Dynamically enabled defense effectiveness evaluation of a home Internet based on vulnerability analysis and attack layer measurement

Abstract

Smart devices in a home Internet, such as routers and cameras, suffer malicious attacks from hackers on a daily basis. Defenders should change system states dynamically to improve the system's defense ability. To quantify evaluation of defense effectiveness, this paper proposes an improved vulnerability scoring method for home Internets based on the Information Security Technology Security Vulnerability Classification Guide, which calculates a vulnerability score. A higher vulnerability score indicates weaker defense ability. This method only considers defense. In actual system attack and defense, the actions of attackers should also be considered. Therefore, a measurement method for the attack layer based on the Markov chain (MC) is proposed. This method builds a model of an attack layer that can assess whether the defender's dynamic defense is effective by calculating the factor attack layer detection probability P d . To optimize computing process, this paper introduces stochastic Petri nets (SPN) so that the parameters of MC transition matrix are easily gained. This model also obtains the minimum time period Δt, during which the defender must change system states. If the change time period is greater than Δt, attackers increase their probability of hacking into the home Internet system. Finally, the study concludes that if the vulnerability scores are lower after the system states are changed, Δt will become longer, meaning the home Internet system has enhanced defense ability.

Security Analysis of IoT Devices by Using Mobile Computing: A Systematic Literature Review

“…Other methods like game-theorybased vulnerability quantification method is also used for inspecting the security vulnerability of network over legacy methods [105]. Similarly, the vulnerability can also be assessed by using vulnerability scoring like Common Vulnerability Scoring System (CVSS), which is based on scoring from 0 to 10 [106]. The vulnerabilities of IoT network can also be investigated by using multi-attacker multi-target graphical model [25].…”