arXiv Machine Learning June 8, 2026 By Parthajit Borah, Sakshi Singh, D. K. Bhattacharyya, J. K. Kalita

AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification

Read the original on arXiv Machine Learning →

arXiv:2606. 06815v1 Announce Type: cross Abstract: As malware illustrates a complex structure and behavior, detection of these has been a significant challenge in the domain of cybersecurity along with related services in daily life.

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

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 Machine Learning

Jun 9

MOLOT System Card: Malicious Operational Logic Observation Transformer

arXiv:2606. 07792v1 Announce Type: cross Abstract: MOLOT (Malicious Operational Logic Observation Transformer) is a static malicious-code detection system designed for SAST setup where package metadata, maintainer history, and dynamic execution traces may be unavailable or unreliable.

By Daniil Lopatkin, Maksim Mitrofanov, Stanislav Rakovsky, Aleksandr Khalikov

llms benchmarks

arXiv AI

Jul 7

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection

arXiv:2607. 03350v1 Announce Type: cross Abstract: Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI.

By Hang Gao, Xiaoyu Chen, Baoquan Cui, Zhen Tang, Peng Qiao, Fengge Wu, Jian Zhang

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arXiv Machine Learning

Jun 9

A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection

arXiv:2510. 26307v3 Announce Type: replace-cross Abstract: Anomaly detection is a critical task in cybersecurity, where identifying insider threats, access violations, and coordinated attacks is essential for ensuring system resilience.

By Laura Jiang, Reza Ryan, Qian Li, Nasim Ferdosian

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arXiv AI

Jul 1

AI-Generated PowerShell Malware: An Experimental Framework and Dataset

arXiv:2606. 30819v1 Announce Type: cross Abstract: Generative AI has emerged as a significant cybersecurity threat, with several recent attack campaigns leveraging LLMs to generate code for malicious purposes via scripting languages such as PowerShell.

By Luciano Pianese, Vittorio Orbinato, Pietro Liguori, Roberto Natella

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arXiv AI

Jun 8

MalTree: Tracing Malware Evolution from Embeddings at Scale

arXiv:2606. 06570v1 Announce Type: cross Abstract: Malware detection remains largely reactive: machine learning models trained on known samples degrade as threats evolve.

By Akash Amalan, Georgios Smaragdakis, Tom J. Viering

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