Search Results for author:
Found 14 papers, 4 papers with code
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification
This paper presents a reputation-based threat mitigation framework that defends potential security threats in electroencephalogram (EEG) signal classification during model aggregation of Federated Learning.
A Robust Adversary Detection-Deactivation Method for Metaverse-oriented Collaborative Deep Learning
Metaverse is trending to create a digital circumstance that can transfer the real world to an online platform supported by large quantities of real-time interactions.
Explainable Label-flipping Attacks on Human Emotion Assessment System
This paper's main goal is to provide an attacker's point of view on data poisoning assaults that use label-flipping during the training phase of systems that use electroencephalogram (EEG) signals to evaluate human emotion.
Data Poisoning Attacks on EEG Signal-based Risk Assessment Systems
Industrial insider risk assessment using electroencephalogram (EEG) signals has consistently attracted a lot of research attention.
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals
The major aim of this paper is to explain the data poisoning attacks using label-flipping during the training stage of the electroencephalogram (EEG) signal-based human emotion evaluation systems deploying Machine Learning models from the attackers' perspective.
A Late Multi-Modal Fusion Model for Detecting Hybrid Spam E-mail
In recent years, spammers are now trying to obfuscate their intents by introducing hybrid spam e-mail combining both image and text parts, which is more challenging to detect in comparison to e-mails containing text or image only.
Optical Character Recognition
Optical Character Recognition (OCR)
On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach
This huge progress in terms of prediction quality does not however find a counterpart in the security of such models and corresponding predictions, where perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy.
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network
Image spam threat detection has continually been a popular area of research with the internet's phenomenal expansion.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Poisoning Attacks and Defenses on Artificial Intelligence: A Survey
This survey is conducted with a main intention of highlighting the most relevant information related to security vulnerabilities in the context of machine learning (ML) classifiers; more specifically, directed towards training procedures against data poisoning attacks, representing a type of attack that consists of tampering the data samples fed to the model during the training phase, leading to a degradation in the models accuracy during the inference phase.
Deep Learning-Based Arrhythmia Detection Using RR-Interval Framed Electrocardiograms
Deep learning applied to electrocardiogram (ECG) data can be used to achieve personal authentication in biometric security applications, but it has not been widely used to diagnose cardiovascular disorders.
Deep User Identification Model with Multiple Biometrics
Most research has only focused on single modality or a single task, while the combination of input modality or tasks is yet to be investigated.
An Enhanced Machine Learning-based Biometric Authentication System Using RR-Interval Framed Electrocardiograms
We evaluated the performance of the proposed system using a confusion matrix and achieved up to 95% accuracy by compact data analysis.
An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning
We evaluated the performance of the proposed system and found that it could achieve up to the 92 percent identification accuracy.
A Machine Learning Framework for Biometric Authentication using Electrocardiogram
The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality.
