Feature selection

Pattern recognition (psychology)

Process engineering

Feature (linguistics)

Intrusion detection system

Algorithm

Data mining

Metaheuristic

Extractor

Machine learning

Engineering

Philosophy

Embedded system

Artificial intelligence

Computer science

Linguistics

Feature extraction

Convolutional neural network

Internet of Things

Selection (genetic algorithm)

Computational Intelligence and Neuroscience

This study proposes a novel framework to improve intrusion detection system (IDS) performance based on the data collected from the Internet of things (IoT) environments. The developed framework relies on deep learning and metaheuristic (MH) optimization algorithms to perform feature extraction and selection. A simple yet effective convolutional neural network (CNN) is implemented as the core feature extractor of the framework to learn better and more relevant representations of the input data in a lower-dimensional space. A new feature selection mechanism is proposed based on a recently developed MH method, called Reptile Search Algorithm (RSA), which is inspired by the hunting behaviors of the crocodiles. The RSA boosts the IDS system performance by selecting only the most important features (an optimal subset of features) from the extracted features using the CNN model. Several datasets, including KDDCup-99, NSL-KDD, CICIDS-2017, and BoT-IoT, were used to assess the IDS system performance. The proposed framework achieved competitive performance in classification metrics compared to other well-known optimization methods applied for feature selection problems.

Intrusion Detection System for IoT Based on Deep Learning and Modified Reptile Search Algorithm