The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Hybridization of Machine Learning Algorithm in Intrusion Detection System
Abstract
In recent years, the field of machine learning grows very fast both on the development of techniques and its application in intrusion detection. The computational complexity of the machine learning algorithms increases rapidly as the number of features in the datasets increases. By choosing the significant features, the number of features in the dataset can be reduced, which is critical to progress the classification accuracy and speed of algorithms. Also, achieving high accuracy and detection rate and lowering false alarm rates are the major challenges in designing an intrusion detection system. The major motivation of this work is to address these issues by hybridizing machine learning and swarm intelligence algorithms for enhancing the performance of intrusion detection system. It also emphasizes applying principal component analysis as feature selection technique on intrusion detection dataset for identifying the most suitable feature subsets which may provide high-quality results in a fast and efficient manner.
Related Content
|
Princy Pappachan, Sreerakuvandana, Mosiur Rahaman.
© 2024.
26 pages.
|
|
Winfred Yaokumah, Charity Y. M. Baidoo, Ebenezer Owusu.
© 2024.
23 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Francesco Marongiu, Domenico Santaniello.
© 2024.
25 pages.
|
|
Suchismita Satapathy.
© 2024.
19 pages.
|
|
Xinyi Gao, Minh Nguyen, Wei Qi Yan.
© 2024.
13 pages.
|
|
Mario Casillo, Francesco Colace, Brij B. Gupta, Angelo Lorusso, Domenico Santaniello, Carmine Valentino.
© 2024.
30 pages.
|
|
Pratyay Das, Amit Kumar Shankar, Ahona Ghosh, Sriparna Saha.
© 2024.
32 pages.
|
|
|