Study on cyber-security for IoT edge utilizing pattern match accelerator

Masamori Kashiyama, Reo Kashiyama, Hiroto Seki, Hiroyuki Hosono

Research output: Contribution to journalArticlepeer-review


A malware detection algorithm that can be embedded in IoT edge computing is proposed in this study and validated using an emulator. This algorithm, with a pattern match accelerator, reduces the computing cost while maintaining a relatively high detection accuracy. For autonomous driving, complicated IoT edge computing must have a huge amount of embedded program codes. In such a situation, the invasion of malware can lead to compromised cybersecurity. In this study, a pattern match accelerator is implemented for such issues, thereby offering IoT edge computing that detects malware automatically. Edge computing is designed to apply simply structural level analysis algorithms using HLAC mask pattern. We developed a pseudo-emulator system environment and conducted performance confirmation of the proposed technique using 641 chosen samples from six types of malware families. The algorithm’s efficiencies demonstrated an identification performance of approximately 80%. In comparison to characteristic extraction using AI, the computing cost was reduced and these processes enable edge computing with high cybersecurity features.

Original languageEnglish
Pages (from-to)15-28
Number of pages14
JournalIEEJ Transactions on Industry Applications
Issue number1
Publication statusPublished - 2020


  • Cyber-security
  • Higher-order Local Auto Correlation (HLAC)
  • IoT edge computing
  • Malware
  • Pattern match accelerator
  • Texture image


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