Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation

Cha, ByungRae; Lee, Dongseob

doi:10.1007/978-3-540-74827-4_18

Cited by 9 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Artificial Intelligence (AI) approaches and data mining techniques to construct a better IDS. Bayesian approach has been one of the major AI approaches utilized by the researchers in the network security domain [3][4][5][6][7][8][9][10][11][12][13] .…”

Section: Related Work: Researchers Have Utilized Variousmentioning

confidence: 99%

From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

Khor¹,

Ting²,

Amnuaisuk³

2009

American J. of Applied Sciences

View full text Add to dashboard Cite

Problem statement: Implementing a single or multiple classifiers that involve a Bayesian Network (BN) is a rising research interest in network intrusion detection domain. Approach: However, little attention has been given to evaluate the performance of BN classifiers before they could be implemented in a real system. In this research, we proposed a novel approach to select important features by utilizing two selected feature selection algorithms utilizing filter approach. Results: The selected features were further validated by domain experts where extra features were added into the final proposed feature set. We then constructed three types of BN namely, Naive Bayes Classifiers (NBC), Learned BN and Expert-elicited BN by utilizing a standard network intrusion dataset. The performance of each classifier was recorded. We found that there was no difference in overall performance of the BNs and therefore, concluded that the BNs performed equivalently well in detecting network attacks. Conclusion/Recommendations: The results of the study indicated that the BN built using the proposed feature set has less features but the performance was comparable to BNs built using other feature sets generated by the two algorithms

show abstract

Section: Related Work: Researchers Have Utilized Variousmentioning

confidence: 99%

From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

Khor¹,

Ting²,

Amnuaisuk³

2009

American J. of Applied Sciences

View full text Add to dashboard Cite

show abstract

“…On the basis of the detection methods, these detection systems can be classified as misuse-based NIDS (or signature-based NIDS) 3,4 and anomaly based NIDS. 5,6 The former aims to detect an attack by comparing incoming packets with its predefined signatures, which is a kind of description for known threats. The latter aims to find an anomaly by identifying obvious deviation between current events and predefined normal profile, which can be used to represent a normal connection or a normal event.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the rapid development of Internet of Things (IoT), there is a need for detecting malicious network events via NIDSs. On the basis of the detection methods, these detection systems can be classified as misuse‐based NIDS (or signature‐based NIDS ) 3,4 and anomaly based NIDS 5,6 . The former aims to detect an attack by comparing incoming packets with its predefined signatures, which is a kind of description for known threats.…”

Section: Introductionmentioning

confidence: 99%

Enhancing intrusion detection with feature selection and neural network

2021

Int J Intell Syst

View full text Add to dashboard Cite

Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.

show abstract

An Investigation of Scalable Anomaly Detection Techniques for a Large Network of Wi-Fi Hotspots

Machaka

Bagula

2015

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Network-Based Anomaly Intrusion Detection Improvement by Bayesian Network and Indirect Relation

Cited by 9 publications

References 6 publications

From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

From Feature Selection to Building of Bayesian Classifiers: A Network Intrusion Detection Perspective

Enhancing intrusion detection with feature selection and neural network

An Investigation of Scalable Anomaly Detection Techniques for a Large Network of Wi-Fi Hotspots

Contact Info

Product

Resources

About