{"title":"基于自整流 Memristor 的水库计算用于网络安全中的实时入侵检测。","authors":"Guobin Zhang, Zijian Wang, Xuemeng Fan, Pengtao Li, Dawei Gao, Zhenyong Zhang, Qing Wan, Yishu Zhang","doi":"10.1021/acs.nanolett.4c04385","DOIUrl":null,"url":null,"abstract":"<p><p>The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO<sub>3-<i>x</i></sub>/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.</p>","PeriodicalId":53,"journal":{"name":"Nano Letters","volume":" ","pages":""},"PeriodicalIF":9.6000,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-Rectifying Memristor-Based Reservoir Computing for Real-Time Intrusion Detection in Cybersecurity.\",\"authors\":\"Guobin Zhang, Zijian Wang, Xuemeng Fan, Pengtao Li, Dawei Gao, Zhenyong Zhang, Qing Wan, Yishu Zhang\",\"doi\":\"10.1021/acs.nanolett.4c04385\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO<sub>3-<i>x</i></sub>/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.</p>\",\"PeriodicalId\":53,\"journal\":{\"name\":\"Nano Letters\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-11-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nano Letters\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1021/acs.nanolett.4c04385\",\"RegionNum\":1,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nano Letters","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1021/acs.nanolett.4c04385","RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
Self-Rectifying Memristor-Based Reservoir Computing for Real-Time Intrusion Detection in Cybersecurity.
The increasing sophistication of cybersecurity threats, driven by the proliferation of big data and the Internet of Things (IoT), necessitates the development of advanced real-time intrusion detection systems (IDSs). In this study, we present a novel approach that integrates NiO-doped WO3-x/ZnO bilayer self-rectifying memristors (SRMs) within a reservoir computing (RC) framework for IDS applications. The proposed crossbar array architecture exploits the exceptional dynamic properties of SRMs, achieving a classification accuracy of 93.07% on the CSE-CIC-IDS2018 data set, while demonstrating ultrahigh information-processing efficiency. Our approach not only leverages the tunable characteristics of memristors but also addresses the challenge of sneak path currents in large-scale integration, offering a robust and scalable solution for next-generation IDS. This work exemplifies the power of emerging electronics in enhancing cybersecurity through innovative hardware implementations.
期刊介绍:
Nano Letters serves as a dynamic platform for promptly disseminating original results in fundamental, applied, and emerging research across all facets of nanoscience and nanotechnology. A pivotal criterion for inclusion within Nano Letters is the convergence of at least two different areas or disciplines, ensuring a rich interdisciplinary scope. The journal is dedicated to fostering exploration in diverse areas, including:
- Experimental and theoretical findings on physical, chemical, and biological phenomena at the nanoscale
- Synthesis, characterization, and processing of organic, inorganic, polymer, and hybrid nanomaterials through physical, chemical, and biological methodologies
- Modeling and simulation of synthetic, assembly, and interaction processes
- Realization of integrated nanostructures and nano-engineered devices exhibiting advanced performance
- Applications of nanoscale materials in living and environmental systems
Nano Letters is committed to advancing and showcasing groundbreaking research that intersects various domains, fostering innovation and collaboration in the ever-evolving field of nanoscience and nanotechnology.