Does subnetting and port hardening influence human adversarial decisions? An investigation via a HackIT tool

Author:

Uttrani Shashank,Aggarwal Palvi,Dutt Varun

Abstract

Prior research in cyber deception has investigated the effectiveness of the timing of deception on human decisions using simulation tools. However, there exists a gap in the literature on how the availability of subnets and port-hardening influence human decisions to attack a system. We tested the influence of subnets and port-hardening on human attack decisions in a simulated environment using the HackIT tool. Availability of subnets (present/absent) within a network and port-hardening (easy-to-attack/hard-to-attack) were varied across four between-subject conditions (N = 30 in each condition): with-subnet with easy-to-attack, with-subnet with hard-to-attack, without-subnet with easy-to-attack, and without-subnet with hard-to-attack. In with-subnet conditions, 40 systems were connected in a hybrid topology network with ten subnets connected linearly, and each subnet contained four connected systems. In without-subnet conditions, all 40 systems were connected in a bus topology. In hard-to-attack (easy-to-attack) conditions, the probabilities of successfully attacking real systems and honeypots were kept low (high) and high (low), respectively. In an experiment, human participants were randomly assigned to one of the four conditions to attack as many real systems as possible and steal credit card information. Results revealed a significant decrease in the proportion of real system attacks in the availability of subnetting and port hardening within the network. Also, more honeypots were attacked in with-subnet conditions than without-subnet conditions. Moreover, a significantly lower proportion of real systems were attacked in the port-hardened condition. This research highlights the implications of subnetting and port-hardening with honeypots to reduce real system attacks. These findings are relevant in developing advanced intrusion detection systems trained on hackers' behavior.

Funder

Department of Science and Technology, Ministry of Science and Technology, India

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Information Systems,Computer Science (miscellaneous)

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