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是一家专注于Linux守护进程基于机器学习（ML）安全防护的早期初创企业，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，传统防御措施在面对适应性威胁和零日漏洞利用时仍显不足。为评估IT专业人士对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位专业人士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情况、可行性及信任度。尽管参与者认可ML在实时异常检测方面的潜力，但研究结果揭示了他们对完全自动化的怀疑、非安全岗位人员有限的安全意识，以及对补丁延迟可能造成攻击窗口的担忧。本文阐述了研究方法、关键发现，以及对推动工业界ML驱动守护进程安全发展的启示。