A Structured State Space Sequence Model for Multi-Class Classification of Malware
Researchers apply a Structured State Space model to classify IoT malware from long-range execution dependencies.
The paper proposes a Structured State Space Sequence (S4) model to detect and classify malware aimed at Internet of Things devices. It cites a projected 40 billion IoT devices by 2030 and argues that weak built-in security is increasing malware variants. S4 discretizes malware sequences and captures long-range dependencies in execution flow. The authors present this as the first empirical S4 malware study and compare it with other deep learning architectures, but the abstract gives no accuracy numbers.
- IoT devices are projected to reach 40 billion by 2030.
- An S4 model classifies malware by modeling long-range execution dependencies.
- Authors claim the first empirical use of S4 for malware analysis.
- The abstract reports no datasets, accuracy, or benchmark figures.
From the original article
1 min read at arxiv.orgBy 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.
By Emmanuela Andam · arxiv.org
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