利用盲源分离算法和神经网络分类器检测恶意无人机

K. Lakshmipriya, S.P. Charu Prafulla, S. Lokesh, O. U. Maheswari
{"title":"利用盲源分离算法和神经网络分类器检测恶意无人机","authors":"K. Lakshmipriya, S.P. Charu Prafulla, S. Lokesh, O. U. Maheswari","doi":"10.55041/ijsrem36797","DOIUrl":null,"url":null,"abstract":"Unmanned aerial vehicle(UAV) technology is the rapid growing technology in the field of monitoring for security purposes, pesticides spraying and various other applications. In the recent days, one of the major concerns is entering of malicious UAVs into the secured perimeter that might result in Drone-based cyberattacks. So, the detection of these malicious UAVs are crucial. In this work, an acoustic method of detecting malicious UAVs is proposed. The mixed form of the acoustic signals of two kinds of drones, namely, Fixed- wing and Multi- rotor are passed through the Blind Source Separation (BSS) block, where the kurtosis is measured along with Independent Component Analysis (ICA) for the separation of the signals. Then the distinctive features, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC) and short time energy are extracted from the acoustic signal and are trained using Neural Network(NN) classifier to identify the malicious UAV. The proposed method under different conditions outperforms the existing techniques with an accuracy of 100% in identification of malicious UAV. Key Words: Blind Source Separation Algorithm, kurtosis, Independent Component Analysis, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC), short time energy, Neural Networks","PeriodicalId":504501,"journal":{"name":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","volume":"51 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Malicious UAV Detection Using Blind Source Separation Algorithm and Neural Network Classifier\",\"authors\":\"K. Lakshmipriya, S.P. Charu Prafulla, S. Lokesh, O. U. Maheswari\",\"doi\":\"10.55041/ijsrem36797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Unmanned aerial vehicle(UAV) technology is the rapid growing technology in the field of monitoring for security purposes, pesticides spraying and various other applications. In the recent days, one of the major concerns is entering of malicious UAVs into the secured perimeter that might result in Drone-based cyberattacks. So, the detection of these malicious UAVs are crucial. In this work, an acoustic method of detecting malicious UAVs is proposed. The mixed form of the acoustic signals of two kinds of drones, namely, Fixed- wing and Multi- rotor are passed through the Blind Source Separation (BSS) block, where the kurtosis is measured along with Independent Component Analysis (ICA) for the separation of the signals. Then the distinctive features, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC) and short time energy are extracted from the acoustic signal and are trained using Neural Network(NN) classifier to identify the malicious UAV. The proposed method under different conditions outperforms the existing techniques with an accuracy of 100% in identification of malicious UAV. Key Words: Blind Source Separation Algorithm, kurtosis, Independent Component Analysis, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC), short time energy, Neural Networks\",\"PeriodicalId\":504501,\"journal\":{\"name\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"volume\":\"51 11\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.55041/ijsrem36797\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.55041/ijsrem36797","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

无人驾驶飞行器(UAV)技术在安全监控、农药喷洒和其他各种应用领域发展迅速。最近,人们关注的一个主要问题是恶意无人飞行器进入安全边界,这可能会导致基于无人机的网络攻击。因此,检测这些恶意无人机至关重要。在这项工作中,提出了一种检测恶意无人机的声学方法。固定翼和多旋翼两种无人机的混合声学信号通过盲源分离(BSS)模块,其中峰度测量与独立分量分析(ICA)用于分离信号。然后,从声学信号中提取出独特的特征、梅尔-频率共振频率系数(MFCC)、伽马音-频率共振频率系数(GTCC)和短时能量,并使用神经网络(NN)分类器进行训练,以识别恶意无人机。所提出的方法在不同条件下识别恶意无人机的准确率为 100%,优于现有技术。关键字盲源分离算法、峰度、独立分量分析、Mel-Frequency Cepstral Coefficient(MFCC)、Gamma tone-Frequency Cepstral Coefficient(GTCC)、短时能量、神经网络
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malicious UAV Detection Using Blind Source Separation Algorithm and Neural Network Classifier
Unmanned aerial vehicle(UAV) technology is the rapid growing technology in the field of monitoring for security purposes, pesticides spraying and various other applications. In the recent days, one of the major concerns is entering of malicious UAVs into the secured perimeter that might result in Drone-based cyberattacks. So, the detection of these malicious UAVs are crucial. In this work, an acoustic method of detecting malicious UAVs is proposed. The mixed form of the acoustic signals of two kinds of drones, namely, Fixed- wing and Multi- rotor are passed through the Blind Source Separation (BSS) block, where the kurtosis is measured along with Independent Component Analysis (ICA) for the separation of the signals. Then the distinctive features, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC) and short time energy are extracted from the acoustic signal and are trained using Neural Network(NN) classifier to identify the malicious UAV. The proposed method under different conditions outperforms the existing techniques with an accuracy of 100% in identification of malicious UAV. Key Words: Blind Source Separation Algorithm, kurtosis, Independent Component Analysis, Mel-Frequency Cepstral Coefficient(MFCC), Gamma tone-Frequency Cepstral Coefficient(GTCC), short time energy, Neural Networks
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信