An Efficient Machine Learning and Deep Belief Network Models for Wireless Intrusion Detection System
Authors: Saheed Yakub Kayode , Baba Usman Ahmad , Orje-Ishegh Terdoo , Longe Olumide Babatope 
Published: October 2022

Wireless Sensor Networks (WSNs) have emerged as a critical component of the Internet of Things (IoT), yet despite their obvious benefits, security challenges persist. As more devices connect to the internet, new cyber assaults join established ones, posing serious concerns to the confidentiality, integrity, and accessibility of data in WSNs. Security in WSNs is a critical and difficult task. Anomaly detection is essential for ensuring the security of WSNs. The detection of abnormal data using a machine learning (ML) algorithm has gained popularity in recent years. Numerous ML classifiers have been employed in WSNs intrusion detection. However, existing research rarely considered feature dimensionality reduction, which is critical for developing a well-performing intrusion detection system (IDS). The purpose of this study is to develop a hybrid solution for intrusion detection in WSNs. For better results, the hybrid technique employed both principal component analysis and the firefly algorithm (PCA + FA) for feature dimensionality reduction. We investigated both ML algorithms (random forest, decision tree, Naïve Bayes) and deep belief networks for intrusion detection in WSNs. The experiment was run on the renowned NSL-KDD dataset in addition to the most recent CICIDS2017 and UNSW-NB15 datasets, to create a stable dataset with a proportionate number of regular traffic and malicious samples. The results demonstrated that the proposed hybrid feature dimensionality reduction techniques PCA + FA-RF, PCA + FA-DT, PCA + FA-NB, and PCA + FA-DBN outperform the individual base models RF, DT, NB, and DBN (without feature dimensionality reduction) in terms of accuracy (ACC), detection rate (DR), and false alarm rate (warning) making the proposed hybrid feature dimensionality reduction a viable option for intrusion detection in WSNs. Additionally, the experimental findings in terms of the training time showed that our proposed models train faster than the single individual models on the UNSW-NB15, NSLKDD, and CICIDS2017. On the NSLKDD single-RF requires approximately 2.18 times higher and single-DBN requires approximately 1.99 times higher training time than PCA + FA-RF and PCA + FA-DBN. For the UNSW-NB15 dataset, the individual single-DT requires approximately 1.10 times higher training time than PCA + FA-DT and PCA + FA-NB on the UNSW-NB15 dataset. Individual single-NB requires approximately 1.08 times higher training time than PCA + FA-DT and PCA + FA-NB. On the CICIDS2017 dataset, our proposed models train faster than single individual models (RF, DT, NB, DBN). Individual single-RF on the CICIDS2017 dataset requires roughly 1.97 times higher training time than PCA + FA-RF and PCA + FA-DT. Individual single-DT takes around 1.20 times higher to train than PCA + FA-RF and PCA + FA-DT. The execution time results revealed our model's capability of detecting intrusions with a great DR, ACC, and FAR (warning) rate. Consequently, indicating its advantage over state-of-the-art methods.