@Article{csse.2023.030630,
AUTHOR = {S. Priya, K. Pradeep Mohan Kumar},
TITLE = {Feature Selection with Deep Reinforcement Learning for Intrusion Detection System},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {46},
YEAR = {2023},
NUMBER = {3},
PAGES = {3339--3353},
URL = {http://www.techscience.com/csse/v46n3/52221},
ISSN = {},
ABSTRACT = {An intrusion detection system (IDS) becomes an important tool for
ensuring security in the network. In recent times, machine learning (ML) and deep
learning (DL) models can be applied for the identification of intrusions over the
network effectively. To resolve the security issues, this paper presents a new
Binary Butterfly Optimization algorithm based on Feature Selection with DRL
technique, called BBOFS-DRL for intrusion detection. The proposed BBOFSDRL model mainly accomplishes the recognition of intrusions in the network.
To attain this, the BBOFS-DRL model initially designs the BBOFS algorithm
based on the traditional butterfly optimization algorithm (BOA) to elect feature
subsets. Besides, DRL model is employed for the proper identification and classification of intrusions that exist in the network. Furthermore, beetle antenna
search (BAS) technique is applied to tune the DRL parameters for enhanced intrusion detection efficiency. For ensuring the superior intrusion detection outcomes
of the BBOFS-DRL model, a wide-ranging experimental analysis is performed
against benchmark dataset. The simulation results reported the supremacy of
the BBOFS-DRL model over its recent state of art approaches.},
DOI = {10.32604/csse.2023.030630}
}