most citedData-Centric Machine Learning Approach for Early Ransomware Detection and Attribution

25 citations · 31 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CR2026

Does Runtime Topology Context Improve LLM-Generated Kubernetes Security Patches?

Farooq Shaikh

Kubernetes is central to the cloud-native ecosystem, orchestrating containerised workloads. Recent work suggests that large language models (LLMs) can automate cluster security rem…

cs.CR2026

A Comparison of Kubernetes Compliance Standards and Configuration Scanners

Michael Krieger, Markus Gierlinger, Farooq Shaikh +1

Kubernetes has become the industry standard for orchestrating containers in microservice-based software architectures. While several hardening guidelines and scanning tools for sec…

cs.CR2026

FORGE: Multi-Agent Graduated Exploitation and Detection Engineering

Farooq Shaikh

Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritizati…

cs.CR2023

Federated Learning Approach for Distributed Ransomware Analysis

Aldin Vehabovic, Hadi Zanddizari, Farook Shaikh +4

Researchers have proposed a wide range of ransomware detection and analysis schemes. However, most of these efforts have focused on older families targeting Windows 7/8 systems. He…

cs.CR2023★ 6 cited

IoT Threat Detection Testbed Using Generative Adversarial Networks

Farooq Shaikh, Elias Bou-Harb, Aldin Vehabovic +3

The Internet of Things(IoT) paradigm provides persistent sensing and data collection capabilities and is becoming increasingly prevalent across many market sectors. However, most I…

cs.CR2023★ 25 cited

Data-Centric Machine Learning Approach for Early Ransomware Detection and Attribution

Aldin Vehabovic, Hadi Zanddizari, Nasir Ghani +4

Researchers have proposed a wide range of ransomware detection and analysis schemes. However, most of these efforts have focused on older families targeting Windows 7/8 systems. He…