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Pushpam, C. Amali
- Performance Analysis Of An Efficient Framework For Intrusion Detection System Using Data Mining Techniques
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Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Soft Computing, Vol 12, No 2 (2022), Pagination: 2583-2587Abstract
In the midst of the COVID-19 epidemic crisis, due to the tremendous development of mobile and internet technologies, the excessive growth in cyber-crime makes networksurity a major concern. As a result, individuals and companies are gradually moving towards the use of Intrusion Detection System (IDS), as it plays a persuasive role in monitoring and detecting the traffic of a network. However, high dimensional data affect the performance of IDS by reducing prediction accuracy, increasing false positive rate and classification time. Hence the focus of this research work is to develop a novel framework by integrating Auto – Bi Level (ABL) Classification with Double Filtering Fine Tuning – Ensemble Hybrid (DFFT-EH) feature selection. The experiments are conducted using NSL- KDD a benchmark intrusion detection dataset and it is proved that the proposed framework performs well with good accuracy, less false positive rate and less classification time when compared with voting ensemble classifier and other existing standard algorithms.Keywords
Auto – Bi Level (ABL) classification, Intrusion Detection System (IDS), Data Mining (DM), Feature Selection, Ensemble
References
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- JAA IDS - Framework Design For An Efficient Intrusion Detection System
Abstract Views :105 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science, Bharathidasan University, IN
1 Department of Computer Science, Bharathidasan University, IN
Source
ICTACT Journal on Communication Technology, Vol 12, No 4 (2021), Pagination: 2573-2576Abstract
Network security has become a very hot research area as its importance is heavily realized in various fields. Various mechanisms and tools are available to support this. But they do not meet the challenges imposed by fast growing technologies. Massive amounts of high dimensional data are one of the challenges. Data with a large number of features is entering and moving around the network. The Intrusion Detection System is a new mechanism that faces this challenge with the support of data mining and feature selection. In data mining, the ensemble is more preferable than a single method. In an ensemble, during the testing phase, all base classifiers are treated equally and individually participate and vote. To take a final decision, some extra effort has to be made. All these increase computation effort and time. To overcome these, this research paper proposes a new framework for intrusion detection systems using the Auto Bi-Level (ABL) classification technique with Double Filtering Fine Tuning–Ensemble Hybrid method.Keywords
Network Security, Features selection, Data Mining, Intrusion Detection System, EnsembleReferences
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