Antoska-Knights, Vesna and Gacovski, Zoran (2023) Methods for Detection and Prevention of Vulnerabilities in the IoT (Internet of Things) Systems. In: Internet of Things - New Insights. INTECH, London, pp. 1-17. ISBN 978-1-83768-988-0
Full text not available from this repository.Abstract
In this chapter, the problem of detection and prevention systems for Internet of Things attacks is discussed. We start with the term Internet of Things (IoT) that defines the use of intelligently connected devices and systems for data collection via embedded sensors and actuators in physical devices. IoT is omnipresent today and is expected to expand globally in the years ahead. This type of progress will provide services that improve the quality of human life and the productivity of enterprises, while creating the possibility for the so-called “Connected Life.” The goal is to achieve protection against intruders who break into IoT systems to obtain certain sensitive data, gain control, or commit any kind of abuse. Reliability, integrity, and availability represent three different aspects in the field of security that should be achieved in systems such as the Internet of Things. In this work, we present three major areas that can help to mitigate the security risks in IoT systems. Also—two methods for intrusion detection are elaborated—signature-based and anomaly-based models. In the last section of this chapter, we present a real-world example that has already been implemented in reality (Intrusion detection system based on Snort at eggs/poultry farm).
| Item Type: | Book Section |
|---|---|
| Subjects: | Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering Scientific Fields (Frascati) > Natural sciences > Mathematics Scientific Fields (Frascati) > Engineering and Technology > Other engineering and technologies |
| Divisions: | Faculty of Technology and Technical Sciences |
| Depositing User: | Prof. d-r. Vesna Knights |
| Date Deposited: | 12 Dec 2023 08:03 |
| Last Modified: | 12 Dec 2023 08:03 |
| URI: | https://eprints.uklo.edu.mk/id/eprint/9380 |
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