Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

Natesan, Prathiba; Rajalaxmi, R. R.; Gowrison, G.; Balasubramanie, P.

doi:10.1007/s10766-016-0456-z

Cited by 32 publications

(13 citation statements)

References 29 publications

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“…Natesan et al [15] proposed optimization algorithm for feature selection. The authors proposed Hadoop based parallel Binary Bat algorithm method for intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

“…The (1) feature selection that is applied to dataset features in our model is numTopFeatures method. In experiment, we implement different values of numTopFeatures parameter in ChiSqSelector method, the value of numTopFeatures = (40, 33, 30,20,19,17,15,12,11,10).The numTopFeatures chooses a fixed number of top features according to a Chi-Squared test [16]. The result of this step dataset with 17 features.…”

Section: Feature Selectionmentioning

confidence: 99%

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Intrusion detection model using machine learning algorithm on Big Data environment

et al. 2018

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Introduction Big Data is the data that are difficult to store, manage, and analyze using traditional database and software techniques. Big Data includes high volume and velocity, and also variety of data that needs for new techniques to deal with it. Intrusion detection system (IDS) is hardware or software monitor that analyzes data to detect any attack toward a system or a network. Traditional intrusion detection system techniques make the system more complex and less efficient when dealing with Big Data, because its analysis properties process is complex and take a long time. The long time it takes to analyze the data makes the system prone to harms for some period of time before getting any alert [1, 2]. Therefore, using Big Data tools and techniques to analyze and store data in intrusion detection system can reduce computation and training time. The IDS has three methods for detecting attacks; Signature-based detection, Anomaly-based detection, and Hybrid-based detection. The signature-based detection is designed to detect known attacks by using signatures of those attacks. It is an effective method of detecting known attacks that are preloaded in the IDS database. Therefore, it is often considered to be much more accurate at identifying an intrusion attempt of Abstract Recently, the huge amounts of data and its incremental increase have changed the importance of information security and data analysis systems for Big Data. Intrusion detection system (IDS) is a system that monitors and analyzes data to detect any intrusion in the system or network. High volume, variety and high speed of data generated in the network have made the data analysis process to detect attacks by traditional techniques very difficult. Big Data techniques are used in IDS to deal with Big Data for accurate and efficient data analysis process. This paper introduced Spark-Chi-SVM model for intrusion detection. In this model, we have used ChiSqSelector for feature selection, and built an intrusion detection model by using support vector machine (SVM) classifier on Apache Spark Big Data platform. We used KDD99 to train and test the model. In the experiment, we introduced a comparison between Chi-SVM classifier and Chi-Logistic Regression classifier. The results of the experiment showed that Spark-Chi-SVM model has high performance, reduces the training time and is efficient for Big Data.

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“…Natesan et al [15] proposed optimization algorithm for feature selection. The authors proposed Hadoop based parallel Binary Bat algorithm method for intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

Section: Feature Selectionmentioning

confidence: 99%

Intrusion detection model using machine learning algorithm on Big Data environment

et al. 2018

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“…This Bayesian Classification Algorithm is considered as the recognition calculation that identifies flooding assaults. Natesan P and Rajalakshmi R proposed a parallel figuring model and a nature enlivened element choice strategy, a Hadoop Based Parallel Binary Bat Algorithm machine is proposed for profitable issue assurance and canny portrayal to get multiplied distinguishing proof rate [7]. The MapReduce programming model of Hadoop improvements computational multifaceted nature, the Parallel Binary Bat be counted improves the features decision and parallel Naïve Bayes Algorithm offers canny gathering.…”

Section: Related Workmentioning

confidence: 99%

Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System

Eswaran*,

Balasubramanie,

Jotheeswari

2019

IJITEE

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Web use and digitized information are getting expanded each day. The measure of information created is likewise getting expanded. On the opposite side, the security assaults cause numerous security dangers in the system, sites and Internet. Interruption discovery in a fast system is extremely a hard undertaking. The Hadoop Implementation is utilized to address the previously mentioned test that is distinguishing interruption in a major information condition at constant. To characterize the strange bundle stream, AI methodologies are used. Innocent Bayes does grouping by a vector of highlight esteems produced using some limited set. Choice Tree is another Machine Learning classifier which is likewise an administered learning model. Choice tree is the stream diagram like tree structure. J48 and Naïve Bayes Algorithm are actualized in Hadoop MapReduce Framework for parallel preparing by utilizing the KDDCup Data Corrected Benchmark dataset records. The outcome acquired is 89.9% True Positive rate and 0.04% False Positive rate for Naive Bayes Algorithm and 98.06% True Positive rate and 0.001% False Positive rate for Decision Tree Algorithm.

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“…The problem of network intrusion detection becomes computationally complex as and when classifiers ingest humongous data. As explained further in [13], robust computing environments help towards cost-effective classification. Therefore, authors in [13] used Hadoop based parallel binary bat algorithm to extract the prominent features and applied Naive Bayes to classify the network instances of KDD cup 99 dataset.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…As explained further in [13], robust computing environments help towards cost-effective classification. Therefore, authors in [13] used Hadoop based parallel binary bat algorithm to extract the prominent features and applied Naive Bayes to classify the network instances of KDD cup 99 dataset. Upon selecting only 24 features, the technique proposed in [13] could improve attack detection rate of Probe and Remote2Local (R2L) types in a coherent manner.…”

Section: Introduction and Related Workmentioning

confidence: 99%

A predictive model for network intrusion detection using stacking approach

Rajagopal¹,

Kundapur²,

Hareesha³

2020

IJECE

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Due to the emerging technological advances, cyber-attacks continue to hamper information systems. The changing dimensionality of cyber threat landscape compel security experts to devise novel approaches to address the problem of network intrusion detection. Machine learning algorithms are extensively used to detect intrusions by dint of their remarkable predictive power. This work presents an ensemble approach for network intrusion detection using a concept called Stacking. As per the popular no free lunch theorem of machine learning, employing single classifier for a problem at hand may not be ideal to achieve generalization. Therefore, the proposed work on network intrusion detection emphasizes upon a combinative approach to improve performance. A robust processing paradigm called Graphlab Create, capable of upholding massive data has been used to implement the proposed methodology. Two benchmark datasets like UNSW NB-15 and UGR’ 16 datasets are considered to demonstrate the validity of predictions. Empirical investigation has illustrated that the performance of the proposed approach has been reasonably good. The contribution of the proposed approach lies in its finesse to generate fewer misclassifications pertaining to various attack vectors considered in the study.

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Hadoop Based Parallel Binary Bat Algorithm for Network Intrusion Detection

Cited by 32 publications

References 29 publications

Intrusion detection model using machine learning algorithm on Big Data environment

Intrusion detection model using machine learning algorithm on Big Data environment

Hadoop based Parallel Machine Learning Algorithms for Intrusion Detection System

A predictive model for network intrusion detection using stacking approach

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