Path: Top -> Journal -> Telkomnika -> 2021 -> Vol 19, No 2, April 2021

An ensemble based approach for effective intrusion detection using majority voting

Journal from gdlhub / 2021-02-06 14:39:53
Oleh : Alwi M. Bamhdi, Iram Abrar, Faheem Masoodi, Telkomnika
Dibuat : 2021-02-06, dengan 1 file

Keyword : DoS, ensemble, intrusion detection system, majority voting, multi-layer perceptron, particle swarm optimization
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/18325
Sumber pengambilan dokumen : Web

Of late, Network Security Research is taking center stage given the vulnerability of computing ecosystem with networking systems increasingly falling to hackers. On the network security canvas, Intrusion detection system (IDS) is an essential tool used for timely detection of cyber-attacks. A designated set of reliable safety has been put in place to check any severe damage to the network and the user base. Machine learning (ML) is being frequently used to detect intrusion owing to their understanding of intrusion detection systems in minimizing security threats. However, several single classifiers have their limitation and pose challenges to the development of effective IDS. In this backdrop, an ensemble approach has been proposed in current work to tackle the issues of single classifiers and accordingly, a highly scalable and constructive majority voting-based ensemble model was proposed which can be employed in real-time for successfully scrutinizing the network traffic to proactively warn about the possibility of attacks. By taking into consideration the properties of existing machine learning algorithms, an effective model was developed and accordingly, an accuracy of 99%, 97.2%, 97.2%, and 93.2% were obtained for DoS, Probe, R2L, and U2R attacks and thus, the proposed model is effective for identifying intrusion.

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PropertiNilai Properti
ID Publishergdlhub
OrganisasiTelkomnika
Nama KontakHerti Yani, S.Kom
AlamatJln. Jenderal Sudirman
KotaJambi
DaerahJambi
NegaraIndonesia
Telepon0741-35095
Fax0741-35093
E-mail Administratorelibrarystikom@gmail.com
E-mail CKOelibrarystikom@gmail.com

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