Return
Identifying the attack surface for IoT network
DOI:10.1016/j.iot.2020.100162.png)
Abstract
En 中文
For this research, our primary goal is to define an attack surface for networks utilizing the IoT (Internet of Things) devices. The IoT consists of systems of integrated objects, computing devices, digital, or mechanical machines that are given the ability to transmit and receive the data over a network without the need for human interaction. Each of these devices can operate independently within the existing Internet infrastructure. Issues will continue to increase as devices become more prevalent and continuously evolve to counter newer threats and schemes. The attack surface of a network sums up all penetration points, otherwise known as attack vectors. An attacker or an unauthorized user can take advantage of these attack vectors to penetrate and change or extract data from the threat environment. For this research, we define a threat model that allows us to systematically analyze the security solutions to mitigate potential risks from the beginning of the design phase. By designing an IoT architecture and breaking it down into several zones, we focus on each zone to identify any vulnerability or weaknesses within a system that allows unauthorized privileges, as well as any attacks that can target that area. We also investigate the available IoT devices across several domains (e.g., wellness, industrial, home, etc.) to provide a 1:1 and 1:n mapping across devices, vulnerabilities, and potential security threats based on the subjective assessment. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
attack vector
attack surface
vulnerabilities
security threats and attacks
security controls
device-level security
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.6
Papers:
1.9K
Citations:
6.9K
Organization
Cited Papers
A competitive fluorescence quenching-based immunoassay for bisphenol A employing functionalized silica nanoparticles and nanogold
RSC Advances
IF0
Internet of things reference architectures, security and interoperability: A survey
INTERNET OF THINGS
IF7.6
Relationships among Parvalbumin-Immunoreactive Neuron Density, Phase-Locked Gamma Oscillations, and Autistic/Schizophrenic Symptoms in PDGFR-β Knock-Out and Control Mice
PLOS ONE
IF0
Cloud-Assisted IoT-Based SCADA Systems Security: A Review of the State of the Art and Future Challenges
IEEE ACCESS
IF3.6

