Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset

Thavasimani, Karthikayini; Srinath, Nuggehalli Kasturirangan

doi:10.11591/ijece.v12i4.pp4031-4041

Cited by 10 publications

(4 citation statements)

References 28 publications

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“…There are many advantages to using this method, including its simplicity, adaptability, and ability to discover the global optimum solution while avoiding the local optimum [28]. Although the aforementioned list of optimization strategies is driven by hunting, they won't be imitating leadership qualities.…”

Section: Related Workmentioning

confidence: 99%

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Raveendra Reddy Enumula

2024

jes

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Accurate diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI) was identified on an early stage is essential in the healthcare industry to stop degeneration. The Smooth Support Vector Machine (SSVM) model, Principal Component Analysis (PCA), feature extraction, and Magnetic Resonance Imaging (MRI) image prepossessing are the components for the diagnosis of AD is proposed in this research at early stage. To assist in the classifier's training, we proposed a novel Improved Weighed Quantum Lion Optimization (IWQLO). The SSVM parameters are specifically proposed to be optimized using a new Switching delayed Lion Optimization (SLO) algorithm. The IWQLO-SSVM approach was effectively used to classify AD and MCI utilizing MRI scans of the Alzheimer's disease Neuroimaging Initiative (ADNI) database and Outcome and Assessment Information Set (OASIS) database. For six example scenarios, the classification accuracy of our proposed method is acceptable. Testing show that the proposed approach improves the performance measures such as accuracy, precision, specificity, sensitivity and recall for detecting the early stage AD diagnosis.

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Section: Related Workmentioning

confidence: 99%

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Raveendra Reddy Enumula

2024

jes

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“…Furthermore, Al-Shabi and Abuhamdah [47] conducted a study using deep learning methods to discover abnormal behaviors in internet networks. It is worth mentioning that many intelligent methods have been used to increase the information security, see [48]- [52]. Because attacks that use viruses intelligently, a novel study of information security has been conducted [53].…”

Section: Figure 2 Overview Of Grey Hole Attack [19]mentioning

confidence: 99%

Malicious attacks modelling: a prevention approach for ad hoc network security

Talib

Alothman

Mohammed

2023

IJEECS

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As a result of the expansions that have taken place in the field of networking and the increase in the number of users of networks, there have recently been breakthroughs made in the techniques and methods used for network security. In this paper, a virtual private network (VPN) is proposed as a means of providing the necessary level of security for particular connections that span across vast networks. After the network performance metrics such as time delay and throughput have been accomplished, the suggested VPN is recommended for the purpose of assuring network security. In addition, artificial intelligence attack predictors and virtual private networks have been implemented with the purpose of preventing harmful activity within such connections. Using a wide variety of machine learning methods like Random Forests and Nave Bays, malicious assaults of any kind can be identified and thwarted in their tracks. Another technique for anticipating attacks is the use of an artificial neural network, which is a type of system that engages in deep learning and learns the behaviors of attacks while it is being trained so that it can then predict attacks. The results of this study demonstrate that the use of machine learning and artificial intelligence techniques can significantly improve the security and performance of virtual private networks and can effectively identify and prevent malicious attacks on networks.

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“…HPT with the classical method and parameter optimization [13][14][15][16]: The fine-tuning of weights and biases (parameters) can provide useful information about the problem, but their size and initial value rely on HPT. Moreover, the number of parameters in deep neural networks (DNNs) and high dimensional datasets is enormous, and calculating the optimum value of these parameters is complicated, not easily implemented, and requires computational systems with remarkable capabilities.…”

Section: Introductionmentioning

confidence: 99%

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception

Zaferani

Teshnehlab

Khodadadian

et al. 2022

Sensors

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In this work, a method for automatic hyper-parameter tuning of the stacked asymmetric auto-encoder is proposed. In previous work, the deep learning ability to extract personality perception from speech was shown, but hyper-parameter tuning was attained by trial-and-error, which is time-consuming and requires machine learning knowledge. Therefore, obtaining hyper-parameter values is challenging and places limits on deep learning usage. To address this challenge, researchers have applied optimization methods. Although there were successes, the search space is very large due to the large number of deep learning hyper-parameters, which increases the probability of getting stuck in local optima. Researchers have also focused on improving global optimization methods. In this regard, we suggest a novel global optimization method based on the cultural algorithm, multi-island and the concept of parallelism to search this large space smartly. At first, we evaluated our method on three well-known optimization benchmarks and compared the results with recently published papers. Results indicate that the convergence of the proposed method speeds up due to the ability to escape from local optima, and the precision of the results improves dramatically. Afterward, we applied our method to optimize five hyper-parameters of an asymmetric auto-encoder for automatic personality perception. Since inappropriate hyper-parameters lead the network to over-fitting and under-fitting, we used a novel cost function to prevent over-fitting and under-fitting. As observed, the unweighted average recall (accuracy) was improved by 6.52% (9.54%) compared to our previous work and had remarkable outcomes compared to other published personality perception works.

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Hyperparameter optimization using custom genetic algorithm for classification of benign and malicious traffic on internet of things–23 dataset

Cited by 10 publications

References 28 publications

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Development of Improved Weighed Quantum Lion Optimization with Smooth Support Vector Machine for Alzheimer’s Disease

Malicious attacks modelling: a prevention approach for ad hoc network security

Hyper-Parameter Optimization of Stacked Asymmetric Auto-Encoders for Automatic Personality Traits Perception

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