An adaptive intrusion detection and prevention system for Internet of Things

Bakhsh, Sheikh Tahir, Alghamdi, Saleh, Alsemmeari, Rayan A. and Hassan, Syed Raheel (2019) An adaptive intrusion detection and prevention system for Internet of Things. International Journal of Distributed Sensor Networks, 15 (11). ISSN 1550-1477

Preview

PDF (1550147719888109) - Published Version Available under License Creative Commons Attribution. Download (823kB) | Preview

Abstract

The revolution of computer network technologies and telecommunication technologies increases the number of Internet users enormously around the world. Thus, many companies nowadays produce various devices having network chips, each device becomes part of the Internet of Things and can run on the Internet to achieve various services for its users. This led to the increase in security threats and attacks on these devices. Due to the increased number of devices connected to the Internet, the attackers have more opportunities to perform their attacks in such an environment. Therefore, security has become a big challenge more than before. In addition, confidentiality, integrity, and availability are required components to assure the security of Internet of Things. In this article, an adaptive intrusion detection and prevention system is proposed for Internet of Things (IDPIoT) to enhance security along with the growth of the devices connected to the Internet. The proposed IDPIoT enhances the security including host-based and network-based functionality by examining the existing intrusion detection systems. Once the proposed IDPIoT receives the packet, it examines the behavior, the packet is suspected, and it blocks or drops the packet. The main goal is accomplished by implementing one essential part of security, which is intrusion detection and prevention system.

Item Type:	Article
Depositing User:	LivePure Connector
Date Deposited:	30 May 2022 11:30
Last Modified:	01 Sep 2023 03:31
URI:	https://ueaeprints.uea.ac.uk/id/eprint/85241
DOI:	10.1177/1550147719888109

Downloads

Actions (login required)

View Item