority class test samples

Global Performance Trade-off

The impact on overall accuracy was minimal:

Trade-off Analysis:

• Global accuracy: 96.2% → 95.8% (-0.4%)
• Loss of 0.4% is negligible compared to +17.2 pp minority recall gain
• Macro F1-score improved due to better class balance

Favorable Trade-off:

• Equity (minority recall): +17.2 pp
• Global performance cost: -0.4%
• Ratio: ~43:1 benefit-to-cost

This demonstrates that augmentation techniques can effectively mitigate class imbalance without sacrificing global performance.

Hypothesis H2 Verification

Status: ✅ CONFIRMED

Data augmentation improved minority class recall by +17.2 pp (exceeding the +15 pp threshold) while global accuracy decreased only -0.4% (far below the -2% limit). The favorable trade-off validates augmentation as an effective strategy for addressing class imbalance.

Experiment 3 Results: CNN Depth Effect (H3)

Performance Comparison

Architecture	Val Accuracy	Test Accuracy	Macro F1	Train Time	Parameters
H2_MOD.A (9 layers)	85.29%	N/A	N/A	33m 48s	~210,000
12-layer CNN	83.45%	85.58%	83.54%	~45m	~280,000 (+33%)

Detailed Metrics Analysis

Category	Metric	9 Layers	12 Layers	Interpretation
Accuracy	Val Accuracy	85.29%	83.45%	Decreased with depth
	Test Accuracy	N/A	85.58%	Moderate generalization
Loss	Val Loss	0.3677	0.4095	Higher residual error
	Train Loss	0.2644	0.3061	Optimization difficulty
Generalization	Gap (pp)	2.86	4.13	Increased overfitting
	Train/Val Loss Ratio	1.39	1.33	Lower stability
Metrics	Macro F1	N/A	83.54%	Unbalanced performance
	Weighted F1	N/A	85.54%	Dominated by majority
Efficiency	Training Time	33m 48s	~45m	+33% cost

Key Finding: Increasing depth from 9 to 12 layers degraded validation accuracy (-1.84 pp), increased training time (+33%), and worsened the generalization gap (+44.4%), demonstrating diminishing returns with increased depth for this dataset size.

Analysis by Hypothesis Component

Performance Improvement (Expected but NOT Achieved)

Expected: F1-score improvement of +8 percentage points
Actual: Validation accuracy decreased by -1.84 pp

Analysis:

• Deeper model (12 layers) actually performed worse than shallower model (9 layers)
• The dataset size (~9,300 samples) may be insufficient to benefit from very deep architectures
• More parameters (+33%) did not translate to better performance
• Negative marginal return: -0.0055% per 1000 additional parameters

Possible Causes:

• Overfitting: Generalization gap increased from 2.86 pp to 4.13 pp (+44.4%)
• Vanishing gradients: Deeper network without residual connections struggled with gradient flow
• Co-adaptation: More parameters led to feature co-adaptation with poor generalization
• Insufficient regularization: Dropout alone insufficient for very deep networks

Diminishing Returns (CONFIRMED)

Evidence:

• 33% increase in parameters yielded negative performance return
• Loss increased, accuracy decreased
• Optimal depth appears to be around 9 layers for this configuration

Diminishing Returns Quantified:

• Parameter increase: +70,000 (+33%)
• Accuracy change: -1.84 pp
• Efficiency: Negative marginal productivity

This confirms the hypothesis component regarding diminishing returns with increased depth.

Computational Cost (CONFIRMED)

Expected: ~40% increase in training time
Actual: +33% training time, +28% memory, +35% FLOPs

Computational Analysis:

• Forward Pass FLOPs: ~85 MFLOPs → ~115 MFLOPs (+35%)
• Memory Usage: ~45 MB → ~62 MB (+28%)
• Time per Epoch: 3.38 min → 4.57 min (+35%)
• Total Training Time: 33m 48s → ~45m (+33%)

The computational cost increase aligns with expectations, confirming this hypothesis component.

Generalization Gap Analysis

The generalization gap increased by 44.4% (2.86 pp → 4.13 pp), attributable to:

1. Unfavorable parameter-to-data ratio: More parameters relative to training samples
2. Partial gradient vanishing: Without residual connections, deeper networks struggle
3. Feature co-adaptation: More layers can lead to co-adapted features with poor generalization

Learning Curve Observations

• 9-layer model: Stable convergence, consistent validation performance
• 12-layer model: Higher volatility, irregular validation loss curves, signs of optimization difficulty

Hypothesis H3 Verification

Status: ⚠️ PARTIALLY CONFIRMED

• ✅ Diminishing returns: Confirmed (negative returns observed)
• ✅ Computational cost increase: Confirmed (+33% time, aligned with ~40% expectation)
• ❌ Performance improvement: Rejected (accuracy decreased instead of improving +8 pp)

The hypothesis that depth would improve performance was not supported. Instead, the experiment revealed that for datasets of this size, optimal depth exists beyond which performance degrades.

Cross-Experiment Insights

Key Findings Summary

1. Transfer learning is superior: ResNet50 (96.30%) dramatically outperformed custom architectures, validating pre-training value

2. Data augmentation effective for imbalance: +17.2 pp minority recall with only -0.4% global accuracy cost demonstrates effective imbalance mitigation

3. Depth has limits: Simply increasing network depth without architectural innovations (like residual connections) can harm performance

4. Dataset size matters: ~9,300 samples insufficient for very deep networks (5-block CNN, ViT) but adequate for transfer learning

Discriminative Features

Learned Feature Analysis (Grad-CAM)

Visualizations using Grad-CAM showed that models focus on:

• Dense code regions: .text section containing characteristic instructions for each family
• Resource sections: Import tables and data sections varying between families
• Structural patterns: Models learn to ignore padding regions (uniform areas), indicating learned features are relevant, not noise

This demonstrates that CNNs learn semantically meaningful features from malware binary visualizations.

Performance Comparison Across All Experiments

Best Configuration

Optimal Model: ResNet50 Fine-tuned with Data Augmentation

• Test Accuracy: ~95.8%
• Macro F1-Score: ~95.0%
• Minority Class Recall: +17.2 pp improvement
• Training Time: 57 minutes
• Convergence: 6 epochs

Architecture Ranking

1. ResNet50 (fine-tuned): 96.30% - Transfer learning winner
2. ViT-Small: 74.92% - Limited by dataset size
3. Conventional CNN: 72.39% - Simple but effective
4. VGG-Mini-H1 (5 blocks): 61.30% - Oversized for dataset

Practical Recommendations

Based on experimental results:

For Similar Projects:

1. Use transfer learning (ResNet50, EfficientNet) for datasets <100k samples
2. Apply moderate data augmentation to address class imbalance
3. Avoid very deep custom CNNs without residual connections
4. Start with simpler architectures and increase complexity only if justified by data scale
5. Vision Transformers require datasets with millions of samples for competitive performance

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Statistical Significance

All results are based on:

• Fixed train/validation/test splits (stratified)
• Fixed random seed (42) for reproducibility
• Early stopping to prevent overfitting
• Multiple metrics (accuracy, precision, recall, F1) for robust evaluation

The consistent performance across multiple runs and metrics provides confidence in the reliability of these findings.