KBB: A hybrid method for intrusion detection

Dubey, Shreya; Dubey, Jigyasu

doi:10.1109/ic4.2015.7375704

Cited by 20 publications

(12 citation statements)

References 5 publications

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“…Shreya and Jigyasu [19] have proposed a supervised machine learning technique method in order to classify the intrusions. The authors have used the naïve bayes (NB) and k-nearest neighbor (KNN) to do such purpose.…”

Section: Related Workmentioning

confidence: 99%

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A hybrid method of genetic algorithm and support vector machine for intrusion detection

Tally¹,

Amintoosi

2021

IJECE

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With the development of web applications nowadays, intrusions represent a crucial aspect in terms of violating the security policies. Intrusions can be defined as a specific change in the normal behavior of the network operations that intended to violate the security policies of a particular network and affect its performance. Recently, several researchers have examined the capabilities of machine learning techniques in terms of detecting intrusions. One of the important issues behind using the machine learning techniques lies on employing proper set of features. Since the literature has shown diversity of feature types, there is a vital demand to apply a feature selection approach in order to identify the most appropriate features for intrusion detection. This study aims to propose a hybrid method of Genetic Algorithm and Support Vector Machine. GA has been as a feature selection in order to select the best features, while SVM has been used as a classification method to categorize the behavior into normal and intrusion based on the selected features from GA. A benchmark dataset of intrusions (NSS-KDD) has been in the experiment. In addition, the proposed method has been compared with the traditional SVM. Results showed that GA has significantly improved the SVM classification by achieving 0.927 of f-measure.

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Section: Related Workmentioning

confidence: 99%

“…The model here can generate statistical rules in order to discriminate the situations that occurred with intrusions. These rules will be used to help the model for classifying new or testing data [19]. However, sometimes it is difficult to acquire a labelled example due to the challenging issue of benchmark availability.…”

Section: Ids Datasetmentioning

confidence: 99%

A hybrid method of genetic algorithm and support vector machine for intrusion detection

Tally¹,

Amintoosi

2021

IJECE

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“…A hybrid technique for Intrusion Detection was proposed by Dubey&Dubey [4] with its basis on K-means Naive-Bayes and Back propagation neural network (KBB). The kmeans which was applied initially was partition-based, unsupervised cluster analysis method.…”

Section: Related Workmentioning

confidence: 99%

Optimized Ensemble Weak Learner Tree Based Network Efficient Intrusion Detection and Alert System using Data Mining

2020

IJITEE

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The modern society accesses various network services through different devices. However, the services afford by the service provider faces various challenges and threats. The services are facing different network threats towards degrading the service performance or the entire network. Number of approaches discussed earlier to restrict the illegal access from malicious users which uses different properties in service level, packet level, user level features. However, they suffer to achieve higher performance in intrusion detection. To improve the performance in intrusion detection an novel tree based ensemble learner algorithm has been proposed in this paper. The method incorporates Random Forest and Random Trees, which are identified as NP complete. The method maintains the list of ensembles which are indexed under trees. At the classification, the Tabu Search algorithm has been used which measures the ensemble class weight (ECW) which has been used to perform classification. According to the result of intrusion detection, an alert has been generated to the administrator. The proposed algorithm improves the performance of intrusion detection.

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“…An example of this is given in Maglaras and Jiang [16] where k-means clustering is used recursively to categorise the outliers detected by the SVM process in order to reduce the number of false positives (in the form of severe alerts). Other similar works that combine clustering with a Bayes classifier [17] or nearest neighbors [18] also exist in the literature.…”

Section: Hierarchical Clusteringmentioning

confidence: 99%

A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes

Stewart¹,

Rosa²,

Μαγλαράς³

et al. 2017

EAI Endorsed Transactions on Industrial Networks and Intelligen

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Industrial Control Systems (ICS) are getting more vulnerable as they become increasingly interconnected with other systems. Industrial Internet of Things(IIoT) will bring new opportunities to business and society, along with new threats and security risks. One major change that ICS will face will be that of the dynamic network topology. Changes in the network architecture will affect the performance of the ICS along with the efficiency of the security mechanisms that are deployed. The current article investigates how changes in the network architecture of a supervisory control and data acquisition (SCADA) system affect the performance of an Intrusion Detection System IDS that is based on the One class Support Vector Machine (OCSVM). Also the article proposes an adaptive mechanism that can cope with such changes and can work in real time situations. The performance of the proposed adaptive IDS is tested using traces from a Hybrid ICS testbed with a dynamic topology.

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KBB: A hybrid method for intrusion detection

Cited by 20 publications

References 5 publications

A hybrid method of genetic algorithm and support vector machine for intrusion detection

A hybrid method of genetic algorithm and support vector machine for intrusion detection

Optimized Ensemble Weak Learner Tree Based Network Efficient Intrusion Detection and Alert System using Data Mining

A Novel Intrusion Detection Mechanism for SCADA systems which Automatically Adapts to Network Topology Changes

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