Probabilistic models for evaluating network edge's resistance against scan and foothold attack

Author:

Wang Shuo123ORCID,Pei Qingqi13,Xiao Yang13,Shao Feng2,Yuan Shuai2,Chu Jiang12,Liao Renjie2ORCID

Affiliation:

1. State Key Laboratory of Integrated Services Networks, School of Telecommunication Engineering Xidian University Xi'an China

2. State Key Laboratory of Astronautic Dynamics Xi'an Satellite Control Center Xi'an China

3. Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi'an China

Abstract

AbstractThe threat of Scan and Foothold Attack to the Network Edge (SFANE) is increasing, which greatly affects the application and development of edge computing network architecture. However, existing works focus on the implementation of specific technologies that resist the SFANE but ignore the effectiveness analysis of them. To overcome this limitation, this paper constructs probabilistic models for evaluating network edge's resistance against SFANE. In particular, the attacker models of the SFANE based on the ATT&CK model are first formalized. Afterward, according to the state‐of‐the‐art defense technologies, three different defense strategies are illustrated: no defense, address mutation, and fingerprint decoy. Subsequently, three different probabilistic models are constructed to provide a deeper analysis of the theoretical effect of these strategies on resisting the SFANE. Finally, the experimental results show that the actual defense effect of each strategy almost perfectly follows its probabilistic model.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3