Double‐Layer Positional Encoding Embedding Method for Cross‐Platform Binary Function Similarity Detection

Jiang, Xunzhi; WANG, Shen; Yu, Xiangzhan; Gong, Yuxin

doi:10.1049/cje.2021.00.139

Cited by 4 publications

(2 citation statements)

References 20 publications

(51 reference statements)

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“…After designing the basic resource layer, this paper designs the platform service layer [11]. The basic resource layer is mainly responsible for theoretical research.…”

Section: Hardware Design Of Power Marketing Meteringmentioning

confidence: 99%

Power Metering Automation System Based on Internet of Things

Dai

2022

Wireless Communications and Mobile Computing

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In order to solve the problems of poor stability and low operation efficiency of the traditional power marketing metering production automatic scheduling system, this paper proposes a power metering automation system based on the Internet of Things. In this paper, the hardware and software of the system are designed under the environment of the Internet of Things. The hardware part is mainly divided into three platforms: basic resource layer, platform business layer, and interface presentation layer. The normal operation is realized through the network topology, and the load balancing server and business subsystem are emphatically designed. The software is completed in four steps: coding, scheduling, backup, and display. The experimental results show that the integrity rate of the electric quantity data collected by this system in the experiment with the traditional system is as high as 99%. Compared with the traditional dispatching system, the power information dispatching system designed under the Internet of Things has better stability and higher operation efficiency.

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“…After designing the basic resource layer, this paper designs the platform service layer [11]. The basic resource layer is mainly responsible for theoretical research.…”

Section: Hardware Design Of Power Marketing Meteringmentioning

confidence: 99%

Power Metering Automation System Based on Internet of Things

Dai

2022

Wireless Communications and Mobile Computing

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“…In 2022 S. X Jiang, X. Wang, Yu, and Y. Gong [16] suggested a cross-platform technique for binary function similarity identification using dual-layer positional encoding. Using the MISA dataset for evaluation, the DP-MIRROR technique outperformed the advanced method with an accuracy of approximately 35%.…”

Section: ░ 2 Related Workmentioning

confidence: 99%

SimCoDe-NET: Similarity Detection in Binary Code using Deep Learning Network

Poornima,

Mahalakshmi

2023

IJEER

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Binary code similarity detection is a fundamental task in the field of computer binary security. However, code similarity is crucial today because of the prevalence of issues like plagiarism, code cloning, and recycling in software due to the ongoing increase of software scale. To resolve these issues, a novel SIMilarity detection in binary COde using DEep learning NETwork (SimCoDe-NET) has been proposed. Initially, op-code features are extracted from the input data by using reverse engineering process and the opcode embedding is generated using N-skip gram method. The extracted features are fed into Bi-GRU neural network for classifying the similarity of the binary codes. The Bi-GRU neural network compares two data samples in feature space to identify whether they belong to similar data or non-similar data. The SimCoDe-NET framework is evaluated by using generated dataset to assess the efficiency of this method. The efficacy of the proposed SimCoDe-NET framework is assessed in terms of precision, accuracy, sensitivity, recall, similarity detection time and similarity detection rate. The accuracy of the proposed method is 99.10% which is relatively high compared to the existing method. The proposed SimCoDe-NET approach improves the accuracy by 84.9%, 88.58%, and 93.9% better than jTrans, UPPC, and HEBCS respectively.

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