cs.CR · 2026-09-16 · No. 115

Cryptography and Security, 2026-09-16.

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

    Cross-Domain Inference for Human Localization: Applying Wi-Fi RSSI Data to CSI-Trained Models

    Ariel Duschanek-Myers, Thomas Welsh, Helmut Neukirchen

    cs.CR · cs.LG · cs.NI

    Wi-Fi signal data can be used to compromise the privacy of individuals. While many existing approaches rely on Channel State Information (CSI), collecting this data on typical IoT devices often requires elevated operating system permissions and specialized drivers. Consequently, this paper investigates the feasibility of utilizing Received Signal Strength Indicator (RSSI) data to predict human locations. RSSI was selected because it is...

    arxiv.org/abs/2609.17204 · PDF

  2. 02

    The MAL Simulator: Cyber Operations Simulation based on Attack & Defense Graphs

    Jakob Nyberg, Sandor Berglund, Andrei Buhaiu, Joakim Loxdal, Pontus Johnson, Mathias Ekstedt

    cs.CR · cs.AI

    We have developed the MAL Simulator, a cyber operation simulator based on the Meta Attack Language (MAL). The MAL Simulator is intended for decision-driven cyber attack and defense simulations, for system analysis and the development of automated agents. By building the simulator around an attack modeling language, it can be adapted to different target domains without modifying the source code. We used the simulator for two case studies where...

    arxiv.org/abs/2609.16563 · PDF

  3. 03

    A Cyber Range Evaluation of Autonomous Network Incident Response Agents

    Jakob Nyberg, Teodor Sommestad, Andrei Buhaiu, Joakim Loxdal, Pontus Johnson, Mathias Ekstedt

    cs.CR · cs.AI

    We test the performance of agents for automated network intrusion response in a cyber range intended for human operator training. The range implements an emulated networking environment with a variable network topology, red-team emulation and simulated user agents. The goal of the defensive agents is to prevent hosts in the network from being accessed by the red-team agent, while minimizing the availability costs induced from defensive...

    arxiv.org/abs/2609.16541 · PDF

  4. 04

    Not All Relations Are Equal: Relation-Balanced and Calibrated Graph Learning for Provenance-Based Intrusion Detection

    Lijie Zheng, Ji He, Alessandro Brighente, Yulong Shen, Mauro Conti

    cs.CR · cs.LG

    Provenance-Based Intrusion Detection Systems (PIDSs) detect Advanced Persistent Threats (APTs) by analyzing system interactions. However, existing methods largely treat relations uniformly, overlooking statistical heterogeneity; in CADETS, relation frequencies differ by approximately $140{,}000\times$. This may cause PIDSs to focus more on frequent relations and overlook differences in normal error levels across relations, increasing the risk...

    arxiv.org/abs/2609.16462 · PDF

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