智能电网网络攻击检测

Aditi Kar Gangopadhyay, Tanay Sheth, Tanmoy Kanti Das, Sneha Chauhan
{"title":"智能电网网络攻击检测","authors":"Aditi Kar Gangopadhyay,&nbsp;Tanay Sheth,&nbsp;Tanmoy Kanti Das,&nbsp;Sneha Chauhan","doi":"10.1007/s43674-022-00042-y","DOIUrl":null,"url":null,"abstract":"<div><p>The paper analyzes observations using a logic-based numerical methodology in Python. The Logical Analysis of Data (LAD) specializes in selecting a minimal number of features and finding unique patterns within it to distinguish ‘positive’ from ‘negative’ observations. The Python implementation of the classification model is further improved by introducing adaptations to pattern generation techniques. Finally, a case study of the Power Attack Systems Dataset used to improvise Smart Grid technology is performed to explore real-life applications of the classification model and analyze its performance against commonly used techniques.</p></div>","PeriodicalId":72089,"journal":{"name":"Advances in computational intelligence","volume":"2 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of cyber attacks on smart grids\",\"authors\":\"Aditi Kar Gangopadhyay,&nbsp;Tanay Sheth,&nbsp;Tanmoy Kanti Das,&nbsp;Sneha Chauhan\",\"doi\":\"10.1007/s43674-022-00042-y\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The paper analyzes observations using a logic-based numerical methodology in Python. The Logical Analysis of Data (LAD) specializes in selecting a minimal number of features and finding unique patterns within it to distinguish ‘positive’ from ‘negative’ observations. The Python implementation of the classification model is further improved by introducing adaptations to pattern generation techniques. Finally, a case study of the Power Attack Systems Dataset used to improvise Smart Grid technology is performed to explore real-life applications of the classification model and analyze its performance against commonly used techniques.</p></div>\",\"PeriodicalId\":72089,\"journal\":{\"name\":\"Advances in computational intelligence\",\"volume\":\"2 5\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in computational intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s43674-022-00042-y\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in computational intelligence","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s43674-022-00042-y","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文使用Python中基于逻辑的数值方法分析观测结果。数据逻辑分析(LAD)专门选择最小数量的特征,并在其中找到独特的模式,以区分“积极”和“消极”的观察结果。通过引入对模式生成技术的调整,进一步改进了分类模型的Python实现。最后,对用于即兴开发智能电网技术的电力攻击系统数据集进行了案例研究,以探索分类模型的实际应用,并分析其相对于常用技术的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Detection of cyber attacks on smart grids

The paper analyzes observations using a logic-based numerical methodology in Python. The Logical Analysis of Data (LAD) specializes in selecting a minimal number of features and finding unique patterns within it to distinguish ‘positive’ from ‘negative’ observations. The Python implementation of the classification model is further improved by introducing adaptations to pattern generation techniques. Finally, a case study of the Power Attack Systems Dataset used to improvise Smart Grid technology is performed to explore real-life applications of the classification model and analyze its performance against commonly used techniques.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Non-linear machine learning with sample perturbation augments leukemia relapse prognostics from single-cell proteomics measurements ARBP: antibiotic-resistant bacteria propagation bio-inspired algorithm and its performance on benchmark functions Detection and classification of diabetic retinopathy based on ensemble learning Office real estate price index forecasts through Gaussian process regressions for ten major Chinese cities Systematic micro-breaks affect concentration during cognitive comparison tasks: quantitative and qualitative measurements
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1