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Title: NFV-Based Scheme for Effective Protection against Bot Attacks in AI-Enabled IoT
Authors: Memos, Vasileios A.
Psannis, Kostas E.
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
FRASCATI::Engineering and technology::Mechanical engineering
FRASCATI::Engineering and technology::Other engineering and technologies
Keywords: Privacy
Wireless sensor networks
Internet of Things
Network function virtualization
Artificial intelligence
Issue Date: 2022
Publisher: IEEE
Source: IEEE Internet of Things Magazine
Volume: 5
Issue: 1
First Page: 91
Last Page: 95
Abstract: The Internet of Things (IoT) is the upcoming network that aspires to interconnect “things” to each other and to the Internet. Many such “things” like smart devices and wireless sensors are already connected to the Internet, improving human life worldwide. These things can also have artificial intelligence, providing many capabilities to their users. However, such IoT-based devices hide risks since they are usually small devices with constrained resources, and hence do not have sufficient built-in security mechanisms. In addition, the increase of such devices with geometric regression worries network administrators who must take countermeasures to restrict and eliminate attackers who aim to turn the IoT into a Botnet of Things network, using compromised devices as bots to unleash distributed-denial-of-service and man-in-the-middle attacks, or/and spread various types of malware. Thus, they can have unauthorized access and steal very sensitive data from users for malicious purposes. In this article, we highlight the problem caused by the uncontrolled development of insecure IoT-based devices and describe an effective network functions virtualization infrastructure in combination with emerging technologies that could provide smart management and enhanced protection against botnet attacks.
ISSN: 2576-3180
Other Identifiers: 10.1109/IOTM.001.2100175
Appears in Collections:Department of Applied Informatics

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