Comments on “Traffic Sign Recognition Using Kernel Extreme Learning Machines With Deep Perceptual Features”

Jain, Sukirty; Singhal, Mona; Shukla, Sanyam

doi:10.1109/tits.2018.2850057

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…Compared with traditional neural networks, ELM improves the learning speed while maintaining good generalization capabilities of the network, and has strong nonlinear fitting capabilities, which can effectively reduce the amount of calculation as well as the search space. Based on the above advantages, ELM has been applied in many fields, such as information collection [37], big data application [38], logo recognition [39], language recognition [40]. Kernel-based Extreme Learning Machine [41] combines the kernel function on the basis of ELM.…”

Section: B Kernel-based Extreme Learning Machinementioning

confidence: 99%

Deep Belief Network Integrating Improved Kernel-Based Extreme Learning Machine for Network Intrusion Detection

et al. 2021

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Deep learning has become a research hotspot in the field of network intrusion detection. In order to further improve the detection accuracy and performance, we proposed an intrusion detection model based on improved deep belief network (DBN). Traditional neural network training methods, like Back Propagation (BP), start to train a model with preset parameters such as the randomly initialized weights and thresholds, which may bring some issues, e.g., attracting the model to the local optimal solutions, or requiring a long training period. We use the Kernel-based Extreme Learning Machine (KELM) with the supervised learning ability to replace the BP algorithm in DBN in a bid to ameliorate the situation. Considering the problem of poor classification performance usually caused by randomly initializing kernel parameters with KELM, an enhanced grey wolf optimizer (EGWO) is designed to optimize the parameters of KELM. In order to improve the search ability and optimization ability of the traditional grey wolf optimizer algorithm, a novel optimization strategy combining the inner and outer hunting is introduced. Experiments on KDDCup99, NSL-KDD, UNSW-NB15 and CICIDS2017 datasets show that the proposed DBN-EGWO-KELM algorithm has greater advantages in terms of its accuracy, precision, true positive rate, false positive rate and other evaluation indices compared with BP, RBF, SVM, KELM, LIBSVM, CNN, DBN-KELM and other intrusion detection models, and can effectively meet the requirements of intrusion detection of complex networks.INDEX TERMS Intrusion detection, deep belief network, kernel-based extreme learning machine, grey wolf optimizer.

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