The paper discusses how any lossless compression algorithm can be transformed into a machine learning method using Normalized Compression Distance or the Minimum Description Length principle, and conversely how any auto‑regressive model can become a lossless compressor via entropy coding. It surveys and formalizes these strategies, introduces a design framework for compression‑based ML, and empirically validates that such methods can match conventional baselines and outperform them on malware detection, achieving accuracy gains up to 0.62 by varying design choices.
arXiv:2610.01949v1 Announce Type: cross Abstract: Ransomware has emerged as a major cybersecurity threat, with incidents increasing in frequency and impact across critical sectors. These attacks are...
The paper presents a lightweight machine‑learning approach for multi‑class malware detection on resource‑constrained devices. Using a LightGBM classifier with SMOTE oversampling, SOM‑US undersampling, and Genetic‑Algorithm feature selection, the authors achieve 89.1 % accuracy on four malware families and 76 % on 16 individual malware types. A second Random‑Forest model further improves family classification to 91.2 % and individual classification to 78.7 %.
HyperZip introduces an efficient data compression framework that uses diffusion-based large language models (dLLMs) with Multi-Token Prediction to speed up compression. It addresses the trade‑off between throughput and compression rate by employing a hypernetwork that generates data‑specific updates from a context representation, allowing the dLLM to adapt to target data without costly fine‑tuning. Experiments show HyperZip outperforms state‑of‑the‑art baselines in both compression rate and speed.
arXiv:2503.11841v2 Announce Type: replace-cross Abstract: Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as truste...
arXiv:2604. 23025v2 Announce Type: replace-cross Abstract: Android malware detectors built with machine learning often suffer from temporal bias: models are trained and evaluated without respecting apps' actual release times, inflating accuracy and weakening real-world robustness.
arXiv:2605. 24903v2 Announce Type: replace-cross Abstract: Machine learning based malware detectors become obsolete over time due to concept drift in benign and malware applications.
arXiv:2607. 11883v1 Announce Type: new Abstract: Compression is fundamental to intelligence.
arXiv:2608. 11249v1 Announce Type: cross Abstract: We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data - including plain text, source code, and structured formats such as XML - and by recent advances in neural language model-based compression.
arXiv:2608. 02671v1 Announce Type: cross Abstract: Malware detection using Hardware Performance Counters (HPC) has emerged as a promising solution to improve the security of computing systems as a complement to antivirus software.
arXiv:2607. 01445v1 Announce Type: cross Abstract: Malware poses a critical and ever-evolving threat, and robust and effective systems for detecting and classifying malware are of essential importance.
Delphi Scanner is a static malware detection system for Windows PE files that balances efficiency and interpretability. It employs a convolutional neural network to model Windows API sequences and a rule‑based interpretation layer to map APIs to high‑level malicious capabilities. Tested on over 190,000 PE files, it achieves 95.35% accuracy with a 1.53 MB model, and demonstrates robustness against out‑of‑distribution samples and adversarial manipulations.
arXiv:2505. 23869v4 Announce Type: replace-cross Abstract: A proposition that connects randomness and compression is put forward via Gibbs entropy over set of measurement vectors associated with a compression process.