arXiv：cs.LG（机器学习，全量分类）· Emmanuela Andam, Rana Shaaban, Emanuel Grant, Naima Kaabouch·· 15 小时前AI 评分25
基于结构化状态空间序列模型（S4）的恶意软件多分类研究
A Structured State Space Sequence Model for Multi-Class Classification of Malware
AI 导读
研究首次将结构化状态空间序列（S4）模型用于恶意软件分析，对恶意软件样本序列进行离散化并捕捉长程依赖，识别执行流中的因果关系。该工作还将其性能与其他深度学习架构进行了全面对比，论文已被 2026 IEEE World AI IoT Congress（AIIoT）接收。
正文
Abstract: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.
| Comments: | Accepted at 2026 IEEE World AI IoT Congress (AIIoT). This is the author's accepted manuscript |
| Subjects: | Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.01893 [cs.CR] |
| (or arXiv:2610.01893v1 [cs.CR] for this version) | |
| https://doi.org/10.48550/arXiv.2610.01893 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Emmanuela Andam [view email]
[v1]
Thu, 1 Oct 2026 15:42:56 UTC (3,449 KB)
来源：arXiv：cs.LG（机器学习，全量分类） · arxiv.org