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

Abstract

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.

Application of Internet of Things Technology in Computer Network Security and Remote-Control Analysis

“…The privacy protection mechanism mainly needs to establish a hierarchical work system, starting from the management aspect, and integrate into a reasonable interconnection. A hierarchical work system can be effective for privacy protection by ensuring clear roles and responsibilities, access controls, employee training and awareness, regular monitoring and auditing, and a clear incident response plan for dealing with breaches (Kashiyama et al, 2021). Network equipment and corresponding information protection codes are enhanced to strengthen the management of data information, thereby providing an important security protection barrier for personal information settings (Yilmaz & Uludag, 2021).…”

Pattern Matching in Python: Expanding the Horizons of Engineering Applications

“…The algorithm utilized a pattern match accelerator to reduce computing costs while maintaining a relatively high detection accuracy. Through performance testing with a pseudo-emulator system and a selection of 641 malware samples, the algorithm demonstrated an identification performance of approximately 80%, showcasing its potential to enable edge computing with robust cybersecurity features at a reduced computational expense compared to AI-based characteristic extraction [2]. Tahir et al developed a novel DNA sequences pattern matching algorithm called EPMA, aiming to address the challenge of pattern matching between different species in bioinformatics.…”