Search alternatives:
wolf optimization » whale optimization (Expand Search), _ optimization (Expand Search)
binary layer » boundary layer (Expand Search), final layer (Expand Search), linear layer (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
layer wolf » layer self (Expand Search), layer mols (Expand Search)
swarm » warm (Expand Search)
wolf optimization » whale optimization (Expand Search), _ optimization (Expand Search)
binary layer » boundary layer (Expand Search), final layer (Expand Search), linear layer (Expand Search)
binary case » binary mask (Expand Search), binary image (Expand Search), primary case (Expand Search)
layer wolf » layer self (Expand Search), layer mols (Expand Search)
swarm » warm (Expand Search)
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21
The architecture of ILSTM.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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22
Parameter setting for LSTM.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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23
LITNET-2020 data splitting approach.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”
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24
Transformation of symbolic features in NSL-KDD.
Published 2023“…The ILSTM was then used to build an efficient intrusion detection system for binary and multi-class classification cases. The proposed algorithm has two phases: phase one involves training a conventional LSTM network to get initial weights, and phase two involves using the hybrid swarm algorithms, CBOA and PSO, to optimize the weights of LSTM to improve the accuracy. …”