Naive Bayes and deep learning model for wireless intrusion detection systems. (28th May 2021)

• Record Type: Journal Article
• Authors: Rajadurai, Hariharan; Gandhi, Usha Devi
• Is Part Of: International journal of engineering systems modelling and simulation. Volume 12:Number 2/3(2021)
• Page Start: 111
• Page End: 119
• Publication Date: 2021-05-28
• Subjects: intrusion detection system -- IDS -- machine learning -- artificial neural network -- ANN -- naive Bayes -- NSL-KDD dataset; Engineering systems -- Computer simulation -- Periodicals; Engineering systems -- Mathematical models -- Periodicals; 620.0042
• Languages: English
• ISSNs: 1755-9758

Abstract

In recent years, online data is exponentially increasing on day basis and accessing that data un-authentically is a major issue. The intruder or attacker is an individual or team who violates the rules of governess by accessing or modifying the data. To protect the online data, there must be a proper mechanism called intrusion detection system (IDS) to be implemented and followed in a right manner. This paper presents a hybrid model for IDS to detect the various attacks in NSL-KDD dataset. The hybrid model is built with a combination of naive Bayes and deep learning (NB-DL) approaches. The NB-DL is tested with benchmarked datasets, and it has scored high accuracy in detecting the attacks. Nowadays, combining two or more different machine learning techniques is popular as multiple methods performance is averaged or selected the best performance between and so on.