Izvorni znanstveni članak
https://doi.org/10.1080/00051144.2023.2269646
Evaluated bird swarm optimization based on deep belief network (EBSO-DBN) classification technique for IOT network intrusion detection
A. Biju
; Department of Computer Science and Engineering, Maria College of Engineering and Technology, Attoor, India
*
S. Wilfred Franklin
; Department of Electronics and Communication Engineering, C.S.I Institute of Technology, Thovalai, India
* Dopisni autor.
Sažetak
Because of the recent development of various intrusion detection systems (IDS), which defend
computer networks from security as well as privacy threats. The confidentiality, integrity and
also availability of data may be compromised in the case that IDS prevention efforts fail. The
amount of private, delicate and crucial data travelling over the worldwide network has expanded
tremendously as a result of the recent development of Internet of Things (IoT) devices. Developing a better edge-based feature selection strategy, a deep learning technique for identifying
and blocking malicious traffic, is the goal of intrusion detection. The classification method Evaluated Bird Swarm Optimization based Deep Belief Network (EBSO-DBN) has shown to be the most
successful in this study. A variation of performance criteria have been used to critically assess
deep learning techniques for IDS (accuracy, precision, recall, f-1 score, false alarm rate and detection rate). To ascertain the optimal performance of IDS models, this study focuses on building
an ensemble classifier utilizing the suggested EBSO-DBN classification algorithm with 98.7% of
accuracy, 99.4% of precision and 98.8% of recall.
Ključne riječi
Intrusion detection systems (IDS); deep learning; internet of things (IoT); malicious traffic; evaluated bird swarm optimization based deep belief network (EBSO-DBN)
Hrčak ID:
322955
URI
Datum izdavanja:
29.11.2023.
Posjeta: 0 *