Innovative Topologies and Algorithms for Neural Networks

Graziani, Salvatore; Xibilia, Maria Gabriella

doi:10.3390/fi12070117

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…As soon as attacks start to activate, using the weak factors of the nodes, the latter change their tactics of behavior, which can make it more challenging to achieve the expected result of the attack. This type of technology will improve the overall security level of wireless sensor networks [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Zhukabayeva,

Adamova,

Ven-Tsen

et al. 2024

JOIV : Int. J. Inform. Visualization

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The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important. This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.

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Section: Introductionmentioning

confidence: 99%

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Zhukabayeva,

Adamova,

Ven-Tsen

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…In the paper (He, 2017) author proposes an architecture of deep neural network for load forecasting of an hourly load of North China city. Deep neural network architecture is also used by other various authors for load forecasting (Graziani & Xibilia, 2020;Li et al, 2019;Sun et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network

Tiwari

Pal

2021

International Journal of Information Retrieval Research

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The irregularity of Indian grid system increases, with increase in the power demand. The quality of power supplied by the power grid is also poor due to continuous variation in frequency and voltage. To overcome this problem of power deficit, Captive Power Plants installed capacity has grown at a faster rate. Here short term load forecasting of Yara Fertilizers India Private limited installed at Babrala, Uttar Pradesh is performed using multi-layer feed-forward Neural network in MATLAB. The algorithm used is a Levenberg Marquardt algorithm. However, the training and results from ANN are very fast and accurate. Inputs given to the Neural Network are time, ambient air temperature from the compressor, cool air temperature at the compressor and IGV opening. The need, benefits and growth of CPP in India and use of ANN for short term load forecasting of CPP has been explained in detail in the paper.

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Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

et al. 2022

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Accurate forecasts of containerised freight volumes are unquestionably important for port terminal operators to organise port operations and develop business plans. They are also relevant for port authorities, regulators, and governmental agencies dealing with transportation. In a time when deep learning is in the limelight, owing to a consistent strip of success stories, it is natural to apply it to the tasks of forecasting container throughput. Given the number of options, practitioners can benefit from the lessons learned in applying deep learning models to the problem. Coherently, in this work, we devise a number of multivariate predictive models based on deep learning, analysing and assessing their performance to identify the architecture and set of hyperparameters that prove to be better suited to the task, also comparing the quality of the forecasts with seasonal autoregressive integrated moving average models. Furthermore, an innovative representation of seasonality is given by means of an embedding layer that produces a mapping in a latent space, with the parameters of such mapping being tuned using the quality of the predictions. Finally, we present some managerial implications, also putting into evidence the research limitations and future opportunities.

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Innovative Topologies and Algorithms for Neural Networks

Cited by 3 publications

References 12 publications

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network

Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development

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