A Novel Exploit Traffic Traceback Method based on Session Relationship

IF 1.7 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal Pub Date : 2023-04-29 DOI:10.5121/csit.2023.130711
Yajing Liu, Ruijie Cai, Xiaokang Yin, Shengli Liu
{"title":"A Novel Exploit Traffic Traceback Method based on Session Relationship","authors":"Yajing Liu, Ruijie Cai, Xiaokang Yin, Shengli Liu","doi":"10.5121/csit.2023.130711","DOIUrl":null,"url":null,"abstract":"Vulnerability exploitation is the key to obtaining the control authority of the system, posing a significant threat to network security. Therefore, it is necessary to discover exploitation from traffic. The current methods usually only target a single stage with an incomplete causal relationship and depend on the payload content, causing attacker easily avoids detection by encrypting traffic and other means. To solve the above problems, we propose a traffic traceback method of vulnerability exploitation based on session relation. First, we construct the session relationship model using the session correlation of different stages during the exploit. Second, we build a session diagram based on historical traffic. Finally, we traverse the session diagram to find the traffic conforming to the session relationship model. Compared with Blatta, a method detecting early exploit traffic with RNN, the detection rate of our method is increased by 50%, independent of traffic encryption methods.","PeriodicalId":42597,"journal":{"name":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","volume":"27 1","pages":""},"PeriodicalIF":1.7000,"publicationDate":"2023-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ADCAIJ-Advances in Distributed Computing and Artificial Intelligence Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5121/csit.2023.130711","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Vulnerability exploitation is the key to obtaining the control authority of the system, posing a significant threat to network security. Therefore, it is necessary to discover exploitation from traffic. The current methods usually only target a single stage with an incomplete causal relationship and depend on the payload content, causing attacker easily avoids detection by encrypting traffic and other means. To solve the above problems, we propose a traffic traceback method of vulnerability exploitation based on session relation. First, we construct the session relationship model using the session correlation of different stages during the exploit. Second, we build a session diagram based on historical traffic. Finally, we traverse the session diagram to find the traffic conforming to the session relationship model. Compared with Blatta, a method detecting early exploit traffic with RNN, the detection rate of our method is increased by 50%, independent of traffic encryption methods.
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
一种基于会话关系的攻击流量溯源方法
漏洞利用是获取系统控制权限的关键,对网络安全构成重大威胁。因此,有必要从流量中发现利用。目前的方法通常只针对单个阶段,且因果关系不完全,依赖于负载内容,导致攻击者很容易通过加密流量等手段逃避检测。针对上述问题,我们提出了一种基于会话关系的漏洞利用流量溯源方法。首先,利用攻击过程中不同阶段的会话相关性,构建会话关系模型。其次,基于历史流量构建会话图。最后,我们遍历会话图以查找符合会话关系模型的流量。与基于RNN的早期漏洞流量检测方法Blatta相比,该方法的检测率提高了50%,且不受流量加密方法的影响。
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
CiteScore
1.40
自引率
0.00%
发文量
22
审稿时长
4 weeks
期刊最新文献
Enhancing Energy Efficiency in Cluster Based WSN using Grey Wolf Optimization Comparison of Pre-trained vs Custom-trained Word Embedding Models for Word Sense Disambiguation Healthcare Data Collection Using Internet of Things and Blockchain Based Decentralized Data Storage Development of an Extended Medical Diagnostic System for Typhoid and Malaria Fever Comparison of Swarm-based Metaheuristic and Gradient Descent-based Algorithms in Artificial Neural Network Training
×
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