发表机构
Artificial Intelligence Research (AIR) Center（人工智能研究（AIR）中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对物联网恶意软件威胁，提出基于结构化状态空间序列（S4）模型的恶意软件检测与分类新框架，首次实证应用S4模型并与其他深度学习架构全面比较，提升系统安全性。
AI 中文摘要
到2030年，物联网（IoT）设备预计将达到400亿台，工业、医疗、农业、汽车以及建筑/家居自动化系统等领域的技术发展日新月异。这种扩张为网络犯罪创造了巨大的攻击面，因为大多数此类设备缺乏足够的内置安全性，为网络犯罪分子利用漏洞打开了大门。网络犯罪分子发动恶意软件攻击以破坏系统或窃取敏感数据，一旦系统被攻破，通常会被要求支付赎金才能解除锁定。当前部署的网络安全措施正被物联网的快速增长所超越，随之而来的是每天产生的恶意软件变种数量不断增加。认识到这一缺陷，本研究探讨并提出了一种新颖的恶意软件检测与分类方法，以保护设备免受进一步攻击，并使物联网系统更加健壮和安全。所提出的框架采用结构化状态空间序列（S4）模型，该模型对恶意软件样本序列进行离散化处理，并捕获长距离依赖关系，本质上识别恶意软件执行流程中隐藏的“因果”关系。本研究提出了两项新颖贡献：S4模型在恶意软件分析中的首次实证应用，以及其与其他深度学习架构性能的全面比较，为这一新范式的未来研究奠定了基础。
英文摘要
By 2030, Internet of Things (IoT) devices are projected to reach 40 billion, with fast-paced technological advancements in fields such as industry, healthcare, agriculture, automobiles, and building/home automation systems. This expansion has created a large attack surface for cybercrime, as the majority of these devices open the door for cybercriminals to exploit vulnerabilities, as they lack adequate built-in security. Cybercriminals launch malware attacks to compromise systems or steal sensitive data, and once a system is compromised, a ransom is typically demanded for its release. Current cybersecurity measures in place are being outpaced by the rapid growth of the IoT, which is accompanied by a subsequent growth in malware variants being created per day. Recognizing this pitfall, this research examines and proposes a novel approach to malware detection and classification to safeguard devices from further attacks and make IoT systems more robust and secure. The framework proposed utilizes a Structured State Space Sequence (S4) model, which discretizes sequences of malware samples in a sequence and captures long-range dependencies, essentially identifying the "cause" and "effect" hidden within malware execution flow. This study presents two novel contributions: the first empirical application of the S4 model for malware analysis, and a comprehensive comparison of its performance against other deep learning architectures, laying the stepping stone for future research in this new paradigm.
CommentsAccepted at 2026 IEEE World AI IoT Congress (AIIoT). This is the author's accepted manuscript
