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Intrusion Detection System Using Deep Belief Network & Particle Swarm Optimization

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Abstract

Securing the services of security such as data integrity, confidentiality and availability is one of the great challenges. Failure to secure above will potentially lead many cyber-attacks. One of the greatest hits for detecting intrusion is an intrusion detection system (IDS) and there are so many advances put forward by many researchers. Even though there exists a large number of Intrusion Detection Systems intruders are still continuing with their job. Another evolving and yet revolutionized strategies is Deep Learning. So, integrating these two systems to create an effective model that could potentially find normal or malicious attacks. In this paper, we classify intrusion using Deep Belief Network and Particle Swarm Optimization into categories like Normal, Probe, DoS, U2R, R2L. The dataset used for applying this model is DARPA 1999 and they are evaluated under various measures. Also, the proposed system is compared with other system like ANFIS, HHO, Fuzzy GNP in which our system outperforms better with greater accuracy of 96.5%.

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Data Availability

The authors declare that no data or material was taken illegally. However, publically available benchmark datasets were taken for implementation.

Code Availability

The authors declare that no exact code has been copied to carry out the research.

Abbreviations

ML:

Machine learning

DL:

Deep learning

SVM:

Support vector machine

KNN:

K nearest neighbor

ANFIS:

Adaptive neuro fuzzy inference system

DBN:

Deep belief network

CNN:

Convolution neural network

RNN:

Recurrent neural network

ANN:

Artificial neural network

HHO:

Harris Hawkins optimization

F-GNP:

Fuzzy graph neural processes

IDS:

Intrusion detection system

NIDS:

Network intrusion detection system

RBM:

Restricted Boltzmann machine

DAE:

Denoising auto encoder

U2R:

User to root

R2L:

Remote to local

DoS:

Denial of service

SNN:

Spiking neural network

MF:

Member function

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Funding

The authors declared that no Funding was received for this work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, 600127, India

    P. J. Sajith & G. Nagarajan

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  1. P. J. Sajith
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  2. G. Nagarajan
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Sajith, P.J., Nagarajan, G. Intrusion Detection System Using Deep Belief Network & Particle Swarm Optimization. Wireless Pers Commun 125, 1385–1403 (2022). https://doi.org/10.1007/s11277-022-09609-x

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