A Network Intrusion Detection Framework based on Bayesian Network using Wrapper Approach
TLDR
This research study presents a wrapper approach for intrusion detection with a superior overall performance and performs better than other leading state-of-the-arts models such as KNN, Boosted DT, Hidden NB and Markov chain.Abstract:
Increasing internet usage and connectivity demands a network intrusion detection system combating cynical network attacks. Data mining therefore is a popular technique used by intrusion detection system to prevent the network attacks and classify the network events as either normal or attack. Our research study presents a wrapper approach for intrusion detection. In this framework Feature selection technique eliminate the irrelevant features to reduce the time complexity and build a better model to predict the result with a greater accuracy and Bayesian network works as a base classifier to predict the types of attack. Our experiment shows that the proposed framework exhibits a superior overall performance in terms of accuracy which is 98.2653 , error rate of 1.73 and keeps the false positive rate at a lower rate of 0.007. Our model performed better than other leading state-of-the-arts models such as KNN, Boosted DT, Hidden NB and Markov chain. The NSL-KDD is used as benchmark data set with Weka library functions in the experimental setup. General Terms Pattern Recognition. Intrusion detection system, Data Miningread more
Citations
More filters
Journal ArticleDOI
CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection
Kurniabudi,Deris Stiawan,Darmawijoyo,Mohammad Yazid Bin Idris,Alwi M. Bamhdi,Rahmat Budiarto +5 more
TL;DR: The experiment results show that the number of relevant and significant features yielded by Information Gain affects significantly the improvement of detection accuracy and execution time.
Journal ArticleDOI
Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection
TL;DR: In this paper, a deep learning-based intrusion detection paradigm for Industrial Internet of Things (IIoT) with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets was proposed.
Journal ArticleDOI
A survey on intrusion detection system: feature selection, model, performance measures, application perspective, challenges, and future research directions
Ankit Thakkar,Ritika Lohiya +1 more
TL;DR: The intrusion detection problem is surveyed by considering algorithms from areas such as ML, DL, and SWEVO for performance evaluation and can serve as a pedestal for research communities and novice researchers in the field of network security for understanding and developing efficient IDS models.
Journal ArticleDOI
Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection
Femi Emmanuel Ayo,Sakinat Oluwabukonla Folorunso,Adebayo Abayomi-Alli,Adebola Olayinka Adekunle,Joseph Bamidele Awotunde +4 more
TL;DR: Results proved that the proposed NIDS based on deep learning model optimized with rule-based hybrid feature selection outperforms other related methods with reduction of false alarm rate, high accuracy rate, reduced training and testing time and is suitable for attack classification in NIDS.
Journal ArticleDOI
Foundations and applications of artificial Intelligence for zero-day and multi-step attack detection
TL;DR: This review proposes a comprehensive framework for addressing the challenge of characterising novel complex threats and relevant counter-measures in the field of intrusion detection, which is typically performed online, and security investigation, performed offline.
References
More filters
Book
Neural networks for pattern recognition
TL;DR: This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition, and is designed as a text, with over 100 exercises, to benefit anyone involved in the fields of neural computation and pattern recognition.
Book ChapterDOI
Neural Networks for Pattern Recognition
Suresh Kothari,Heekuck Oh +1 more
TL;DR: The chapter discusses two important directions of research to improve learning algorithms: the dynamic node generation, which is used by the cascade correlation algorithm; and designing learning algorithms where the choice of parameters is not an issue.
Journal ArticleDOI
Review: Intrusion detection by machine learning: A review
TL;DR: This chapter reviews 55 related studies in the period between 2000 and 2007 focusing on developing single, hybrid, and ensemble classifiers and discusses current achievements and limitations in developing intrusion detection systems by machine learning.
Journal ArticleDOI
A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
TL;DR: The experimental results demonstrate that the proposed hybrid intrusion detection method is better than the conventional methods in terms of the detection rate for both unknown and known attacks while it maintains a low false positive rate.
Journal ArticleDOI
An efficient intrusion detection system based on support vector machines and gradually feature removal method
TL;DR: With the combination of clustering method, ant colony algorithm and support vector machine, an efficient and reliable classifier is developed to judge a network visit to be normal or not.