The proposed hybrid deep learning intrusion prediction IoT (HDLIP-IoT) framework.

ABSTRACT: Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of threats. The most common form of attack against IoT is Distributed Denial of Service (DDoS). The growth of preventive processes against DDoS attacks has prompted IoT professionals and security experts to focus on this topic. Due to the increasing prevalence of DDoS attacks, some methods for distinguishing different types of DDoS attacks based on individual network features have become hard to implement. Additionally, monitoring traffic pattern changes and detecting DDoS attacks with accuracy are urgent and necessary. In this paper, using Modifi

SUBMITTER: Fadel MM 

PROVIDER: S-EPMC9337696 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature