DaemonSec is an early-stage startup exploring machine learning (ML)-based security for Linux daemons, a critical yet often overlooked attack surface. While daemon security remains underexplored, conventional defenses struggle against adaptive threats and zero-day exploits. To assess the perspectives of IT professionals on ML-driven daemon protection, a systematic interview study based on semi-structured interviews was conducted with 22 professionals from industry and academia. The study evaluates adoption, feasibility, and trust in ML-based security solutions. While participants recognized the potential of ML for real-time anomaly detection, findings reveal skepticism toward full automation, limited security awareness among non-security roles, and concerns about patching delays creating attack windows. This paper presents the methods, key findings, and implications for advancing ML-driven daemon security in industry.

翻译：DaemonSec是一家探索基于机器学习（ML）的Linux守护进程安全的早期初创企业，该领域是一个关键但常被忽视的攻击面。尽管守护进程安全研究尚不充分，传统防御措施在应对自适应威胁和零日漏洞利用方面存在不足。为评估IT专业人员对ML驱动守护进程保护的看法，本研究基于半结构化访谈，对来自工业界和学术界的22名专业人员进行了系统性访谈研究。该研究评估了ML安全解决方案的采用度、可行性和可信度。尽管参与者认可ML在实时异常检测方面的潜力，但研究发现存在对完全自动化的怀疑、非安全岗位人员安全意识有限，以及对补丁延迟造成攻击窗口的担忧。本文介绍了推动工业界ML驱动守护进程安全的方法、关键发现及启示。