A Robust CNN for Malware Classification against Executable Adversarial Attack

Automated accurate emotion recognition system using rhythm-specific deep convolutional neural network technique with multi-channel EEG signals

Identification of crops is an important topic in the agricultural domain. Hyperspectral remote sensing data are very useful for crop feature extraction and classification. Remote sensing data is an unstructured data and Convolutional Neural Network (CNN) can work on unstructured data efficiently. This paper presents an evaluation of CNN for crop classification using the Indian Pines standard dataset obtained from the AVIRIS sensor and the study area dataset obtained from the EO-1hyperion sensor. Optimized CNN has been tuned by training the model on different parameters. It has been compared with two classification algorithms: Deep Neural Network (DNN) and Convolutional Autoencoder. According to the test results, the proposed optimized CNN model provided better results as compared to the other two methods. CNN has given 97 ± 0.58% overall accuracy for the Indian Pines standard dataset and 78 ± 2.43% for our study area dataset.

The report presents an algorithm for constructing a convolutional hierarchical neural network classifier, which is a modification of the algorithm for constructing hierarchical neural network classifiers suggested before. The original algorithm was designed to exploit intrinsic class hierarchy to build a class tree with a neural network in each node classifying groups of initial classes (in a non-terminal node) or a subset of original classes (in a terminal node). The convolutional modification utilizes convolutional neural networks instead of regular fully connected networks in order to apply the model to image classification tasks. Use of class hierarchy for image classification should reduce the number of adjusted neural network parameters compared to deep convolutional neural networks, and therefore it should reduce training and inference time. In this context the algorithm may be compared with some pruning techniques. The convolutional hierarchical neural network classifier inherits some hyperparameters of a conventional hierarchical neural network classifier, like the activation threshold and the threshold by the share of voting patterns. The goal of this study was to explore different strategies of choosing these hyperparameters. To test these strategies, we used the CIFAR-10 dataset. Also, for demonstration purposes we apply the convolutional hierarchical neural network classifier to the CIFAR-100 dataset.

Social media sentiment analysis through parallel dilated convolutional neural network for smart city applications

Cervical cancer is the fourth most prevalent disease among women. Prompt diagnosis and its management can significantly improve patients’ survival rates. Therefore, routine screening for cervical cancer is of paramount importance. Herein, we explore the potential of a deep learning model to automatically distinguish abnormal cells from normal cells. The ThinPrep cytologic test dataset was collected from the fourth central hospital of Baoding city, China. Based on the dataset, four classification models were developed. The first model was a 10-layer convolutional neural network (CNN). The second model was an advancement of the first model equipped with a spatial pyramid pooling (SPP) layer (CNN + SPP) to treat cell images based on their sizes. Based on the first model, the third model replaced the CNN layers with the inception module (CNN + Inception). However, the fourth model incorporated both the SPP layer and the inception module into the first model (CNN + inception + SPP). The performances of the four models are estimated and compared by using the same testing data and evaluation index. The testing results demonstrated that the fourth model yields the best performance. Moreover, the area under the curve (AUC) for module four was 0.997.

During the last decade, the cybersecurity literature has conferred a high-level role to machine learning as a powerful security paradigm to recognise malicious software in modern anti-malware systems. However, a non-negligible limitation of machine learning methods used to train decision models is that adversarial attacks can easily fool them. Adversarial attacks are attack samples produced by carefully manipulating the samples at the test time to violate the model integrity by causing detection mistakes. In this paper, we analyse the performance of five realistic target-based adversarial attacks, namely Extend, Full DOS, Shift, FGSM padding + slack and GAMMA, against two machine learning models, namely MalConv and LGBM, learned to recognise Windows Portable Executable (PE) malware files. Specifically, MalConv is a Convolutional Neural Network (CNN) model learned from the raw bytes of Windows PE files. LGBM is a Gradient-Boosted Decision Tree model that is learned from features extracted through the static analysis of Windows PE files. Notably, the attack methods and machine learning models considered in this study are state-of-the-art methods broadly used in the machine learning literature for Windows PE malware detection tasks. In addition, we explore the effect of accounting for adversarial attacks on securing machine learning models through the adversarial training strategy. Therefore, the main contributions of this article are as follows: (1) We extend existing machine learning studies that commonly consider small datasets to explore the evasion ability of state-of-the-art Windows PE attack methods by increasing the size of the evaluation dataset. (2) To the best of our knowledge, we are the first to carry out an exploratory study to explain how the considered adversarial attack methods change Windows PE malware to fool an effective decision model. (3) We explore the performance of the adversarial training strategy as a means to secure effective decision models against adversarial Windows PE malware files generated with the considered attack methods. Hence, the study explains how GAMMA can actually be considered the most effective evasion method for the performed comparative analysis. On the other hand, the study shows that the adversarial training strategy can actually help in recognising adversarial PE malware generated with GAMMA by also explaining how it changes model decisions.

Introduction: Accurate and automated assessment of anterior cruciate ligament (ACL) injuries in MR images is essential for athlete healthcare and rapid diagnosis of knee injuries. However, challenges such as the small size of the ligament, variations in MR image quality, and complex anatomical structures complicate the classification process.Methods: In this study, we propose a hierarchical deep learning model for the detection and classification of ACL injuries. The model consists of two main phases: ACL segmentation and injury classification. In the first phase, we employ an encoder-decoder architecture with attention mechanisms to accurately identify the ACL region in MR images, while suppressing background noise. Skip connections are used to preserve spatial details and improve segmentation accuracy. In the second phase, the segmented ACL region is input into a hierarchical convolutional neural network (CNN) for classification. Dense blocks are incorporated to maximize feature reuse, while max-pooling and global average pooling (GAP) layers help to reduce overfitting and improve feature extraction.Results: The proposed method was evaluated on a knee MRI dataset and compared with other state-of-the-art approaches. Our model demonstrated high accuracy in both segmentation and classification tasks, owing to the integration of attention mechanisms and hierarchical feature extraction.Conclusion: This approach offers a robust solution for the automated assessment of ACL injuries, providing clinicians and sports medicine specialists with a reliable tool for more efficient and accurate diagnosis.

White blood cells (WBCs) are the most diverse types of cells observed in the healing process of injured skeletal muscles. In the recovery process, WBCs exhibit a dynamic cellular response and undergo multiple changes of the protein expression. The progress of healing can be analyzed by the number of WBCs or by the number of specific proteins observed in light microscopy images obtained at different time points after injury. We propose a deep learning quantification and analysis system called DeepQuantify to analyze WBCs in light microscopy images of uninjured and injured muscles of female mice. The DeepQuantify system features in segmentation using the localized iterative Otsu's thresholding method, masking postprocessing, and classification of WBCs with a convolutional neural network (CNN) classifier to achieve a high accuracy and a low manpower cost. The proposed two-layer CNN classifier designed based on the optimization hypothesis is evaluated and compared with other CNN classifiers. The DeepQuantify system adopting these CNN classifiers is evaluated for quantifying CD68-positive macrophages and 7/4-positive neutrophils and compared with the state-of-the-art deep learning segmentation architectures. DeepQuantify achieves an accuracy of 90.64% and 89.31% for CD68-positive macrophages and 7/4-positive neutrophils, respectively. The DeepQuantify system employing the proposed two-layer CNN architecture achieves better performance than those deep segmentation architectures. The quantitative analysis of two protein dynamics during muscle recovery is also presented.

This study investigates whether convolutional neural networks (CNNs) trained on visual representations of Portable Executable (PE) files can rival traditional machine learning classifiers trained on engineered features. A dataset of over 200,000 PE files [1] was used to derive two feature sets (Basic and Ember-Lite) [2] and to generate 256x256 greyscale and entropy images [3],[4]. Three CNNs (SimpleCNN, ResNet-18 [5], EfficientNet-B0 [6]) were trained and evaluated against five baselines (Random Forest, XGBoost [7], CatBoost [8], LightGBM, Logistic Regression). Tree-based models with enriched features achieved the highest scores, with CatBoost reaching a ROC-AUC of 0.990. The best CNN, EfficientNet-B0 on entropy images, obtained a ROC-AUC of 0.954. Although CNNs did not surpass feature-based models, they showed competitive results when feature engineering was constrained. These findings indicate that visual approaches offer a promising alternative for static malware detection, particularly when combined with entropy-based representations [9].

Since capsule endoscopy offers high-resolution, non-invasive imaging of the digestive tract, it has completely changed gastrointestinal diagnostics. However, the necessity for high diagnostic prediction precision, imbalanced datasets, and the variety of medical situations make automated classification of endoscopic images a considerable issue. In order to increase the precision and resilience of capsule endoscopy image classification, we provide a Hybrid CNN-SVM model in this work that combines the deep feature extraction capabilities of Convolutional Neural Networks (CNNs) with the higher classification margins of Support Vector Machines (SVMs). To ensure realistic variability free of structural distortions, the model uses MedAug-Net, a region-aware augmentation technique, to selectively improve non-ROI areas while retaining important diagnostic features. Furthermore, a Global Average Pooling (GAP) layer and depth wise separable convolutions are employed to improve computational efficiency without sacrificing robust feature representation. Better generalization and lower misclassification rates are ensured by replacing the traditional softmax layer with an SVM classifier with an RBF kernel. According to experimental results, the suggested model outperforms traditional Standard CNN architectures, which reach 98.30% accuracy under the identical experimental settings, with an accuracy of 99.47%. According to the findings, the Hybrid CNN- SVM model greatly increases classification accuracy when used in alongside MedAug-Net, making it an effective and clinically dependable method for analyzing capsule endoscopy images.

Accurate identification of driver's drowsiness state through Electroencephalogram (EEG) signals can effectively reduce traffic accidents, but EEG signals are usually stored in various clients in the form of small samples. This study attempts to construct an efficient and accurate privacy-preserving drowsiness monitoring system, and proposes a fusion model based on tree Federated Learning (FL) and Convolutional Neural Network (CNN), which can not only identify and explain the driver's drowsiness state, but also integrate the information of different clients under the premise of privacy protection. Each client uses CNN with the Global Average Pooling (GAP) layer and shares model parameters. The tree FL transforms communication relationships into a graph structure, and model parameters are transmitted in parallel along connected branches of the graph. Moreover, the Class Activation Mapping (CAM) is used to find distinctive EEG features for representing specific classes. On EEG data of 11 subjects, it is found that this method has higher average accuracy, F1-score and AUC than the traditional classification method, reaching 73.56%, 73.26% and 78.23%, respectively. Compared with the traditional FL algorithm, this method better protects the driver's privacy and improves communication efficiency.

Fine-tuning of pretrained convolutional neural networks (CNNs) has been proven to be an effective strategy for remote sensing image scene classification, particularly when a limited number of labeled data sets are available for training purposes. However, such a fine-tuning process often needs that the input images are resized into a fixed size to generate input vectors of the size required by fully connected layers (FCLs) in the pretrained CNN model. Such a resizing process often discards key information in the scenes and thus deteriorates the classification performance. To address this issue, in this paper, we introduce a scale-free CNN (SF-CNN) for remote sensing scene classification. Specifically, the FCLs in the CNN model are first converted into convolutional layers, which not only allow the input images to be of arbitrary sizes but also retain the ability to extract discriminative features using a traditional sliding-window-based strategy. Then, a global average pooling (GAP) layer is added after the final convolutional layer so that input images of arbitrary size can be mapped to feature maps of uniform size. Finally, we utilize the resulting feature maps to create a new FCL that is fed to a softmax layer for final classification. Our experimental results conducted using several real data sets demonstrate the superiority of the proposed SF-CNN method over several well-known classification methods, including pretrained CNN-based ones.

In recent years, attention mechanisms have shown great potential in various computer vision tasks. However, most existing methods focus on developing more complex attention modules for better performance, which inevitably increases the complexity of the model. To overcome performance and complexity tradeoffs, this paper proposes efficient spatial and channel attention (ESCA), a symmetric, comprehensive, and efficient attention module. By analyzing squeeze-and-excitation (SE), convolutional block attention module (CBAM), coordinate attention (CA), and efficient channel attention (ECA) modules, we abandon the dimension-reduction operation of SE module, verify the negative impact of global max pooling (GMP) on the model, and apply a local cross-channel interaction strategy without dimension reduction to learn attention. We not only care about the channel features of the image, we also care about the spatial location of the target on the image, and we take into account the effectiveness of channel attention, so we designed the symmetric ESCA module. The ESCA module is effective, as demonstrated by its application in the ResNet-50 classification benchmark. With 26.26 M parameters and 8.545 G FLOPs, it introduces a mere 0.14% increment in FLOPs while achieving over 6.33% improvement in Top-1 accuracy and exceeding 3.25% gain in Top-5 accuracy. We perform image classification and object detection tasks on ResNet, MobileNet, YOLO, and other architectures on popular datasets such as Mini ImageNet, CIFAR-10, and VOC 2007. Experiments show that ESCA can achieve great improvement in model accuracy at a very small cost, and it performs well among similar models.

Addressing the technological requirement for real-time monitoring of fish density in dim aquaculture environments, this study proposes a near-infrared (NIR) image classification method using a modified ResNet18 architecture. Initially, an NIR-Fish dataset comprising 736 high-quality annotated images (256 × 256 resolution) spanning three density scenarios (low, medium, and high density) was constructed. Contrast-Limited Adaptive Histogram Equalization (CLAHE) preprocessing was implemented with an 8 × 8 tiling strategy and clip limit = 4.0, significantly enhancing the discernibility of faint boundary features. A dual-channel attention module (DCAM) was embedded into the ResNet18 backbone, featuring a parallel architecture integrating Global Average Pooling (GAP) and Global Max Pooling (GMP). This design synergistically optimized local salient feature enhancement and global statistical feature fusion through parameter-shared fully connected layers (reduction ratio of 16:1). The experiments show that the classification accuracy of the proposed method on the independent test set is 80.57%, which is 4.34 percentage points higher than that of the original ResNet18. F1 scores for the three density levels were 0.8308 (low), 0.7674 (medium), and 0.8294 (high), respectively. Ablation studies confirmed the dual-channel design’s significant performance contribution, while the parameter-sharing mechanism effectively mitigated overfitting risks. By leveraging feature complementarity and lightweight design, this work overcomes the classification bottleneck for NIR images under low signal-to-noise conditions, providing a highly robust technical solution for intelligent aquaculture management.

A biosensing method for the direct serological detection of liver diseases by integrating a SERS-based sensor and a CNN classifier