Optimization of Malware Detection and Defense Algorithm Based on Hybrid Convolutional Graph Neural Network

Background: Intensive care units (ICUs) admit high-risk patients whose physiological parameters are continuously monitored to provide timely care. However, traditional ICU monitors are known to generate high rate of false alarms (FAs) resulting in alarm fatigue. In this work, we investigate the use of a hybrid convolutional neural network (CNN) architecture aiming to reduce the burden of FAs. Methods and Results: We retrospectively analyzed 953 independent life threatening alarms from the streaming data of 410 ICU patients. Specifically, we used four channels of electrocardiographic (ECG), arterial blood pressure (BP) and photoplethysmographic (PPG) signals to detect noise in each of the physiological signals, as well as the onset of an arrhythmia. To this end, we developed a hybrid CNN architecture that fuses the information from multiple physiological signals as well as the handcrafted features (Figure 1). Using 5-fold cross validation repeated for 5 times, we obtained an arrhythmia detection accuracy of 87.5% with a standard deviation of 0.5% (Table 1). In practice, the proposed hybrid-CNN algorithm would have suppressed 77.05% of the FA generated by an existing monitoring system, without prior knowledge of the underlying alarm, which is superior to any other published algorithm. Evaluation on the PhysioNet 2015 Challenge database achieved an overall classification accuracy of 93.9%, indicating its efficacy and generalizability. Further, our method remains adaptable to various arrhythmic conditions as well as multiple physiological signals. Conclusions: A hybrid CNN architecture can significantly reduce FA burden and hence improve the quality of care in ICUs

Malware attacks have escalated significantly with an increase of internet users and connected devices. With the rise of various types of malwares released by the hackers, constructing new competitive methods are necessary to identify the advanced malware. However, conventional malware detection struggles to identify new and evolving malware variants accurately because of its dependence on handcrafted features and static-signature based methods. To address this problem, this research proposes convolutional neural network (CNN) based di strategy polar fox optimization algorithm (DSPFOA) for malware detection to fine-tune the CNN parameters effectively which later assists to overcome the limitations of CNN. The model integrates the sine chaotic mapping and Cauchy operator mutation as DSPFOA prevents the model from local optima issue, and extends search space solution, also enhance convergence. This ensures that the CNN learns highly discriminative features which makes the system more accurate and robust in detecting both known and evolving malware variants. The CNN DSPFOA achieves a high accuracy of 99.65 and 99.76% by utilizing BIG2015 and Malimg dataset respectively compared to existing methods like masked self-supervised model with swin transformer (MalSort).

Brain tumors are formed due to the abnormal growth of the cells that get multiplied and become in uncontrollable perspective. Tumors can damage brain cells by pressing down the skull, which consociate begins in, which negatively affects human health. In advanced stages, a brain tumor is a more dangerous infection that cannot be relieved. Brain tumor detection and early prevention are necessary in today's world. An extreme learning machine (ELM) is a widely adopted algorithm in machine learning. It is proposed to use classification models in brain tumor imaging. This classification is based on the techniques implemented: Convolutional Neural Networks(CNN) and Generative Adversarial Networks(GAN). CNN efficiently solves the convex optimization problem and is faster, requiring less human effort. The algorithmic architecture of GAN uses two neural networks, pocking one against the other. These networks are implemented in various fields to classify brain Tumor images. The present study mainly aims to introduce a new proposed classification system of preschool children brain imaging with Hybrid Convolutional Neural Networks and with the techniques of GAN. The proposed technique is compared with the existing hybrid-CNN and hybrid-GAN techniques. The outcomes are encouraging because the loss is deduced, and the accuracy facet increases. The proposed system achieved a training accuracy of 97.8% and a validation accuracy of 89%. The outcomes of the studies show that ELM in the platform of GAN for preschool children brain imaging classification has achieved higher predictive performance than the traditional classification mechanisms in increasingly complex scenarios. Time elapsed for training brain images samples finds inference value for training samples and time elapsed value increased by 28.9855%. Approximation ratio for cost based on probability, finding Approximation ratio for low probability range is increased by 88.1%. The combination of CNN, GAN and hybrid-CNN, hybrid-GAN, and hybrid CNN + GAN, compared with the proposed hybrid system, increased Detection latency for low range learning rate by 3.31%.

Convolutional neural networks (CNNs) have been widely used in image recognition and processing tasks. Memristor-based CNNs accumulate the advantages of emerging memristive devices, such as nanometer critical dimensions, low power consumption, and functional similarity to biological synapses. Most studies on memristor-based CNNs use either software models of memristors for simulation analysis or full hardware CNN realization. Here, we propose a hybrid CNN, consisting of a hardware fixed pre-trained and explainable feature extractor and a trainable software classifier. The hardware part was realized on passive crossbar arrays of memristors based on nanocomposite (Co-Fe-B)x(LiNbO3)100−x structures. The constructed 2-kernel CNN was able to classify the binarized Fashion-MNIST dataset with ~ 84% accuracy. The performance of the hybrid CNN is comparable to the other reported memristor-based systems, while the number of trainable parameters for the hybrid CNN is substantially lower. Moreover, the hybrid CNN is robust to the variations in the memristive characteristics: dispersion of 20% leads to only a 3% accuracy decrease. The obtained results pave the way for the efficient and reliable realization of neural networks based on partially unreliable analog elements.

This study aims to evaluate the effectiveness of the Convolutional Neural Network ( CNN ) method combined with Support Vector Machine ( SVM ) in detecting URLs.Phishing.Phishing is one of the significant cyber threats, where attackers try to trick users into providing sensitive information through fake websites.With the increasing number of phishing attacks , there is a need for effective methods to detect and prevent this threat.In this study, a dataset containing URLs phishing and non-phishing data were used to train the CNN -SVM model .The training process involved feature extraction from URLs using CNN , which is capable of capturing complex patterns in the data, followed by classification using SVM , which is known for its ability to handle high-dimensional data.Testing was conducted across nine different scenarios to evaluate the performance of the model under various conditions.The test results showed that the hybrid CNN -SVM model achieved a precision of 95%, a recall of 92%, and an F1-Score of 93%, with an overall accuracy of 94%.These results indicate that the model is not only effective in detecting URLs phishing , but also has a good balance between precision and recall.This study indicates that the combination of CNN and SVM can be an effective solution for detecting URLs phishing , making a significant contribution to the development of better cyber security systems.

At present there are some worm detection systems, primarily for a single LAN or with hardware router environment, which main use worm propagation characteristics for detection and has high false alarm rate, but it is not applicable for large-scale network for detecting. This paper presents a distributed worm detection technology, which is divided into two parts, client end and the console end program. The system uses the rule-based detection methods to monitor network worms, and the console side manages and coordinates the multiple side work of detecting. Experimental results show that the method can be good applicable for worms conduct surveillance based on a single or multiple local area network and is used for worm alarming, the method has high detection rate and low false alarm rate.

Background: Accurate segmentation and characterization of brain tumors from magnetic resonance imaging (MRI) are paramount for diagnosis, treatment planning, and monitoring. While deep learning models like U-Net have set a high standard for segmentation, they can fail to detect complex, multifocal disease and often lack the tools for in-depth clinical characterization beyond basic volume. Methods: A comprehensive, web-based platform built upon a hybrid Convolutional Neural Network (CNN) and Transformer architecture is presented. An end-to-end workflow is provided by the system from medical image upload (DICOM/NIfTI) to final analysis. It features a dedicated, interactive 3D reconstruction environment with real-time controls for mesh manipulation, lighting, and data export (STL for 3D printing, JSON for reports). All measurements are performed in native medical imaging units (millimeters) to ensure clinical accuracy. The platform also includes a detailed analysis tool for calculating a full suite of morphological and clinical metrics, including an estimated WHO grade. Results: Quantitatively, the Hybrid model achieved a mean Dice coefficient of 0.91 and a mean sensitivity of 0.94 across the test set, outperforming the U-Net (0.86 Dice, 0.88 sensitivity) and a traditional algorithm (0.72 Dice, 0.75 sensitivity). In a representative case of multifocal glioma, the hybrid model identified three distinct tumor foci with a total volume of 67,480 mm³, whereas the U-Net identified only a single mass of 15,140 mm³, representing a 4.4-fold increase in detected tumor burden. These results were visualized and explored in the platform's interactive 3D viewer, which provided real-time statistics and allowed for immediate export of the 3D model. Conclusion: Our work demonstrates a complete platform that not only leverages a state-of-the-art segmentation model but also provides the necessary tools for interactive visualization, analysis, and data dissemination. By seamlessly integrating a high-performance algorithm with a user-centric interface, the system serves as a powerful tool for medical education, clinical training, and reproducible research.

Automatic heart disease detection from human heartbeats is a challenging and intellectual assignment in signal processing because periodically monitoring of the heart beat arrhythmia for patient is an essential task to reduce the death rate due to cardiovascular disease (CVD). In this paper, the focus of research is to design hybrid Convolutional Neural Network (CNN) architecture by making use of Grasshopper Optimization Algorithm (GOA) to classify different types of heart diseases from the ECG signal or human heartbeats. Convolutional Neural Network (CNN) as an artificial intelligence approach is widely used in computer vision-based medical data analysis. However, the traditional CNN cannot be used for classification of heart diseases from the ECG signal because lots of noise or irrelevant data is mixed with signal. So this study utilizes the pre-processing and selection of feature for proper heart diseases classification, where Discrete Wavelet Transform (DWT) is used for the noise reduction as well as segmentation of ECG signal and Grasshopper Optimization Algorithm (GOA) is used for selection of R-peaks features from the extracted feature sets in terms of R-peaks and R-R intervals that help to attain better classification accuracy. For training as well as testing of projected Heartbeats Classification Model (HCM), the Standard MIT-BIH arrhythmia database is utilized with hybrid Convolutional Neural Network (CNN) architecture. The assortment of proper R-peaks and R-R intervals is a major factor and because of the deficiency of apposite pre-processing phases like noise removal, signal decomposition, smoothing and filtering, the uniqueness of extracted feature is less. The experimental outcomes show that the planned HCM is effective for detecting irregular human heartbeats via R-peaks and R-R intervals. When the proposed Heartbeats Classification Model (HCM) was verified on the database, model achieved higher efficiency than other state-of-the-art techniques for 16 heartbeat disease categories and the average classification accuracy is 99.58% with fast and robust responses where the correctly classified heartbeats are 86,005 and misclassified beats is only 108 with 0.42% error rate.

Convolutional neural networks are known to outperform all other neural network models when classifying a wide variety of 2D-shapes. This type of networks supports a massively parallel extraction of low-level features in the processed images. Especially this characteristic is assumed to impact the performance of convolutional networks in character recognition tasks - and in particular when considering scaled, rotated, translated or otherwise deformed patterns. Yet training of convolutional networks is rather time-consuming due to the relatively high complexity of the entire model. To speed-up the training process, we will propose a new variant of convolutional networks - the so-called hybrid convolutional neural network (HCNN). HCNN-networks combine the original idea of LeCun's convolutional networks with the benefits of RBF-like neurons in all the layers and with the winner-takes- all mechanism applied during recall. In the tests done so far in hand-written digit recognition, HCNN proved to be capable of considerably speeding-up the training process while maintaining roughly the same performance of the trained networks like original convolutional networks.

ABSTRACTAnomaly detection is an active research topic in hyperspectral remote sensing and has been applied in many areas including environmental monitoring, urban survey, mineral mapping, and national security. Usually, it makes a detection decision without any prior target information or background information. Several anomaly detection algorithms (e.g. Reed–Xiaoli detector, blocked adaptive computationally efficient outlier nominator and random-selection-based anomaly detector) have been developed, which rely on estimating background information only from a hyperspectral image without considering target information in making a detection decision. These methods may be efficient in general but sometimes with high false alarm rate (FAR). In order to reduce FAR, this study proposes a novel method that incorporates both background and target information, derived from the hyperspectral imagery, into anomaly detection algorithms. The target information is helpful to detect anomalies as outliers. With a scene of real airborne visible infrared imaging spectrometer data, the experimental results demonstrate that the proposed method has produced better detection results and higher time efficiency compared with those using the traditional algorithms that only consider background information.

Due to high spatial and temporal resolution, geostationary meteorological satellite imagery is a valuable source of information on the development of deep convective clouds and related severe weather events. Some methods for automatic deep convection detection from satellite data provide a satisfactory probability of detection for independent datasets, but are characterized by a high false alarm rate. The paper gives a description of an algorithm for automatic detection of deep convective clouds with satellite imagery using gradient boosting, logistic regression, and artificial neural network models. The results of validation of the proposed method using dependent and independent data of ground-based observations for the period 2013-2020 are presented. A low false alarm rate and high probability of detection suggest that the algorithm can be used in the operational mode.

The attack that occurred recently involved the utilization of malicious software, commonly referred to as malware, along with advanced techniques such as machine learning, specifically deep learning, code transformation, and polymorphism. This makes it harder for cyber experts to detect malware using traditional analysis methods. In view of the low accuracy and high false positive rate of traditional malware detection methods, this research proposes a fine-tuned deep learning model with a novel dataset that is compared with ANN, Support Vector Machine (SVM), random forest (RF), K-Nearest Neighbourhood (KNN) classifiers. Converting a malware code into an image could allow users to effectively identify the presence of malware, even if the original code is modified by the creator. This is due to the fact that the attributes of images remain unchanged, allowing for reliable identification. So, researchers used deep learning technology to detect malware, like detecting malaria from red blood cells. The deep learning model found that a more detailed analysis of malware data sets, focusing on RGB and greyscale images, is needed. These data sets currently rely on publicly available data, but the accuracy of the traditional model could be a lot higher. It also produces many false positive results. The main goal is to create a new data set and model using malware images to identify and categorize malware using deep learning without relying on existing image detection and transfer learning models. The researcher adjusted different hyper parameters, like the number of neurons, filters, stride, hidden units, layers, learning rate, batch size, activation function, optimizer, and epochs. Identifying and correcting issues in models, improving their clarity, stability, and fairness, as well as debugging and monitoring them, is a challenging task. To eliminate this obstacle, assess the model's behaviour and performance by employing various methods such as logging, profiling, testing, visualization, and model clarity. To overcome the challenges posed by the electricity shutdown, we utilized Google's cloud-based GPU and Python 3.7 language to conduct the experiment and train the model. Kali Linux is an operating system that can automatically encrypt file systems and has a lower chance of crashing the system due to malware in a virtual sandbox. The researcher used techniques like early stopping and cross-validation to prevent over fitting and assess generalization in addition to monitoring and evaluating the model's behaviour and performance. The researcher used different methods like L1 and L2 regularization, dropout, and batch normalization to improve the model's performance and avoid over fitting. The binary portable executable (PE) malware dataset is collected from Kaggle, Malimg, Virusshare, Malvis, MS Big2015, and VX-underground and finally converted to greyscale and RGB images to create a novel dataset to fill the lack of image dataset. The raw dataset was then rescaled to a 128x128 greyscale and RGB (red, green, blue) image and flattened to 1024-byte vector images input into convolution neural network (CNN) interpolation to extract features for malware detection. The newly acquired dataset is utilized as an input for the innovative DL algorithms in order to create a tailor-made model capable of accurately predicting malware. The findings in this customized CNN model by fine-tuning hyper-parameters with the three-channel RGB dataset outperformed a greyscale dataset with an accuracy of 98.7% and error rate of 1.3% in current malware compared with other artificial neural network (ANN), ML algorithms such as Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and also Random Forest (RF) models. The proposed approach is compatible with all operating systems (Windows, Linux, and Mac) and can identify different types of malwares, such as packed, polymorphic, obfuscated, metamorphic, or variations of a malware family. There are some limitations to the shortage of malware image datasets and computational costs for fine-tuning hyper parameters in the whole model. Furthermore, the proposed approach detects malware and groups it into families without using common techniques like disassembly, decompilation, de-obfuscation, or running malicious code in a virtual environment.

In this paper analysis of modern virtual reality algorithms based on mobile devices was done. As a result, algorithmic shortcomings were identified and the usage of convolutional neural networks was proposed. Within the research the qualitative analysis of modern architectures of convolutional neural networks was carried out and their separate shortcomings at use in systems on the basis of processor architecture аdvanced RISC machine was shown. As a result of this research it was found that to achieve the target accuracy and speed of the system it is important to use a hybrid convolutional neural network, which significantly improves the quality criteria of the system. The optimal structure and parameters for initialization and training of a hybrid convolutional neural network system used for virtual reality are obtained. The optimal training sample was formed and the use of pre-trained hybrid convolutional neural network on another device of аdvanced RISC machine architecture was described.

Deep convolutional neural networks can achieve remarkable results for detecting and recognizing large and medium sized objects in visible band color images. However, the ability to detect small objects has yet to achieve the same level performance. Our focus is on applications that require the accurate detection and localization of small moving objects that are distant from the sensor. State of the art object detection and classification networks (such YOLOv3 &#x005B;1&#x005D; and Mask R-CNN &#x005B;2&#x005D;) are not well suited for finding small moving objects in infra-red imagery, nor do they handle temporal information in video sequences. To overcome the limitations of these methods, we propose the Moving Target Indicator Network (MTINet), a new model specifically designed for detecting small moving targets in clutter. Several versions of the MTINet are presented with different refinements (such as spatial pyramid blocks and improved attention mechanism) to increase the probability of detection and reduce false alarm rates. The MTINet is trained to maximize the <i>target to clutter ratio</i> (TCR) metric, which represents the first use of the TCR loss function for detecting moving objects. The further challenge the MTINet, we also evaluated its performance with simulated sensor movement mimicking effects of image stabilization processes. Finally, we also propose a modification of the Reed-Xiaoli detector &#x005B;3&#x005D; (originally developed for anomaly detection in hyperspectral data) to enable it to detect temporal anomalies caused by moving objects, and refer to it as the temporal-ARX (t-ARX) algorithm. We find that the t-ARX algorithm can achieve better probability of detection at lower false alarm rates, while the MTINet is superior at finding difficult targets at higher false alarm rates. We then show that the combination of the two algorithms achieves the best overall performance.

Patients in Intensive Care Units (ICU) face the threat of decompensation, a rapid decline in health associated with a high risk of death. This study focuses on creating and evaluating machine learning (ML) models to predict decompensation risk in ICU patients. It proposes a novel approach using patient vitals and clinical data within a specified timeframe to forecast decompensation risk sequences. The study implemented and assessed long short-term memory (LSTM) and hybrid convolutional neural network (CNN)-LSTM architectures, along with traditional ML algorithms as baselines. Additionally, it introduced a novel decompensation score based on the predicted risk, validated through principal component analysis (PCA) and k-means analysis for risk stratification. The results showed that, with PPV = 0.80, NPV = 0.96 and AUC-ROC = 0.90, CNN-LSTM had the best performance when predicting decompensation risk sequences. The decompensation score’s effectiveness was also confirmed (PPV = 0.83 and NPV = 0.96). SHAP plots were generated for the overall model and two risk strata, illustrating variations in feature importance and their associations with the predicted risk. Notably, this study represents the first attempt to predict a sequence of decompensation risks rather than single events, a critical advancement given the challenge of early decompensation detection. Predicting a sequence facilitates early detection of increased decompensation risk and pace, potentially leading to saving more lives.