Using Blockchain, RAID, & BitTorrent Technologies to Secure Digital Evidence from Ransomware

Osama Sam Abuomar, Rebecca Yale Gross
{"title":"Using Blockchain, RAID, & BitTorrent Technologies to Secure Digital Evidence from Ransomware","authors":"Osama Sam Abuomar, Rebecca Yale Gross","doi":"10.1109/eIT57321.2023.10187306","DOIUrl":null,"url":null,"abstract":"Digital evidence is an important part of solving and prosecuting crimes. However, most the storage systems used are out-of-date and on unsecure networks. Attackers often exploit these system vulnerabilities through the use of ransomware. The recent COVID pandemic has seen a drastic rise in these types of attacks. If an attacker is able to ransom a system the digital evidence stored in it is locked and no longer available, which creates problems for police officers and prosecutors. To mitigate these attacks from destroying months or years of generated digital evidence, the use of blockchain, RAID network storage, and BitTorrent technologies are proposed. Blockchains are public or private ledgers made up of nodes. Each node contains a full copy of the ledger and secures it from tampering by a hash that points to the previous block in the chain. To keep the blockchain from getting too large and slowing down the system as more blocks are added to the chain, the use of RAID and BitTorrent technologies will be used to break up the digital evidence that has been generated.","PeriodicalId":113717,"journal":{"name":"2023 IEEE International Conference on Electro Information Technology (eIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE International Conference on Electro Information Technology (eIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/eIT57321.2023.10187306","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Digital evidence is an important part of solving and prosecuting crimes. However, most the storage systems used are out-of-date and on unsecure networks. Attackers often exploit these system vulnerabilities through the use of ransomware. The recent COVID pandemic has seen a drastic rise in these types of attacks. If an attacker is able to ransom a system the digital evidence stored in it is locked and no longer available, which creates problems for police officers and prosecutors. To mitigate these attacks from destroying months or years of generated digital evidence, the use of blockchain, RAID network storage, and BitTorrent technologies are proposed. Blockchains are public or private ledgers made up of nodes. Each node contains a full copy of the ledger and secures it from tampering by a hash that points to the previous block in the chain. To keep the blockchain from getting too large and slowing down the system as more blocks are added to the chain, the use of RAID and BitTorrent technologies will be used to break up the digital evidence that has been generated.
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
使用区块链,RAID和BitTorrent技术来保护勒索软件的数字证据
数字证据是解决和起诉犯罪的重要组成部分。然而,大多数使用的存储系统都是过时的,并且在不安全的网络上。攻击者经常通过使用勒索软件来利用这些系统漏洞。最近的COVID大流行导致这类攻击急剧增加。如果攻击者能够勒索一个系统,存储在其中的数字证据就会被锁定,不再可用,这给警察和检察官带来了麻烦。为了避免这些攻击破坏数月或数年生成的数字证据,建议使用区块链、RAID网络存储和BitTorrent技术。区块链是由节点组成的公共或私有分类账。每个节点都包含分类账的完整副本,并确保它不被指向链中前一个块的哈希篡改。为了防止区块链变得太大,并随着更多区块被添加到链中而减慢系统速度,将使用RAID和BitTorrent技术来分解已经生成的数字证据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Correlation of Egg counts, Micro-nutrients, and NDVI Distribution for Accurate Tracking of SCN Population Density Detection Supervised Deep Learning Models for Detecting GPS Spoofing Attacks on Unmanned Aerial Vehicles ChatGPT: A Threat Against the CIA Triad of Cyber Security Smart UX-design for Rescue Operations Wearable - A Knowledge Graph Informed Visualization Approach for Information Retrieval in Emergency Situations Comparative Study of Deep Learning LSTM and 1D-CNN Models for Real-time Flood Prediction in Red River of the North, USA
×
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