Designing of on line intrusion detection system using rough set theory and Q-learning algorithm

Sengupta, Nandita; Sen, Jonaki; Sil, Jaya; Saha, Moumita

doi:10.1016/j.neucom.2012.12.023

Cited by 41 publications

(19 citation statements)

References 10 publications

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“…From Table 2, it is observed that, even though each filter uses different ranking techniques, some features are common across different filter methods. Using simple majority vote, features 4,29,34,12,39,3,5,6,30,33,38,25, and 23 (indicated in bold) appeared across more than three filter methods; this shows the level of importance these features are to the output class (see Table 3). Table 3 shows the 13 selected features out of the onethird split of the most important features of NSL-KDD dataset using EMFFS method.…”

Section: Pre-processing Datasetmentioning

confidence: 99%

“…Olusola et al [28] proposed a rough set-based feature selection method that selects important features from an input data using KDD '99 dataset. Sengupta et al [29] designed an online intrusion detection system (IDS) using both rough set theory and Q-learning algorithm to achieve a maximum classification algorithm that classifies data as either normal or anomaly using NSL-KDD network traffic data. A fast attribute reduction algorithm based on rough set theory was proposed in [30].…”

Section: Related Workmentioning

confidence: 99%

“…NSL-KDD dataset, an improved version of KDD CUP '99 widely deployed in the literature [26,29,42] for intrusion detection, was used to validate our proposed algorithm. NSL-KDD is a labelled benchmark dataset from KDD CUP '99 to improve its flaws.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Osanaiye

Cai

Choo

et al. 2016

J Wireless Com Network

263

136

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Widespread adoption of cloud computing has increased the attractiveness of such services to cybercriminals. Distributed denial of service (DDoS) attacks targeting the cloud's bandwidth, services and resources to render the cloud unavailable to both cloud providers, and users are a common form of attacks. In recent times, feature selection has been identified as a pre-processing phase in cloud DDoS attack defence which can potentially increase classification accuracy and reduce computational complexity by identifying important features from the original dataset during supervised learning. In this work, we propose an ensemble-based multi-filter feature selection method that combines the output of four filter methods to achieve an optimum selection. We then perform an extensive experimental evaluation of our proposed method using intrusion detection benchmark dataset, NSL-KDD and decision tree classifier. The findings show that our proposed method can effectively reduce the number of features from 41 to 13 and has a high detection rate and classification accuracy when compared to other classification techniques.

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Section: Pre-processing Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Osanaiye

Cai

Choo

et al. 2016

J Wireless Com Network

263

136

View full text Add to dashboard Cite

show abstract

“…In the paper [12], a wide-ranging method has been proposed in developing IDS where RST and Q-learning algorithm are included to provide accommodation real time traffic data for sensing impositions with maximum classification correctness. Rough set theory is applied on discrete data only and so in the effort cut is applied on restricted attributes for discretization.…”

Section: Literature Surveymentioning

confidence: 99%

An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering

Dixit¹

2017

IJCA

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Here in this broadside a novel approach for the Discretization of Nonstop Characteristics for the Classification of various datasets is proposed. The Planned Procedure implemented here works in Two Phases, in the first stage K-means Clustering is applied on the dataset to cluster the data on the basis of classes available in the dataset and second is to classify the Clustered Data using Support Vector Machine Classifier. The various Untried results achieved on different datasets proves that the planned procedure provides less mean number of cuts and reduced mean discretization time and also provides higher accuracy with better Scalability.

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“…1 An IDS is a security tool that monitors all activity on a network and detects any attempts which may destroy confidentiality, integrity or availability of computer networks or systems. 2,3 To have an efficient and effective IDS, patterns of previously observed activity are usually analyzed to determine normal traffic or attack specifications. Such specifications can be used in newly observed patterns for intrusion detection.…”

Section: Introductionmentioning

confidence: 99%

Online Anomaly Detection Based on Support Vector Clustering

Adibi¹,

Shahrabi²

2015

IJCIS

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A two-phase online anomaly detection method based on support vector clustering (SVC) in the presence of non-stationary data is developed in this paper which permits arbitrary-shaped data clusters to be precisely treated. In the first step, offline learning is performed to achieve an appropriate detection model. Then the current model dynamically evolves to match the rapidly changing real-world data. To reduce the dimension of the quadratic programming (QP) problem emerging in the SVC, self-organizing map (SOM) and a replacement mechanism are used to summarize the incoming data. Thus, the proposed method can be efficiently and effectively useable in real time applications. The performance of the proposed method is evaluated by a simulated dataset, three subsets extracted from the KDD Cup 99 dataset, and the keystroke dynamics dataset. Results illustrate capabilities of the proposed method in detection of new attacks as well as normal pattern changes over the time.

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Designing of on line intrusion detection system using rough set theory and Q-learning algorithm

Cited by 41 publications

References 10 publications

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing

An Improved SVM Classifier for Discretization of Attributes using K-Means Clustering

Online Anomaly Detection Based on Support Vector Clustering

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