arXiv Machine Learning August 4, 2026 By Martin Mocko, Daniela Chud\'a

Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Learning

Read the original on arXiv Machine Learning →

arXiv:2608. 02348v1 Announce Type: cross Abstract: Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 13

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

arXiv:2509. 23449v2 Announce Type: replace Abstract: Binary code similarity detection is a core task in reverse engineering.

By Charles E. Gagnon, Steven H. H. Ding, Philippe Charland, Benjamin C. M. Fung

llms rag agents benchmarks safety

arXiv AI

Sep 17

MiST: Mid-Training LLMs for Cybersecurity

MiST (Mid-trained Security Transformer) is a suite of 8B and 32B language models tailored for cybersecurity, achieving strong performance on public benchmarks. The approach uses a mid-training stage that adapts general pre-trained models to the domain by curating a compact, expert-vetted seed corpus and generating high-quality synthetic training data, rather than continual pre-training on large raw text. MiST checkpoints improve mean cybersecurity accuracy by +13.1 and +8.6 absolute percentage points over Qwen baselines for 8B and 32B models, respectively, and provide a stronger initialization for downstream task-specific fine-tuning and reinforcement learning.

By Oded Ovadia, Elad Ben Zaken, Elad Guttman, Orly Moreno Kadosh

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv AI

Jul 31

Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

arXiv:2607. 26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS).

By Yinan Gao, Jiarong Xu, Xiaohang Zhao, Xiao Fang

arXiv AI

Aug 18

Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis

arXiv:2511. 11439v3 Announce Type: replace-cross Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics.

By Yiling He, Junchi Lei, Hongyu She, Shuo Shao, Xinran Zheng, Yiping Liu, Zhan Qin, Lorenzo Cavallaro

nlp fine-tuning

arXiv AI

Jun 3

High-Precision APT Malware Attribution with Out-of-Scope Resilience

arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.

By Peter Williams, Adam Sobey, Erisa Karafili

One email a morning, machine-written

Related stories

Beyond Embeddings: Interpretable Feature Extraction for Binary Code Similarity

MiST: Mid-Training LLMs for Cybersecurity

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis

High-Precision APT Malware Attribution with Out-of-Scope Resilience