In this research, we address a critical vulnerability of Federated Learning (FL) in practical applications: their susceptibility to adversarial attacks from malicious participants, which can severely degrade a global model’s performance. We propose MADE-PI, a novel defense mechanism designed to identify and exclude malicious clients early in the training process. Our method introduces a pre-aggregation step where the server evaluates each client update using a Proportional-Integral (PI) score. This score is a unique distance-based metric inspired by the PID controller, where a proportional term P measures the immediate deviation of a client’s update, and an integral term I tracks its behavior over time. By analyzing the distribution of these scores, our technique effectively flags and removes malicious contributions. We validate our approach through an empirical study on three datasets representing various applications of FL, including intelligent transportation, medical image analysis, and handwriting recognition. We concentrate on data poisoning as a highly relevant class of attacks. The results demonstrate that MADE-PI outperforms conventional defenses by providing superior malicious client detection and improving both learning efficiency and final model accuracy.
As collaborative robots (cobots) become available in industry, it is crucial to enable non-coders to program cobots easily. For example, in sorting tasks that may require adjustments over time, workers could easily adjust the cobot’s behavior by utilizing learning from demonstrations (LfD) approaches. This paper presents an LfD approach for enabling a cobot to learn sorting tasks by integrating the Gaussian Mixture Model (GMM), validated in Mujoco for policy training. Quantitative analysis shows that the improved BC-GMM achieves the lowest mean squared error (MSE = 0.00207) and highest R-squared score (R2=0.89), outperforming other methods, such as standard BC, diffusion policy, and implicit behavioral cloning (IBC).
In the era of big data, effective feature selection is critical for improving model performance, reducing computational cost, and enhancing interpretability in high-dimensional datasets. This paper introduces a novel filter-based feature selection algorithm, Class-wise Mean Deviation (CMD), which quantifies the discriminative power of features by measuring the absolute deviation of class-wise means from the global mean. Designed for scalability and real-world application, CMD is computationally efficient and particularly effective in handling the severe class imbalance common in large-scale datasets. We evaluate CMD on two large-scale, highly imbalanced benchmark intrusion detection datasets—CICIDS2017 and CICIDS2019—and compare its performance against established filter methods including Variance Threshold, Pearson Correlation, Mutual Information, and Fisher Score. Experimental results demonstrate that CMD consistently achieves competitive or superior classification performance, even with a minimal number of features. These findings highlight CMD’s potential as a robust and interpretable feature selection technique for cybersecurity and other high-dimensional domains.
Many sophisticated cyberattacks (e.g., advanced persistent threat (APT), supply chain attacks) used command and control (C2) channel to remotely explore and control the compromised systems. As domain name system (DNS) is almost always needed everywhere on the Internet and DNS traffic is allowed by almost all network security systems (e.g., firewall), sophisticated malware have used DNS traffic to disguise their C2 channels.In this paper, we investigate how deep learning techniques can be used to detect DNS based C2 traffic. Specifically, we have analyzed Sliver DNS C2 activities and have identified machine learning features that can effectively distinguish DNS C2 from normal DNS activities. We have built and trained LSTM RNN model and empirically validated the effectiveness of our trained LSTM RNN model with large amount of benign and Sliver DNS C2 records. Our LSTM RNN model is able to achieve 96.889% TPR, 0.001% FPR and 99.98% accuracy in detecting beacon mode DNS C2 activities and 99.34% TPR, 0.001% FPR and 99.99% accuracy in detecting interactive mode DNS C2 activities.
Due to its low cost and non-invasive nature, electroencephalography (EEG) is widely used in the diagnosis of neurological disorders and in monitoring cognitive states, such as the detection of Alzheimer’s disease (AD). While deep learning methods have shown promise in AD detection from EEG, signal noise and variability across channels and recording durations remain significant challenges. In this study, we propose EDLAD, an ensemble deep learning approach for Alzheimer’s detection that integrates multiple deep learning models, each specialized in different types of EEG-derived features. EDLAD leverages ensemble learning to improve predictive performance by combining the outputs of multiple models. Specifically, three deep learning models are independently trained: a Transformer-based model and two dense network classifiers. The Transformer-based model, called Conformer, which integrates the convolutional and transformer layers, is designed to process the original EEG signals. In parallel, features in the time and frequency domains are extracted and fed into two separate neural networks to complement the learning process and improve the robustness of the final predictions. Experiments conducted on two EEG datasets demonstrate that the proposed ensemble framework, EDLAD, consistently improves the classification accuracy at the segment and subject levels across all window lengths. It outperforms both the single Conformer model and a meta-learning ensemble approach. Furthermore, EDLAD surpasses feature-level fusion methods that simply concatenate feature representations from the three models.
