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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Comparative Analysis of Different Attack on Web Log Data using Machine Learning Technique |
| Authors Name: | Diwakar Prasad Nuniya , Nisha |
| Unique Id: | IJSDR2305056 |
| Published In: | Volume 8 Issue 5, May-2023 |
| Abstract: | It is proposed in this research that a multi-stage filter be designed based on the analysis and distribution of various types of network assaults in web log datasets. An extended GOA algorithm with a decision tree algorithm is used in the first stage of the filter to detect frequent attacks, and an enhanced GOA algorithm with a genetic algorithm is used in the second stage to detect moderate attacks. An upgraded GOA algorithm using Nave Bayes as a base learner has been utilized in the final stage of the filter to detect the rare attacks. Many features are included in benchmark datasets used to test and evaluate intrusion detection systems. These massive datasets, on the other hand, necessitate more computational power and time. Intrusion detection relies heavily on the identification of relevant and irrelevant features in high-dimensional datasets. This research provides a strategy for reducing the number of features in an ensemble in order to better classify web-attacks. Information security policy breaches are known as intrusions. Intrusion detection (ID) is a set of actions for detecting and recognizing suspicious behaviors that make the expedient acceptance of standards of secrecy, quality, consistency, and availability of a computer-based network system more difficult. Using the GOA technique, we describe a new approach to feature selection and classification for the NSL-KDD cup 99 intrusion detection dataset. As a primary goal, it is to reduce the number of features used in training data for intrusion classification. Features are selected and eliminated in supervised learning in order to improve classification accuracy by focusing on the most significant input training features and eliminating those that are less important. Several input feature subsets of the training dataset of NSL-KDD cup 99 dataset were used in the experiment to test the classifier. |
| Keywords: | Intrusion Detection, Ensemble Learning, GA Algorithm, Features Selection, GAO Algorithm |
| Cite Article: | "Comparative Analysis of Different Attack on Web Log Data using Machine Learning Technique", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 5, page no.365 - 374, May-2023, Available :http://www.ijsdr.org/papers/IJSDR2305056.pdf |
| Downloads: | 000337070 |
| Publication Details: | Published Paper ID: IJSDR2305056 Registration ID:206245 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: 365 - 374 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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