A Network Intrusion Detection Framework based on Bayesian Network using Wrapper Approach

Kabir, Reazul; Onik, Abdur Rahman; Samad, Tanvir

doi:10.5120/ijca2017913992

Cited by 28 publications

(9 citation statements)

References 14 publications

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“…So any significant deviation from the assumption will cause a decrease in detection accuracy [36]. Some anomaly detection studies that use Bayesian networks include works by Reazul et al [37] and Ding et al [38].…”

Section: ) Bayes Network (Bn)mentioning

confidence: 99%

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

et al. 2020

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Feature selection (FS) is one of the important tasks of data preprocessing in data analytics. The data with a large number of features will affect the computational complexity, increase a huge amount of resource usage and time consumption for data analytics. The objective of this study is to analyze relevant and significant features of huge network traffic to be used to improve the accuracy of traffic anomaly detection and to decrease its execution time. Information Gain is the most feature selection technique used in Intrusion Detection System (IDS) research. This study uses Information Gain, ranking and grouping the features according to the minimum weight values to select relevant and significant features, and then implements Random Forest (RF), Bayes Net (BN), Random Tree (RT), Naive Bayes (NB) and J48 classifier algorithms in experiments on CICIDS-2017 dataset. 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. Specifically, the Random Forest algorithm has the highest accuracy of 99.86% using the relevant selected features of 22, whereas the J48 classifier algorithm provides an accuracy of 99.87% using 52 relevant selected features with longer execution time.

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Section: ) Bayes Network (Bn)mentioning

confidence: 99%

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

et al. 2020

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“…Feature ranking, also called as feature weighting, assesses individual features and assigns them weights according to their degrees of relevance [40,41], while feature selection (FS) has been evaluated by [42] and [43]. In Feature Ranking algorithms category, a subset of features is often selected from the top of a ranking list.…”

Section: ) Examining Feature Ranking For Feature Selection and Classmentioning

confidence: 99%

An Approach for Optimizing Ensemble Intrusion Detection Systems

et al. 2021

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“…On the general assumption of the behavior of the target system model, the precision of the method is determined, with any notable departure from it is likely to reduce precision in detection. Bayesian networks have been applied in a few anomaly detection studies [22] [25].…”

Section: Bayes Network (Bn)mentioning

confidence: 99%

“…Next the word, Forest represented as a collection of classifiers. The decision tree is different from one to other depends on random selection of the desired attributes corresponds to each node.Number of works has been done related to anomaly detection using random forest [22][24].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

A Hybrid Approach for the Analysis of Feature Selection using Information Gain and BAT Techniques on The Anomaly Detection

Gosu¹,

Priyadarsini²,

Motupalli³

2021

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Every day, millions of people in many institutions communicate with each other on the Internet. The past two decades have witnessed unprecedented levels of Internet use by people around the world. Almost alongside these rapid developments in the internet space, an ever increasing incidence of attacks carried out on the internet has been consistently reported every minute. In such a difficult environment, Anomaly Detection Systems (ADS) play an important role in monitoring and analyzing daily internet activities for security breaches and threats. However, the analytical data routinely generated from computer networks are usually of enormous size and of little use. This creates a major challenge for ADSs, who must examine all the functionality of a certain dataset to identify intrusive patterns. The selection of features is an important factor in modeling anomaly-based intrusion detection systems. An irrelevant characteristic can lead to overfitting which in turn negatively affects the modeling power of classification algorithms. The objective of this study is to analyze and select the most discriminating input characteristics for the construction of efficient and computationally efficient schemes for an ADS. In the first step, a heuristic algorithm called IG-BA is proposed for dimensionality reduction by selecting the optimal subset based on the concept of entropy. Then, the relevant and meaningful features are selected, before implementing Number of Classifiers which includes: (1) An irrelevant feature can lead to overfitting which in turn negatively affects the modeling power of the classification algorithms. Experiment was done on CICIDS-2017 dataset by applying (1) Random Forest (RF), (2) Bayes Network (BN), (3) Naive Bayes (NB), (4) J48 and (5) Random Tree (RT) with results showing better detection precision and faster execution time. The proposed heuristic algorithm outperforms the existing ones as it is more accurate in detection as well as faster. However, Random Forest algorithm emerges as the best classifier for feature selection technique and scores over others by virtue of its accuracy in optimal selection of features.

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A Network Intrusion Detection Framework based on Bayesian Network using Wrapper Approach

Cited by 28 publications

References 14 publications

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

CICIDS-2017 Dataset Feature Analysis With Information Gain for Anomaly Detection

An Approach for Optimizing Ensemble Intrusion Detection Systems

A Hybrid Approach for the Analysis of Feature Selection using Information Gain and BAT Techniques on The Anomaly Detection

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