A hybrid method consisting of GA and SVM for intrusion detection system

Aslahi-Shahri, B. M.; Rahmani, Rasoul; Chizari, M.; Maralani, A.; Eslami, M.; Golkar, Mohammad Javad; Ebrahimi, A.

doi:10.1007/s00521-015-1964-2

Cited by 190 publications

(70 citation statements)

References 32 publications

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“…If the selected individuals reach the expected threshold that the distance between the populations is less than the accepted value, the iteration process converges and the algorithm ends. Otherwise, it returns to step 3 and continues to cycle [9,10].…”

Section: Realization Of Svm Prediction Based On Genetic Algorithm Optmentioning

confidence: 99%

Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm

Shuai

Chen

2017

ITM Web Conf.

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Abstract:To take most advantage of the medical data resources from maternal and child health information platform and to improve the medical level, the team bring up a method based on support vector machine (SVM) algorithm which is aimed at predicting blood flow and blood pressure within 2-24 hours after parturition. We cleaned up the extracted data, determine the linear correlation via Pearson correlation coefficient, and utilize the significance to test and justify the relevance of data. Also, genetic algorithm is used to optimize the parameters. Then, we filter out the data with strong correlation coefficient and make predictions through the SVM algorithm. Finally, we determine the effectiveness of the prediction by doing the comparison between predicted results and the real data. The experiments show that, SVM is valid and feasible for the prediction of postpartum hemorrhage and the blood pressure.

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Section: Realization Of Svm Prediction Based On Genetic Algorithm Optmentioning

confidence: 99%

Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm

Shuai

Chen

2017

ITM Web Conf.

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“…One important application of data mining is for fraud detection which especially in the two recent decades many researchers have investigated the deployment of data mining algorithms and techniques and combining or integrating them for intrusion detection systems (e.g. [4], [14]- [16]). For instance, Aslahi Shahri et al [16] proposed a hybrid method of support vector machine and genetic algorithm (GA) and implementing that in intrusion detection.…”

Section: Previous Researchsmentioning

confidence: 99%

“…[4], [14]- [16]). For instance, Aslahi Shahri et al [16] proposed a hybrid method of support vector machine and genetic algorithm (GA) and implementing that in intrusion detection. this algorithm reduces the number of features from 45 to 10 and categorizes them into three priorities using GA algorithm.…”

Section: Previous Researchsmentioning

confidence: 99%

An Improved Intrusion Detection System based on Combined Cluster Centers and Nearest Neighbors

Shapoorifard¹,

Shamsinejad²

2017

IJCA

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In order to make computer systems completely secure, in addition to firewalls and other intrusion protection devices, other systems called intrusion detection systems (IDS) are needed to detect intrusion and provide solutions to counter the intruder if he penetrated through firewall, antivirus and other security devices. Many IDS have been developed based on machine learning techniques. Specifically, advanced detection approaches created by combining or integrating multiple learning techniques have shown better detection performance than general single learning techniques. This paper proposes an improvement for a feature representation approach, namely the cluster center and nearest neighbor (CANN) approach.

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“…Aslahi Shahri in [10] have proposed a wrapper method for feature selection for IDS model. This wrapper model combines the concept of Genetic Algorithm(GA) with Support Vector Machine(SVM).…”

Section: Related Workmentioning

confidence: 99%

A Feature Selection Technique for Intrusion Detection System Based on Iwd and Aco

Haneef¹,

Singh²

2017

ijarcs

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Rapid advancements in the internet technology and its vulnerabilities have led researchers to devise intelligent systems that can provide network security. Intrusion detection systems (IDS) scrutinize all the features to detect intrusive data. Some of the features may be redundant or irrelevant to the detection process, which results in computational complexities and increased training time. To mitigate this problem, a process known as feature selection is used to remove the redundant and irrelevant attributes of the dataset. This paper proposes an intelligent hybrid technique for the purpose of feature selection of KDD CUP'99 dataset using the concepts of metaheuristic optimization. This hybrid approach combines the concepts of Intelligent Water Drops(IWD) and Ant Colony Optimization(ACO)to select features from data. The objective of this work is to optimize the process of feature selection in order to achieve optimal feature subset and reduce the training time.

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A hybrid method consisting of GA and SVM for intrusion detection system

Cited by 190 publications

References 32 publications

Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm

Prediction of Postpartum Hemorrhage Volume of Pregnant Women Based on GA-SVM Algorithm

An Improved Intrusion Detection System based on Combined Cluster Centers and Nearest Neighbors

A Feature Selection Technique for Intrusion Detection System Based on Iwd and Aco

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