Tootfinder

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Bishwajit Prasad Gond, Rajneekant, Pushkar Kishore, Durga Prasad Mohapatra
https://arxiv.org/abs/2506.16224

Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
This paper investigates the application of natural language processing (NLP)-based n-gram analysis and machine learning techniques to enhance malware classification. We explore how NLP can be used to extract and analyze textual features from malware samples through n-grams, contiguous string or API call sequences. This approach effectively captures distinctive linguistic patterns among malware and benign families, enabling finer-grained classification. We delve into n-gram size selection, featu…

Improving Genomic Models via Task-Specific Self-Pretraining
Sohan Mupparapu, Parameswari Krishnamurthy, Ratish Puduppully
https://arxiv.org/abs/2506.17766 …

Improving Genomic Models via Task-Specific Self-Pretraining
Pretraining DNA language models (DNALMs) on the full human genome is resource-intensive, yet often considered necessary for strong downstream performance. Inspired by recent findings in NLP and long-context modeling, we explore an alternative: self-pretraining on task-specific, unlabeled data. Using the BEND benchmark, we show that DNALMs trained with self-pretraining match or exceed the performance of models trained from scratch under identical compute. While genome-scale pretraining may still…