A hybrid Ant Lion Optimization algorithm based lightweight deep learning framework for cyber attack detection in IoT environment

Brij B. Gupta

doi:10.1016/j.compeleceng.2024.109944

A hybrid Ant Lion Optimization algorithm based lightweight deep learning framework for cyber attack detection in IoT environment

2025-03-01

1

PRE

AI

DOI：10.1016/j.compeleceng.2024.109944

Original

Original request for help

Save

Abstract

En 中文

Internet of Things (IoTs) are integral part of Web3, in which they are used for information collecting and sharing. However, the limited storage capacity of IoT decides made them vulnerable to many types of cyber attacks. In this context, we proposed a hybrid deep learning model for the detection of cyber attacks in the IoT environment. The proposed approach used features selection technique for the selection of efficient features and Ant Lion Optimization algorithm for tuning the hyper-parameters. This hybrid approach model train for five epochs and detects the attack traffic with an accuracy of 97%, which makes it efficient and lightweight for IoT applications. The proposed model is also outperformed the standard machine learning and deep learning models.

Keywords:

IoT

DoS attack

Probe attack

CNN

Ant Lion Optimization algorithm

A hybrid Ant Lion Optimization algorithm based lightweight deep learning framework for cyber attack detection in IoT environment

Abstract

Journal

Organization