Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

Autor: John Foley, Henry Ochenyi, Naghmeh Moradpoor
Jazyk: angličtina
Rok vydání: 2020
Předmět:
IoT Dataset
Routing protocol
Science (General)
Article Subject
Computer Networks and Communications
Computer science
QA75 Electronic computers. Computer science
02 engineering and technology
Cyber-security
Lossy compression
Network topology
Machine learning
computer.software_genre
Metrics
01 natural sciences
Machine Learning
Q1-390
Vulnerability assessment
Global network
Centre for Distributed Computing
Networking and Security
0202 electrical engineering
electronic engineering
information engineering
T1-995
Combined IoT Attacks
Technology (General)
Wireless network
business.industry
Network Metrics
010401 analytical chemistry
Objective Functions
Information security
Power Metrics
AI and Technologies
0104 chemical sciences
006.3 Artificial intelligence
020201 artificial intelligence & image processing
Artificial intelligence
Networks
business
computer
Information Systems
Zdroj: Security and Communication Networks, Vol 2020 (2020)
ISSN: 1939-0114
1939-0122
DOI: 10.1155/2020/2804291
Popis: One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.
Databáze: OpenAIRE
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