arXiv Machine Learning

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

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.

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

llms benchmarks

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

benchmarks

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

llms

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

rag

arXiv AI

Jul 1

CVE-TTP KG: Knowledge Graph Linking Software Vulnerabilities to Attack Behaviors

arXiv:2606. 31557v1 Announce Type: cross Abstract: In the evolving threat landscape, adversaries exploit software vulnerabilities to launch sophisticated attacks, challenging traditional defenses.

By Basant Agarwal, Dincy R. Arikkat, Swati Yadav, Serena Nicolazzo, Antonino Nocera, Vinod P

llms

arXiv AI

Jun 3

A Hybrid Approach For Malware Classification Using Secondary Features Fusion

arXiv:2606. 03432v1 Announce Type: cross Abstract: The number of malware (either variant or novel) is rapidly increasing, making malware detection and mitigation a complex problem.

By Raja Khurram Shahzad, Muhammad Mustaqeem, Haroon Elahi

arXiv AI

6d ago

Evaluating LLM Generated Detection Rules in Cybersecurity

arXiv:2509. 16749v1 Announce Type: cross Abstract: LLMs are increasingly pervasive in the security environment, with limited measures of their effectiveness, which limits trust and usefulness to security practitioners.

By Anna Bertiger, Bobby Filar, Aryan Luthra, Stefano Meschiari, Aiden Mitchell, Sam Scholten, Vivek Sharath

llms 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 Machine Learning

Jul 3

Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes

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.

By Derek Everett, Edward Raff, James Holt

arXiv AI

Jul 9

Large Language Models (LLMs) and Generative AI in Cybersecurity and Privacy: A Survey of Dual-Use Risks, AI-Generated Malware, Explainability, and Defensive Strategies

arXiv:2607. 06963v1 Announce Type: cross Abstract: Large Language Models (LLMs) and generative AI (GenAI) systems, such as ChatGPT, Claude, Gemini, LLaMA, Copilot, Stable Diffusion by OpenAI, Anthropic, Google, Meta, Microsoft, Stability AI, respectively, are revolutionizing cybersecurity, enabling both automated defense and sophisticated attacks.

By Kiarash Ahi, Saeed Valizadeh

llms diffusion benchmarks safety

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