The Improved Network Intrusion Detection Techniques Using the Feature Engineering Approach with Boosting Classifiers DOI Creative Commons
Hari Mohan, Joon Yoo, Saurabh Agarwal

и другие.

Mathematics, Год журнала: 2024, Номер 12(24), С. 3909 - 3909

Опубликована: Дек. 11, 2024

In the domain of cybersecurity, cyber threats targeting network devices are very crucial. Because exponential growth wireless devices, such as smartphones and portable risks becoming increasingly frequent common with emergence new types threats. This makes automatic accurate detection network-based intrusion essential. this work, we propose a system utilizing comprehensive feature engineering approach combined boosting machine-learning (ML) models. A TCP/IP-based dataset 25,192 data samples from different protocols has been utilized in our work. To improve dataset, used preprocessing methods label encoding, correlation analysis, custom iterative encoding. model’s accuracy for prediction, then unique methodology that included novel scaling random forest-based selection techniques. We three conventional models (NB, LR, SVC) four classifiers (CatBoostGBM, LightGBM, HistGradientBoosting, XGBoost) classification. The 10-fold cross-validation were employed to train each model. After an assessment using numerous metrics, best-performing model emerged XGBoost. With mean metric values 99.54 ± 0.0007 accuracy, 99.53 0.0013 precision, 0.001 recall, F1-score 0.0014, XGBoost produced best performance overall. Additionally, showed ROC curve evaluating model, which demonstrated all obtained perfect AUC value one. Our suggested methodologies show effectiveness detecting intrusions, setting stage be real time. method provides strong defensive measure against malicious intrusions into infrastructures while keep varying.

Язык: Английский

GAN-SkipNet: A Solution for Data Imbalance in Cardiac Arrhythmia Detection Using Electrocardiogram Signals from a Benchmark Dataset DOI Creative Commons
Hari Mohan, Joon Yoo, Serhii Dashkevych

и другие.

Mathematics, Год журнала: 2024, Номер 12(17), С. 2693 - 2693

Опубликована: Авг. 29, 2024

Electrocardiography (ECG) plays a pivotal role in monitoring cardiac health, yet the manual analysis of ECG signals is challenging due to complex task identifying and categorizing various waveforms morphologies within data. Additionally, datasets often suffer from significant class imbalance issue, which can lead inaccuracies detecting minority samples. To address these challenges enhance effectiveness efficiency arrhythmia detection imbalanced datasets, this study proposes novel approach. This research leverages MIT-BIH dataset, encompassing total 109,446 beats distributed across five classes following Association for Advancement Medical Instrumentation (AAMI) standard. Given dataset’s inherent imbalance, 1D generative adversarial network (GAN) model introduced, incorporating Bi-LSTM synthetically generate two signal classes, represent mere 0.73% fusion (F) 2.54% supraventricular (S) The generated are rigorously evaluated similarity real data using three key metrics: mean squared error (MSE), structural index (SSIM), Pearson correlation coefficient (r). In addition addressing work presents deep learning models tailored classification: SkipCNN (a convolutional neural with skip connections), SkipCNN+LSTM, SkipCNN+LSTM+Attention mechanisms. further accuracy, test dataset assessed an ensemble model, consistently outperforms individual models. performance evaluation employs standard metrics such as precision, recall, F1-score, along their average, macro weighted average counterparts. Notably, SkipCNN+LSTM emerges most promising, achieving remarkable F1-scores 99.3%, were elevated impressive 99.60% through techniques. Consequently, innovative combination balancing techniques, GAN-SkipNet not only resolves posed by but also provides robust reliable solution detection. stands poised clinical applications, offering potential be deployed hospitals real-time detection, thereby benefiting patients healthcare practitioners alike.

Язык: Английский

Процитировано

5
Next-Generation Diagnostics: The Impact of Synthetic Data Generation on the Detection of Breast Cancer from Ultrasound Imaging DOI Creative Commons
Hari Mohan, Serhii Dashkevych, Joon Yoo

и другие.

Mathematics, Год журнала: 2024, Номер 12(18), С. 2808 - 2808

Опубликована: Сен. 11, 2024

Breast cancer is one of the most lethal and widespread diseases affecting women worldwide. As a result, it necessary to diagnose breast accurately efficiently utilizing cost-effective widely used methods. In this research, we demonstrated that synthetically created high-quality ultrasound data outperformed conventional augmentation strategies for diagnosing using deep learning. We trained deep-learning model EfficientNet-B7 architecture large dataset 3186 images acquired from multiple publicly available sources, as well 10,000 generated generative adversarial networks (StyleGAN3). The was five-fold cross-validation techniques validated four metrics: accuracy, recall, precision, F1 score measure. results showed integrating produced into training set increased classification accuracy 88.72% 92.01% based on score, demonstrating power models expand improve quality datasets in medical-imaging applications. This larger comprising synthetic significantly improved its performance by more than 3% over genuine with common augmentation. Various procedures were also investigated set’s diversity representativeness. research emphasizes relevance modern artificial intelligence machine-learning technologies medical imaging providing an effective strategy categorizing images, which may lead diagnostic optimal treatment options. proposed are highly promising have strong potential future clinical application diagnosis cancer.

Язык: Английский

Процитировано

4
Advanced Segmentation of Gastrointestinal (GI) Cancer Disease Using a Novel U-MaskNet Model DOI Creative Commons

Aditya Pal,

Hari Mohan, Mohamed Ben Haj Frej

и другие.

Life, Год журнала: 2024, Номер 14(11), С. 1488 - 1488

Опубликована: Ноя. 15, 2024

The purpose of this research is to contribute the development approaches for classification and segmentation various gastrointestinal (GI) cancer diseases, such as dyed lifted polyps, resection margins, esophagitis, normal cecum, pylorus, Z line, ulcerative colitis. This relevant essential because current challenges related absence efficient diagnostic tools early diagnostics GI cancers, which are fundamental improving diagnosis these common diseases. To address above challenges, we propose a new hybrid model, U-MaskNet, combination U-Net Mask R-CNN models. Here, utilized pixel-wise instance segmentation, together forming solution classifying segmenting cancer. Kvasir dataset, includes 8000 endoscopic images validate proposed methodology. experimental results clearly demonstrated that novel model provided superior compared other well-known models, DeepLabv3+, FCN, DeepMask, well improved performance state-of-the-art (SOTA) including LeNet-5, AlexNet, VGG-16, ResNet-50, Inception Network. quantitative analysis revealed our outperformed achieving precision 98.85%, recall 98.49%, F1 score 98.68%. Additionally, achieved Dice coefficient 94.35% IoU 89.31%. Consequently, developed increased accuracy reliability in detecting cancer, it was proven can potentially be used process and, consequently, patient care clinical environment. work highlights benefits integrating opening way further medical image segmentation.

Язык: Английский

Процитировано

1
The Improved Network Intrusion Detection Techniques Using the Feature Engineering Approach with Boosting Classifiers DOI Creative Commons
Hari Mohan, Joon Yoo, Saurabh Agarwal

и другие.

Mathematics, Год журнала: 2024, Номер 12(24), С. 3909 - 3909

Опубликована: Дек. 11, 2024

In the domain of cybersecurity, cyber threats targeting network devices are very crucial. Because exponential growth wireless devices, such as smartphones and portable risks becoming increasingly frequent common with emergence new types threats. This makes automatic accurate detection network-based intrusion essential. this work, we propose a system utilizing comprehensive feature engineering approach combined boosting machine-learning (ML) models. A TCP/IP-based dataset 25,192 data samples from different protocols has been utilized in our work. To improve dataset, used preprocessing methods label encoding, correlation analysis, custom iterative encoding. model’s accuracy for prediction, then unique methodology that included novel scaling random forest-based selection techniques. We three conventional models (NB, LR, SVC) four classifiers (CatBoostGBM, LightGBM, HistGradientBoosting, XGBoost) classification. The 10-fold cross-validation were employed to train each model. After an assessment using numerous metrics, best-performing model emerged XGBoost. With mean metric values 99.54 ± 0.0007 accuracy, 99.53 0.0013 precision, 0.001 recall, F1-score 0.0014, XGBoost produced best performance overall. Additionally, showed ROC curve evaluating model, which demonstrated all obtained perfect AUC value one. Our suggested methodologies show effectiveness detecting intrusions, setting stage be real time. method provides strong defensive measure against malicious intrusions into infrastructures while keep varying.

Язык: Английский

Процитировано

1