[2601.16795] Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt

[2601.16795] Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt

arXiv - Machine Learning 4 min read

About this article

Abstract page for arXiv paper 2601.16795: Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt

Computer Science > Cryptography and Security arXiv:2601.16795 (cs) [Submitted on 23 Jan 2026 (v1), last revised 21 Mar 2026 (this version, v2)] Title:Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt Authors:Kenan Begovic, Abdulaziz Al-Ali, Qutaibah Malluhi View a PDF of the paper titled Building a Robust Risk-Based Access Control System to Combat Ransomware's Capability to Encrypt, by Kenan Begovic and Abdulaziz Al-Ali and Qutaibah Malluhi View PDF HTML (experimental) Abstract:Ransomware core capability, unauthorized encryption, demands controls that identify and block malicious cryptographic activity without disrupting legitimate use. We present a probabilistic, risk-based access control architecture that couples machine learning inference with mandatory access control to regulate encryption on Linux in real time. The system builds a specialized dataset from the native ftrace framework using the function_graph tracer, yielding high-resolution kernel-function execution traces augmented with resource and I/O counters. These traces support both a supervised classifier and interpretable rules that drive an SELinux policy via lightweight booleans, enabling context-sensitive permit/deny decisions at the moment encryption begins. Compared to approaches centered on sandboxing, hypervisor introspection, or coarse system-call telemetry, the function-level tracing we adopt provides finer behavioral granularity than syscall-only telemetr...

Originally published on March 24, 2026. Curated by AI News.

Related Articles

Llms

[R] GPT-5.4-mini regressed 22pp on vanilla prompting vs GPT-5-mini. Nobody noticed because benchmarks don't test this. Recursive Language Models solved it.

GPT-5.4-mini produces shorter, terser outputs by default. Vanilla accuracy dropped from 69.5% to 47.2% across 12 tasks (1,800 evals). The...

Reddit - Machine Learning · 1 min ·
Top 10 AI certifications and courses for 2026
Ai Startups

Top 10 AI certifications and courses for 2026

This article reviews the top 10 AI certifications and courses for 2026, highlighting their significance in a rapidly evolving field and t...

AI Events · 15 min ·
Hub Group Using AI, Machine Learning for Real-Time Visibility of Shipments
Machine Learning

Hub Group Using AI, Machine Learning for Real-Time Visibility of Shipments

Hub Group says it’s using artificial intelligence and machine learning to leverage data from its GPS-equipped container fleet to give cus...

AI Events · 4 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·
More in Machine Learning: This Week Guide Trending

No comments

No comments yet. Be the first to comment!

Stay updated with AI News

Get the latest news, tools, and insights delivered to your inbox.

Daily or weekly digest • Unsubscribe anytime