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Found 21 papers, 5 papers with code
Managing ML-Based Application Non-Functional Behavior: A Multi-Model Approach
Our solution goes beyond the state of the art by providing an architectural and methodological approach that continuously guarantees a stable non-functional behavior of ML-based applications, is applicable to different ML models, and is driven by non-functional properties assessed on the models themselves.
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.
Tailoring Machine Learning for Process Mining
Machine learning models are routinely integrated into process mining pipelines to carry out tasks like data transformation, noise reduction, anomaly detection, classification, and prediction.
Rethinking Certification for Trustworthy Machine Learning-Based Applications
Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum.
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user 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.
ModularFed: Leveraging Modularity in Federated Learning Frameworks
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms.
Reward Shaping Using Convolutional Neural Network
Due to (1) the previous success of using message passing for reward shaping; and (2) the CNN planning behavior, we use these messages to train the CNN of VIN-RS.
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)
Speaker Identification from emotional and noisy speech data using learned voice segregation and Speech VGG
Speech signals are subjected to more acoustic interference and emotional factors than other signals.
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.
Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats
Detection of illegal and threatening items in baggage is one of the utmost security concern nowadays.
Towards Federated Learning-Enabled Visible Light Communication in 6G Systems
Nevertheless, concerns pertaining to privacy and communication overhead when sharing raw data of the involved clients with a server constitute major bottlenecks in the implementation of centralized ML techniques.
Selecting Optimal Trace Clustering Pipelines with AutoML
Trace clustering has been extensively used to preprocess event logs.
Using Meta-learning to Recommend Process Discovery Methods
Process discovery methods have obtained remarkable achievements in Process Mining, delivering comprehensible process models to enhance management capabilities.
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.
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.
