TELKOMNIKA Telecommunication, Computing, Electronics and Control
An ensemble based approach for effective intrusion detection using majority voting

TELKOMNIKA Telecommunication, Computing, Electronics and Control
An ensemble based approach for effective intrusion detection using majority voting

DoS
Ensemble
Intrusion detection system
Majority voting
Multi-layer perceptron
Particle swarm optimization

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.

Alwi M. Bamhdi, Iram Abrar, Faheem Masoodi

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 15, 2020

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