Heart Disease Prediction using an optimized Extreme Learning Machine with Bacterial Colony optimization

Vigneshvaran, P.; Kathiravan, A. Vijaya

doi:10.1109/icosec54921.2022.9952051

Cited by 5 publications

(1 citation statement)

References 22 publications

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“…The three basic steps of the BCO technique are chemotaxis and communication, elimination and reproduction, and migration. Algorithm 1 shows the process of BCO and it has been used to solve a variety of practical problems including fuzzy clustering [22], feature selection [23], data clustering [24,25], the multiobjective problem [26], scheduling [27], neural network [28] and disease detection [29]. Chemotaxis is constantly accompanied by communication during the whole BCO lifespan.…”

Section: Bacterial Colony Optimization (Bco)mentioning

confidence: 99%

DDoS Attack Detection using Back Propagation Neural Network Optimized by Bacterial Colony Optimization

2023

IJIES

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The on-demand provision of computing resources as services over the internet is known as cloud computing. The distributed denial of service (DDoS) attack is a major security risk that affects cloud services. Because of the computational complexity that must be handled, detecting DDoS attacks is a very difficult operation for cloud computing. The back propagation neural network (BPNN) method is frequently employed for DDoS attack detection due to its great flexibility and straightforward construction. But it has drawbacks such as slow convergence, inconsistencies, and instability during training. In this work, the proposed optimized BPNN uses bacterial colony optimization (BCO) for optimizing the connection weights and biases to enhance the performance of BPNN. The optimized BPNN is developed to identify DDoS attacks in the cloud environment. The performance of the BCO-BPNN detection scheme is assessed using four DDoS attack datasets such as NSL-KDD, ISCXIDS2012, CIC-IDS2017, and UNSW-NB15. Its respective detection accuracy with the NSL-KDD, ISCXIDS2012, CIC-IDS2017, and UNSW-NB15 datasets is 0.9892%, 0.9883%, 0.9341%, and 0.9987%. The results of the studies demonstrate that the suggested BCO-BPNN performs better than existing BPNN variants, conventional BPNN, and support vector machine (SVM) methods.

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