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 Modified Whale Optimization Algorithm (MWOA) feature extraction and Hybrid Long Short Term Memory (LSTM), shown that DDoS attack detection methods can be developed and tested on various datasets. The MWOA technique, which is used to optimize the weights of the LSTM neural network to reduce prediction errors in the hybrid LSTM algorithm, is used. Additionally, MWOA can optimally extract IP packet features and identify DDoS attacks with the support of MWOA-LSTM model. The proposed MWOA-LSTM framework outperforms standard support vector machines (SVM) and Genetic Algorithm (GA) as well as standard methods for detecting attacks based on precision, recall and accuracy measurements.