Sampling-Based Machine Learning Models for Intrusion Detection in Imbalanced Dataset

Electronics, Год журнала: 2024, Номер 13(10), С. 1878 - 1878

Опубликована: Май 11, 2024

Cybersecurity is one of the important considerations when adopting IoT devices in smart applications. Even though a huge volume data available, related to attacks are generally significantly smaller proportion. Although machine learning models have been successfully applied for detecting security on applications, their performance affected by problem such imbalance. In this case, prediction model preferable majority class, while predicting minority class poor. To address problems, we apply two oversampling techniques and undersampling balance different categories. verify performance, five models, namely decision tree, multi-layer perception, random forest, XGBoost, CatBoost, used experiments based grid search with 10-fold cross-validation parameter tuning. The results show that both can improve used. Based results, XGBoost SMOTE has best terms accuracy at 75%, weighted average precision 82%, recall F1 score 78%, Matthews correlation coefficient 72%. This indicates technique effective multi-attack under imbalance scenario.

Язык: Английский

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IFSrNet: Multi-Scale IFS Feature-Guided Registration Network Using Multispectral Image-to-Image Translation

Electronics, Год журнала: 2024, Номер 13(12), С. 2240 - 2240

Опубликована: Июнь 7, 2024

Multispectral image registration is the process of aligning spatial regions two images with different distributions. One main challenges it faces to resolve severe inconsistencies between reference and target images. This paper presents a novel multispectral network, Multi-scale Intuitionistic Fuzzy Set Feature-guided Registration Network (IFSrNet), address registration. IFSrNet generates pseudo-infrared from visible using Cycle Generative Adversarial (CycleGAN), which equipped multi-head attention module. An end-to-end network encodes input intuitionistic fuzzification, employs an improved feature descriptor—Intuitionistic Set–Scale-Invariant Feature Transform (IFS-SIFT)—to guide its operation. The results will be presented in direct output. For this task we have also designed specialised loss functions. experiment demonstrate that outperforms existing methods Visible–IR dataset. has potential employed as image-to-image translation paradigm.

Язык: Английский

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Virtual sample generation for small sample learning: a survey, recent developments and future prospects

Neurocomputing, Год журнала: 2024, Номер unknown, С. 128934 - 128934

Опубликована: Ноя. 1, 2024

Язык: Английский

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COMPUTATIONALLY EFFICIENT DEEP FEDERATED LEARNING WITH OPTIMIZED FEATURE SELECTION FOR IOT BOTNET DETECTION

Intelligent Systems with Applications, Год журнала: 2024, Номер unknown, С. 200462 - 200462

Опубликована: Ноя. 1, 2024

Язык: Английский

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