arXiv subjects

Martin Jurecek

Publications and source records attributed to Martin Jurecek.

5 recordsLinked to original sources

arXiv2026

Image-Based Techniques and Ensemble Soft Voting for Malware Classification

In this chapter, we investigate image-based malware family classification using an ensemble learning framework and a soft voting strategy. We consider malware binaries that have been converted into images using eight distinct conversion strategies. Three complementary feature extraction tracks are applied to these images: handcrafted descriptors combining Histogram of Oriented Gradients (HOG) and Haralick texture features along with 38 statistical features; dense embeddings obtained from three pretrained neural networks (VGG16, ResNet50, and ViT-B/16), where each pretrained model is used as a frozen feature extractor with its classification head removed; and 512-dimensional embeddings derived from a custom Convolutional Neural Network (CNN) trained directly on the malware images. Each of the three feature extraction techniques is evaluated with machine learning classifiers across all eight image conversion types. The best individual results are 77.8% accuracy for the handcrafted features, 73.8% for the pretrained neural network track, and 74.8% for the custom CNN track. Then we consider various soft voting ensemble strategies, and we find that the best-performing soft voting pool--consisting of fifteen voters selected on a dedicated validation split--achieves 80.2% accuracy across the 17 malware families under consideration, a statistically significant improvement of 2.4 percentage points over the best individual model. A quantitative diversity analysis confirms that the different feature representations are complementary, with the handcrafted descriptors being the strongest contributors.

cs.CR↗

arXiv2026

Neural Fingerprints for Malware Analysis: An Image-Based Metric Learning Approach with Application to Cross-Domain Classification

Identifying the family of a newly observed malware sample is a core task in threat intelligence, yet conventional classifiers must be retrained whenever a new family appears. This chapter develops an image-based metric learning approach that instead learns to extract discriminative neural fingerprints--fixed-length embeddings--from malware-as-image representations, so that family membership can be determined by nearest-neighbor search in the embedding space. The central advantage of this formulation is zero-shot capability: because the learned embedding induces a similarity metric rather than a fixed set of class boundaries, families that were never seen during training can be recognized by comparison against a gallery, with no retraining. We demonstrate this directly by training an encoder on MalNet-Images-Tiny and MalImg combined (453 families, 96,769 images) and evaluate it zero-shot on a held-out 17-family grayscale dataset with no family overlap. Using a lightweight CNN with multi-proxy anchor loss, this model attains 73.1% retrieval@1 and 90.5% open-set AUROC on families the encoder has never seen. We benchmark our embedding approach against two conventional paradigms in a same-domain setting, where all three are competitive at classifying malware into families. We further show that the learned embeddings transfer across datasets. Unlike classifiers, our embedding approach also yields interpretable similarity scores and scales to large galleries via Facebook AI Similarity Search (FAISS). Finally, we provide a comprehensive evaluation of the learned embedding space using retrieval@k, cluster purity, silhouette score, separation ratio, few-shot accuracy, and open-set detection metrics, along with robustness analysis under image perturbations.

cs.CR↗

arXiv2026

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model. Machine learning models for malware detection or classification are particularly susceptible to performance degradation caused by concept drift, as attackers constantly modify existing malware. In this chapter, we analyze two machine learning-based approaches to automated concept drift detection-a novel approach based on One-Class Support Vector Machines (OCSVM) and a previously-studied technique based on Minibatch K-Means (MK-Means). For comparison we also consider Maximum Mean Discrepancy (MMD), a statistical technique for detecting changes in multidimensional data. We conduct an extensive series of experiments comparing the effectiveness of four learning models, namely, Multilayer Perceptron, Random Forest, Support Vector Machines, and eXtreme Gradient Boosting. For each of these models, we consider three distinct scenarios: A static scenario where no model retraining occurs, a periodic scenario where models are constantly retrained irrespective of concept drift, and a drift-aware scenario where models are only retrained when concept drift is detected. Under the drift-aware scenario, we analyze the tradeoff between accuracy and training efficiency using Pareto Front analysis. We find that all three concept drift detection techniques achieve classification accuracy comparable to periodic retraining, while offering substantially greater efficiency in terms of the number of models that must be retrained. In addition, drift-aware retraining based on our OCSVM technique generally outperforms the MK-Means and MMD approaches. Overall, these results provide strong evidence that we can accurately detect concept drift in malware classification models.

cs.LG↗

arXiv2026

CAM-Guided Saliency Cutout and Image-Based Malware Classification

Dropout regularization is commonly used to reduce overfitting by removing parts of a neural network during training. For Convolutional Neural Networks (CNN), cutouts serve a somewhat analogous purpose. Cutouts can be implemented as data augmentation: the original training image is retained, and additional copies are created with regions removed. In this chapter, we test whether cutout placement can be improved by using High-Resolution Class Activation Mapping (HiResCAM). We compare four controlled training conditions: no cutout, standard random cutout, low-saliency cutout, and high-saliency cutout. We experiment using grayscale malware images from the RawMal-TF dataset (17 families with~1,000 samples per family), and for comparison to natural images, we experiment with the well-known CIFAR-100 dataset. All experiments are based on ResNet18 with~100 training epochs. For the cutout experiments, we test cutout areas of~5\%, 10\%, 20\%, and~30\%, and we consider~$M\in\{4,8}$ augmented copies per original training image. The RawMal-TF results are slightly worse for all three cutout cases (random, high and low saliency) as compared to no cutouts. In contrast, our CIFAR-100 experimental results improve slightly under low-saliency cutout. These results suggest that the value of saliency-guided cutout is domain dependent, and that malware images should not be treated as equivalent to natural images.

cs.CV↗

arXiv2023

Social Media Bot Detection using Dropout-GAN

Bot activity on social media platforms is a pervasive problem, undermining the credibility of online discourse and potentially leading to cybercrime. We propose an approach to bot detection using Generative Adversarial Networks (GAN). We discuss how we overcome the issue of mode collapse by utilizing multiple discriminators to train against one generator, while decoupling the discriminator to perform social media bot detection and utilizing the generator for data augmentation. In terms of classification accuracy, our approach outperforms the state-of-the-art techniques in this field. We also show how the generator in the GAN can be used to evade such a classification technique.

cs.LG↗