Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection

COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.

Towards robust autonomous driving systems through adversarial test set generation

Ransomware is malware that encrypts a victim's files and demands payment, typically in cryptocurrencies, to restore access. The global spread of ransomware attacks underscores the need for automatic detection systems. Signature‐based antivirus tools cannot identify novel ransomware strains, while machine learning (ML) and deep learning (DL) models offer promise because they learn data patterns and generalize to new unseen samples. However, the choice of model architecture remains an open question: do classical ML methods suffice or do DL methods bring a considerable performance increase for static malware analysis? In this paper, we benchmark three classic algorithms (Logistic Regression, Random Forest and XGBoost) and two deep architectures (Multi‑Layer Perceptron and TabNet) on the Windows Portable Executable (PE) ransomware detection dataset from Kaggle. We perform comprehensive data preprocessing, 5‑fold stratified cross‑validation, and compute performance metrics like accuracy, precision, recall, F1 score, area under the ROC curve (AUC), and Matthews correlation coefficient (MCC). We further examine the feature importance, plot receiver operating characteristic (ROC) curves, and discuss the computational expense and interpretability of each method. Our experiments show that tree ensemble methods (Random Forest and XGBoost) achieve near‑perfect detection with very low false positives and negatives, outperforming Logistic Regression and MLP. Deep models offer no significant advantages for this tabular data set but with higher computational expense. We discuss the implications of these results for ransomware defense in practice and propose directions for future research.

Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs) are susceptible to adversarial scenarios. In these scenarios, an attacker attempts to manipulate or deceive a machine learning model by providing malicious input resulting in incorrect predictions or decisions, which can have severe consequences in security, healthcare, and finance applications. Failures in the ML algorithm can lead to failures in the application domain and the system to which they provide functionality, which may possess performance requirements, hence the need for software reliability and resilience assessment. Many studies propose enhanced techniques to improve the robustness and resilience of ML algorithms. Yet, very few provide quantitative methods that promote risk assessment or measure progress toward improvements in such systems. This research demonstrates the applicability of software reliability and resilience tools to ML algorithms providing an objective approach to assess reliability and resilience recovery after a degradation from known adversarial attacks. An image classification model using CNNs has been created as the target of two generative adversarial attacks (i) the Fast Gradient Sign Method and (ii) the Projected Gradient Descent. The classifier and adversarial attacks were utilized in a min-max game scenario to replicate incremental learning and adaptive adversarial training, to minimize/maximize the number of failures of the classification algorithm. Model-generated data was collected and prepared in a format suitable for software reliability growth models (SRGM) with and without covariates and resilience models, including failure counts, memory usage, and the intensity of noise added by the attacks. Our results suggest that tools incorporating SRGMs and resilience techniques are suitable for quantifying and predicting the resilience and reliability of ML models and may find practical use in many domains in which these models are applied.

Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review

In recent years, a significant amount of research has focused on analyzing the effectiveness of machine learning (ML) models for malware detection. These approaches have ranged from methods such as decision trees and clustering to more complex approaches like support vector machine (SVM) and deep neural networks. In particular, neural networks have proven to be very effective in detecting complex and advanced malware. This, however, comes with a caveat. Neural networks are notoriously complex. Therefore, the decisions that they make are often just accepted without questioning why the model made that specific decision. The black box characteristic of neural networks has challenged researchers to explore methods to explain black-box models such as SVM and neural networks and their decision-making process. Transparency and explainability give the experts and malware analysts assurance and trustworthiness about the ML models’ decisions. In addition, it helps in generating comprehensive reports that can be used to enhance cyber threat intelligence sharing. As such, this much needed analysis drives our work in this paper to explore the explainability and interpretability of ML models in the field of online malware detection. In this paper, we used the Shapley Additive exPlanations (SHAP) explainability technique to achieve efficient performance in interpreting the outcome of different ML models such as SVM Linear, SVM-RBF (Radial Basis Function), Random Forest (RF), Feed-Forward Neural Net (FFNN), and Convolutional Neural Network (CNN) models trained on an online malware dataset. To explain the output of these models, explainability techniques such as KernalSHAP, TreeSHAP, and DeepSHAP are applied to the obtained results.

Intrusion detection plays a critical role in safeguarding computer networks from malicious activities and unauthorized access. Traditional machine learning (ML) models have been widely employed for intrusion detection, but their effectiveness can be limited due to the complex and evolving nature of network attacks. An efficient Convolutional Neural Network (CNN) that utilizes one-dimensional convolutions, batch normalization, and max pooling layers is designed to capture the attack patterns in network traffic data. By leveraging the inherent feature extraction capabilities of CNNs, the proposed architecture demonstrates improved performance in identifying network intrusions compared to traditional ML models. The effectiveness of the novel CNN architecture is evaluated by conducted comprehensive experiments on two benchmark intrusion detection datasets. The designed CNN performance was compared with four other ML models such as logistic regression, k-nearest neighbors, decision trees, and random forest. The experimental results revealed that the proposed CNN architecture outperformed the traditional ML models with highest accuracy of 97.68% and 97.39% and highest F1-score of 97.68% and 97.40% for. UNSW-NB15 and CICIDS2017 datasets, respectively. The proposed CNN architecture serves as a promising alternative to traditional ML models, offering improved performance in identifying and mitigating network attacks.

Study question Can a machine learning (ML) model, developed using modern neural network architecture produce comparable annotation data; utilisable for algorithmic outcome prediction, to manual time-lapse annotations? Summary answer The model automatically annotated unseen embryos with comparable results to manual methods, generating morphokinetic data to enable comparably predictive outputs from an embryo selection algorithm. What is known already The application of artificial intelligence across healthcare industries, including fertility, is increasing. Several ML models are available that seek to generate or analyse embryo images and morphokinetic data, and to determine embryo viability potential. Along with photographic images, the use of time-lapse in IVF laboratories has amassed numeric data, resulting predominantly from annotated manual assessment of images over time. Embryo annotation practice is variable in quality, can be subjective and is time-consuming; commonly taking several minutes per embryo. The development of rapid, accurate automatic annotation would represent a significant time-saving as well as an increase in reproducibility and accuracy. Study design, size, duration Multicentre quality assured annotation data from 63,383 time-lapse monitored embryos (EmbryoScope®), comprising over 400 million individual images, were used to train a ML model to automatically generate morphokinetic annotations. Data was derived from 8 UK clinics within a cohesive group between 2012-2021. Accuracy was assessed using 900 unseen embryos (with live birth outcome) by comparing the output of an established in-house, prospectively validated embryo selection model when the input was either ML-automated, or manual annotations. Participants/materials, setting, methods Multi-focal plane images were processed on the Azure cloud (Microsoft) and resampled to 300x300 pixels. A Laplacian-based focal stacking algorithm merged frames into a single image. The model consisted of an EfficientNetB4 Convolutional Neural Network classifier to extract features and classify the stage of embryo images. A Temporal Convolutional Network interpreted a time-series of image features; producing annotations from pronuclear fading through to blastocyst. Soft localisation loss function used QA data to integrate annotation subjectivities. Main results and the role of chance The ML model rapidly and automatically generated annotations. Efficacy and comparability of the ML model to automate reliable, utilisable annotations was demonstrated by comparison with manual annotation data and the ML model’s ability to auto-generate annotations which could be used to predict live birth by providing annotation data to an established, validated in house embryo selection model. Live birth-predictive capability was measured, and benchmarked against manual annotation, using the area under the receiver operating characteristic curve (AUC). When tested on time-lapse images, collected from pronuclear fading to full blastulation, representing 900 previously unseen, transferred blastocysts where live birth outcomes were blinded, the in-house developed auto-annotation ML model resulted in an AUC of 0.686 compared with 0.661 for manual annotations, for live birth prediction. Auto annotation using the developed model took only milliseconds to complete per embryo. The developed auto-annotation model, built and tested on large data, is considered suitable for productionisation with the aim of being validated and integrated into an application to support IVF laboratory practice. Limitations, reasons for caution Whilst this model was trained to recognise key morphokinetic events, there are other morphokinetic variables that may be useful in the prediction of live birth and further improve embryo selection, or deselection, ability. Akin to manual interpretation, some embryos may fail to be annotated or need second opinion. Wider implications of the findings There is increasing evidence supporting the application of ML to utilise big data from time-lapse imaging and fertility care generally. Whilst promising benefits to IVF clinics and patients, responsible use of data is required alongside large high-quality datasets, and rigorous validation, to ensure safe and robust applications. Trial registration number N/A

Systematic Review of Machine Learning in Diagnosis of Myeloproliferative Neoplasia

Emotion recognition (ER) is crucial for understanding human behaviours, social interactions, and psychological well-being. Electroencephalography (EEG) has emerged as a promising tool for capturing the neural correlates of emotions. This work is a systematic review of articles in ER using EEG signals. A total of 120 articles from 1041 articles were selected based on PRISMA guidelines using defined inclusion and exclusion criteria, published between 2018 and 2024. This article aims to provide an in-depth understanding of the current landscape of ER from EEG signals utilizing deep learning (DL). This review offers valuable guidance for researchers and practitioners seeking more refined and reliable emotion classification systems. To explore the effectiveness of DL models in EEG-based ER, several potential DL models, such as convolutional neural network, long short-term memory (LSTM), gated recurrent unit (GRU), hybrid bidirectional LSTM (BiLSTM), bidirectional GRU, and advanced DL models such as convolutional recurrent neural network and EEG-Conformer models are applied to two popular datasets, SEED and GAMEEMO, respectively, to depict the full process of ER. Additionally, the performance of DL models is also compared with the performance of basic machine learning (ML) models such as SVM, k-nearest neighbors, logistic regression, and boosting algorithms such as AdaBoost, XGBoost and LightGBM. Through extensive experiments and performance evaluations, the performance of different models when applied to the datasets mentioned above is compared. The accuracy, precision, recall, and F1-scores are analysed to determine the most effective model for EEG-based ER. The findings of this study demonstrate that the performance of hybrid DL models is more efficacious than that of ML models. The best-performing model (BiLSTM) classified the emotions, with an accuracy of 90.54% when applied to the GAMEEMO dataset. This research contributes to the growing body of literature on ER and provides insights into the feasibility of using EEG signals to understand emotional states, and presents a structured roadmap for future exploration. The findings can aid in the development of more accurate and reliable ER systems, which can have wide-ranging applications in psychology, social sciences, and human-computer interactions.

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic data, improving IDS robustness and accuracy against both traditional and adversarial attacks.

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Machine learning (ML) models are able to process complex images, providing state-of-the-art performance in tasks such as image classification and semantic segmentation. These models can be mapped to highly efficient commercial-off-the-shelf (COTS) specialized hardware accelerators, whose reliability should be carefully evaluated before deployment. Unfortunately, given the large number of ML model architectures, possible configurations or input selections, and the numerous COTS accelerator architectures available, exhaustively testing every model-accelerator combination with beam experiments is unfeasible. Additionally, the radiation data obtained with a specific combination can hardly be extended to different configurations. In this paper, we test vision transformer (ViT) and segmentation convolutional neural network (CNN) models, in addition to several ML micro-benchmarks, on the Google Coral Edge TPU at 6 different radiation facilities, investigating particle-, software-, and hardware-dependent reliability behaviors. Our experimental results show that, while the cross section for radiation-induced silent data corruption (SDCs) can be up to 8 orders-of-magnitude higher when testing with high-LET heavy ions compared to atmospheric neutrons, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">characteristics</i> of the SDCs are similar across all types of radiation tested. Instead, the most impactful factors that lead to misclassifications in beam tests are actually the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">model complexity</i> and the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input selection</i>. These results can be leveraged to more efficiently plan and utilize the beam time available in radiation experiments, thus improving the understanding of the fault models affecting the software, while also characterizing the reliability of the underlying hardware accelerator.

In recent years, Machine Learning (ML) models, especially deep learning models, have become commodities. In this context, data centers which hold a lot of data often buy ML models from ML model providers, train them on their data locally and use the trained models to provide intelligent services. Existing work has shown that there is a risk of data leakage, which could cause incalculable consequences. Even under the black-box condition, there are still some attacks that can steal the private data held by data centers, and the Capacity Abuse Attack (CAA) is the state-of-the-art attack method. CAA attackers steal the training data by labeling malicious samples with the data to be stolen. However, the label encodings are usually mapped into other output forms such as categories, and it is impossible for the adversary to know the mapping relationship between the form outputted by the trained model and the label encodings. Without the mapping relationship, CAA becomes invalid. Aiming at the limitation of CAA, this study proposes a novel practical attack method, i.e., Capacity Abuse Attack II (CAAII), which can find the mapping relationship between the output in the arbitrary form returned by the trained model and the values of the stolen data. Experiments are conducted on MNIST, Fashion-MNIST, and CIFAR10 datasets, and experimental results show that no matter what forms are returned by the model, our attack method can always find the mapping relationship and successfully steals the training data.

This research focuses on cataract disease diagnosis and prediction using machine learning (ML) models. It seeks to solve the difficulties associated with predicting cataract illness by fusing medical necessity with technology breakthroughs. The complex problem statement includes issues including the lack of data, the lack of resources, and the requirement for preventative healthcare actions. By recognizing and addressing these issues, the article seeks to establish the groundwork for a strong and reliable cataract prediction paradigm. The study investigates the use of several machine learning models, such as ensemble approaches, decision trees, and neural networks, and assesses how well they predict the occurrence of cataracts. This methodology advances cataract prediction while also providing a more comprehensive investigation of machine learning applications in healthcare. The application of several convolutional neural network (CNN) models and their analysis. It explores the technical details of the models, such as padding, normalization, kernel sizes, and filter counts. By fusing technology breakthroughs with healthcare requirements, the project aims to contribute to the changing field of healthcare innovation by providing a more nuanced understanding of cataract prevalence and a route toward more effective, focused, and compassionate healthcare interventions. The paper discusses the effectiveness of several convolutional neural network (CNN) models for classifying cataract eye images. A comparison is made between the accuracy of multiple models: KNN, Inception V3, Xception, LeNet-CNN, and the proposed SqueezeNet model. This work emphasizes how important it is to choose the right CNN architecture for cataract eye classification and highlights the possibility for improving diagnostic capabilities in clinical situations. The suggested SqueezeNet model achieved the best classification accuracy for cataract eye pictures, demonstrating its applicability in this specific use case. This highlights the importance of choosing the right CNN architecture for cataract eye categorization as well as the possibility of improving diagnostic capabilities in clinical situations