arXiv AI June 10, 2026 By Sidahmed Benabderrahmanea, Petko Valtchev, James Cheney, Talal Rahwan

A Source Domain is All You Need: Source-Only Cross-OS Transfer Learning for APT Anomaly Detection via Semantic Alignment and Optimal Transport

arXiv:2606. 10216v1 Announce Type: cross Abstract: Advanced Persistent Threats (APTs) are stealthy, multi-stage cyberattacks whose detection is difficult due to scarce labeled traces, severe class imbalance, and the challenge of generating realistic malicious behavior.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 4

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

arXiv:2608. 01454v1 Announce Type: cross Abstract: Provenance-based intrusion detection systems (PIDS) frequently report strong performance, but the conclusions drawn from these results can be highly sensitive to benchmarking choices and evaluation protocols.

By Lorenzo Guerra, Thomas Chapuis, Guillaume Duc, Pavlo Mozharovskyi, Van-Tam Nguyen

benchmarks

arXiv Machine Learning

Jun 17

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.

By Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi, Md Erfan, Md Rayhanur Rahman

llms fine-tuning safety

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

arXiv Machine Learning

Jun 30

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data

arXiv:2408. 16028v4 Announce Type: replace-cross Abstract: Supervised-learning-based vulnerability detectors often fall short due to limited labelled training data.

By Weizhou Wang, Eric Liu, Xiangyu Guo, Xiao Hu, Ilya Grishchenko, David Lie

llms benchmarks

arXiv AI

Jun 3

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

arXiv:2606. 03453v1 Announce Type: cross Abstract: Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation.

By Farooq Shaikh

llms agents

arXiv AI

Jun 17

ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software

arXiv:2606. 17283v1 Announce Type: cross Abstract: Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at the cost of the others.

By Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria, Haoran Xi, Abdelouahab Benchikh, Tiffany Bao, Ruoyu Wang, Yan Shoshitaishvili, Adam Doup\'e, Hammond Pearce, Brendan Dolan-Gavitt

Related stories

How Benchmarks and Evaluation Protocols Shape Conclusions in Provenance-Based Intrusion Detection

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

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

ANVIL: Anomaly-based Vulnerability Identification without Labelled Training Data

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software