cs.CR · 2026-08-04 · No. 74

Cryptography and Security, 2026-08-04.

4 new papers in cs.CR. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

4 entries
  1. 01

    Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning

    Yiran Gao, Tao Li, Kim Hammar

    cs.CR · cs.AI

    Incident response is currently managed by security operators using predefined playbooks, resulting in slow, labor-intensive security decision-making processes. Consequently, there is a growing need for automated incident response planning. Decision-theoretic approaches based on control, optimization, and reinforcement learning have been proposed to automate such planning tasks with well-grounded approaches, yet most of which, while...

    arxiv.org/abs/2608.02422 · PDF

  2. 02

    Antares: Foundation Models for Agentic Vulnerability Localization

    Supriti Vijay, Aman Priyanshu, Didier Chapoteau, Arthur Goldblatt, Jianliang He, Kimia Majd, Fraser Burch, Baturay...

    cs.CR · cs.AI

    Vulnerability localization is a fundamental step in software security, requiring models to reason over large codebases and iteratively identify vulnerable implementations. We present Antares, a family of compact language models (350M, 1B, and 3B parameters) for agentic vulnerability localization. Based on IBM Granite base models, Antares is trained through a two-stage pipeline that combines supervised fine-tuning on cybersecurity reasoning...

    arxiv.org/abs/2608.02407 · PDF

  3. 03

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

    Martin Mocko, Daniela Chudá

    cs.CR · cs.LG

    Malware clustering is a critical task in cybersecurity that helps discover threats and analyze evolving malware families. While self-supervised learning (SSL) and tabular representation learning (TRL) have achieved breakthroughs in other domains, their application to binary program clustering (the task of clustering all incoming samples regardless of label) remains largely unexplored. This study presents the first systematic investigation of...

    arxiv.org/abs/2608.02348 · PDF

  4. 04

    A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks

    Li Yang

    cs.CR · cs.LG

    Electric Vehicle Charging Systems (EVCSs) are increasingly connected with Internet of Things (IoT) devices, which improves charging intelligence but also expands their exposure to cyber-attacks. Intrusion Detection Systems (IDSs) are essential for securing EV charging networks; however, conventional Machine Learning (ML)-based IDSs often rely on manual model design and mainly optimize detection performance without fully considering inference...

    arxiv.org/abs/2608.02274 · PDF

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