Genetic Algorithm based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection

Srinivasu, Pakkurthi; Avadhani, P. S.

doi:10.1016/j.proeng.2012.06.021

Cited by 23 publications

(9 citation statements)

References 3 publications

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“…Pseudo-Bayesian probability correlation and Self-Organizing maps are used to identify the different kinds of multistep attacks. Shrinivasu et.al, [11] have used Genetic Algorithm weight extraction algorithm to extract and improve weights between the neurons of artificial neural network. This GA-NN based intrusion detection is effectively used to identify the intrusions in network.…”

Section: Literature Surveymentioning

confidence: 99%

Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

Gaikwad,

Swami,

Kolte

2019

IJEAT

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An intrusion detection system is a process which automates analyzing activities in network or a computer system. It is used to detect nasty code, hateful activities, intruders and uninvited communications over the Internet. The general intrusion detection system is struggling with some problems like false positive rate, false negative rate, low classification accuracy and slow speed. Now-a-days, this has turned an attention of many researchers to handle these issues. Recently, ensemble of different base classifier is widely used to implement intrusion detection system. In ensemble method of machine learning, the proper selection of base classifier is a challenging task. In this paper, machine learning ensemble have designed and implemented for the intrusion detection system. The ensemble of Partial Decision Tree and Sequential Minimum optimization algorithm to train support vector machine have used for intrusion detection system. Partial Decision Tree rule learner is simplicity and it generates rules fast. Sequential Minimum optimization algorithm is easy to use and is better scaling with training set size with less computational time. Due to these advantages of both classifiers, they jointly used with different methods of ensemble. We make use of all types of methods of ensemble. The performances of base classifiers have evaluated in term of false positive, accuracy and true positive. Performance results display that proposed majority voting method of ensemble using Partial Decision Tree rule learner and Sequential Minimum optimization algorithm based Support Vector Machine offers highest classification among different ensemble classifiers on training dataset. This method of ensemble exhibits highest true positive and lowest false positive rates. It is also observed that stacking of both PART and SMO exhibits lowest and same classification accuracy on test dataset.

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Section: Literature Surveymentioning

confidence: 99%

Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

Gaikwad,

Swami,

Kolte

2019

IJEAT

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“…In reference (Panda et al, 2012) discussed using a combination of class rather than an intrusion detection system in class. (Srinivasu & Avadhani, 2012) presented a method for an intrusion detection system using genetic algorithms for deriving the weights of the neural network method. (Khan et al, 2007) presented an intrusion detection system using support vector machine and hierarchical clustering.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2019

IAJSE

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By developments in social networks, the number of attacks and intrusions into these networks are increasing in different ways. Hence an intrusion detection system could play an important role in protecting the systems against the hackers. In order to protect computer networks and systems, different solutions introduced which are called intrusion detection system. The purpose of such systems is to protect the computers and networks from prohibited uses, damages and misuse of such systems against internal and external intruders and hackers. Each intrusion detection system may use two different approaches to identify any suspicious (abnormal) activity or intrusion. In order to identify the misuse of systems, intrusion patterns are used; however those approaches used to identify abnormal activities, the normal manner of users are used as the pattern. Decision tree is one of the most famous and oldest methods in data analysis to make categorizing model. In those algorithms based on decision tree, the output is represented as a tree which is made of different states of values. Since the results of them could be interpreted and do not need all input parameters, decision trees are in scope their structures are easy to interpret too. Efficiency of a system strictly depends on selecting the properties. Since by an increase in properties, the computational cost also increases, it seems that design and implementation of systems needs the minimum number of them. The proposed method uses a training set of KDD-Cup99. The proposed method uses three main learning algorithms, SVM, Naive Bayes and J48 decision tree is implemented and evaluated separately. These algorithms are also implemented and evaluated individually as well. The results show the superiority of the proposed method with 97% efficiency using J48 learning algorithm and ADABOOST classification by reducing the dimension IG method.

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“…Jiankun Hu and Xinghuo Yu [14] have implemented an enhanced incremental HMM stochastic process for intrusion detection system. Shrinivasu and P.S.Avadhani [15] have proposed GA-NN based the intrusion detection system. Genetic Algorithm Weight Extraction Algorithm is used to extract and optimize the weights between the neurons of ANN to identify the intrusions effectively.…”

Section: Related Workmentioning

confidence: 99%

Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

Gaikwad¹,

Thool²

2015

2015 International Conference on Computing Communication Control and Automation

114

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Intrusion detection system is widely used to protect and reduce damage to information system. It protects virtual and physical computer networks against threats and vulnerabilities. Presently, machine learning techniques are widely extended to implement effective intrusion detection system. Neural network, statistical models, rule learning, and ensemble methods are some of the kinds of machine learning methods for intrusion detection. Among them, ensemble methods of machine learning are known for good performance in learning process. Investigation of appropriate ensemble method is essential for building effective intrusion detection system. In this paper, a novel intrusion detection technique based on ensemble method of machine learning is proposed. The Bagging method of ensemble with REPTree as base class is used to implement intrusion detection system. The relevant features from NSL_KDD dataset are selected to improve the classification accuracy and reduce the false positive rate. The performance of proposed ensemble method is evaluated in term of classification accuracy, model building time and False Positives. The experimental results show that the Bagging ensemble with REPTree base class exhibits highest classification accuracy. One advantage of using Bagging method is that it takes less time to build the model. The proposed ensemble method provides competitively low false positives compared with other machine learning techniques.

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Genetic Algorithm based Weight Extraction Algorithm for Artificial Neural Network Classifier in Intrusion Detection

Cited by 23 publications

References 3 publications

Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

Untitled

Intrusion Detection System Using Bagging Ensemble Method of Machine Learning

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