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Found 12 papers, 0 papers with code
usfAD Based Effective Unknown Attack Detection Focused IDS Framework
To address this challenge, we put forth two strategies for semi-supervised learning based IDS where training samples of attacks are not required: 1) training a supervised machine learning model using randomly and uniformly dispersed synthetic attack samples; 2) building a One Class Classification (OCC) model that is trained exclusively on benign network traffic.
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis
With the increased use of network technologies like Internet of Things (IoT) in many real-world applications, new types of cyberattacks have been emerging.
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats
Within this second tier, we also embed a multi-classification mechanism coupled with a clustering algorithm.
Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans
This study aims to present a hybrid model that combines a CNN model's feature extraction capabilities with an LSTM model's detection capabilities.
Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma
Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC).
Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search
Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods.
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs
This blend synthesizes minority instances and eliminates Tomek links, resulting in a balanced dataset that significantly enhances detection accuracy in WSNs.
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities.
Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecture
Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4, 350 Images, achieving remarkable performance with an accuracy of 99. 17%, precision of 99. 13%, recall of 99. 16%, and f1-score of 99. 14%.
An Optimized Ensemble Deep Learning Model For Brain Tumor Classification
Experimentation is conducted on the Figshare Contrast-Enhanced MRI (CE-MRI) brain tumor dataset, comprising 3064 images.
A Dependable Hybrid Machine Learning Model for Network Intrusion Detection
Network intrusion detection systems (NIDSs) play an important role in computer network security.
Machine Learning-based Lung and Colon Cancer Detection using Deep Feature Extraction and Ensemble Learning
The model is evaluated on histopathological (LC25000) lung and colon datasets.
