Abstract
As the technology is advancing so are the data storing practices. Nowadays data is stored online which is the main reason as to why the data is constantly under threat. Therefore there is an urgent need of computer se- curity for securing this confidential data, which is mostly customer personal data which if got leaked will not only pose threat to the customer but also to the organization liable for storing and preserving that data. These unwanted activities are termed as intrusions and the detection of these unwanted activities by constantly monitoring and analysing the system is known as intrusion detection. IDS created using data mining techniques is an effective way of detecting intrusions whose implementation is discussed ahead in this paper. The approach involves building of classification model and hybrid model which are created using classification techniques and, combining both classification and clustering techniques respectively. Classification model can detect known attacks effectively whereas hybrid models can detect unknown or new attacks also. NSL-KDD dataset is used as training dataset which is normalalized and then its feature reduction is done using different techniques. The best feature selection technique among all is chosen by using decision table algorithm. The comparison of the results of different models is done over different performance evaluation parameters. The results show that hybrid models perform better than classification models with improved results as the the data is first preprocessed which makes a classifier more effective.
Keywords:
Data mining, IDS, NSL-KDD, CFS Feature Selection, Feature selection, intrusion detection, U2R attacks, R2L attacks
References
Jonathon Ng, Deepti Joshi, and Shankar M Banik. Applying data mining techniques to intrusion detection. In Information Technology- New Generations (ITNG), 2015 12th International Conference on, pages 800–801. IEEE, 2015.
Varsha Singh, Shubha Puthran, and Avanish Tiwari. Intrusion detec- tion using data mining with correlation. In Convergence in Technology (I2CT), 2017 2nd International Conference for, pages 620–625. IEEE, 2017.
Ming Zhu and ZiLi Huang. Intrusion detection system based on data mining for host log.
Varsha Singh and Shubha Puthran. Intrusion detection system using data mining a review. In Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), 2016 International Confer- ence on, pages 587–592. IEEE, 2016.
Nazmul Shahadat, Imam Hossain, Anisur Rohman, and Nawshi Matin. Experimental analysis of data mining application for intrusion detection with feature reduction. In Electrical, Computer and Commu- nication Engineering (ECCE), International Conference on, pages 209–216. IEEE, 2017.
Amreen Sultana and MA Jabbar. Intelligent network intrusion detec- tion system using data mining techniques. In Applied and Theoretical Computing and Communication Technology (iCATccT), 2016 2nd Interna- tional Conference on, pages 329–333. IEEE, 2016.
Dikshant Gupta, Suhani Singhal, Shamita Malik, and Archana Singh. Network intrusion detection system using various data mining tech- niques. In Research Advances in Integrated Navigation Systems (RAINS), International Conference on, pages 1–6. IEEE, 2016.
Yanjie Zhao. Network intrusion detection system model based on data mining. In Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2016 17th IEEE/ACIS Inter- national Conference on, pages 155–160. IEEE, 2016.
Kathleen Goeschel. Reducing false positives in intrusion detection sys- tems using data-mining techniques utilizing support vector machines, decision trees, and naive bayes for off-line analysis. In SoutheastCon, 2016, pages 1–6. IEEE, 2016.
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