%0 Journal Article %1 urvashimodiimproved %A Modi, Urvashi %A Jain, Anurag %D 2016 %J Informatics Engineering, an International Journal (IEIJ) %K 99 IDS KDDCUP Machine Network Precision Recall Security WEKA learning %N 2 %P 17 - 29 %R 10.5121/ieij.2016.4203 %T An Improved Method to Detect Intrusion Using Machine Learning Algorithms %U http://airccse.org/journal/ieij/current.html %V 4 %X An intrusion detection system detects various malicious behaviors and abnormal activities that might harm security and trust of computer system. IDS operate either on host or network level via utilizing anomaly detection or misuse detection. Main problem is to correctly detect intruder attack against computer network. The key point of successful detection of intrusion is choice of proper features. To resolve the problems of IDS scheme this research work propose “an improved method to detect intrusion using machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three algorithms J48, J48Graft and Random forest gives much better results than other machine learning algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.

@article{urvashimodiimproved, abstract = {An intrusion detection system detects various malicious behaviors and abnormal activities that might harm security and trust of computer system. IDS operate either on host or network level via utilizing anomaly detection or misuse detection. Main problem is to correctly detect intruder attack against computer network. The key point of successful detection of intrusion is choice of proper features. To resolve the problems of IDS scheme this research work propose “an improved method to detect intrusion using machine learning algorithms”. In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random forest. To identify network based IDS with KDDCUP 99 dataset, experimental results shows that the three algorithms J48, J48Graft and Random forest gives much better results than other machine learning algorithms. We use WEKA to check the accuracy of classified dataset via our proposed method. We have considered all the parameter for computation of result i.e. precision, recall, F – measure and ROC.}, added-at = {2020-05-29T14:42:47.000+0200}, author = {Modi, Urvashi and Jain, Anurag}, biburl = {https://www.bibsonomy.org/bibtex/2b2a005191267b817dd27aa62b12fa219/ieij1}, doi = {10.5121/ieij.2016.4203}, interhash = {02a51b82f9982b077e06800bda83f545}, intrahash = {b2a005191267b817dd27aa62b12fa219}, journal = {Informatics Engineering, an International Journal (IEIJ)}, keywords = {99 IDS KDDCUP Machine Network Precision Recall Security WEKA learning}, month = {June}, number = 2, pages = {17 - 29}, timestamp = {2020-05-29T14:42:47.000+0200}, title = {An Improved Method to Detect Intrusion Using Machine Learning Algorithms}, url = {http://airccse.org/journal/ieij/current.html}, volume = 4, year = 2016 }