Related papers: Image-Based Malware Classification Using QR and Az…
In recent years, malware becomes more threatening. Concerning the increasing malware variants, there comes Machine Learning (ML)-based and Deep Learning (DL)-based approaches for heuristic detection. Nevertheless, the prediction accuracy of…
The escalating frequency and scale of recent malware attacks underscore the urgent need for swift and precise malware classification in the ever-evolving cybersecurity landscape. Key challenges include accurately categorizing closely…
In this paper we present an elaborated graph-based algorithmic technique for efficient malware detection. More precisely, we utilize the system-call dependency graphs (or, for short ScD graphs), obtained by capturing taint analysis traces…
Although deep convolutional neural network has been proved to efficiently eliminate coding artifacts caused by the coarse quantization of traditional codec, it's difficult to train any neural network in front of the encoder for gradient's…
In today's interconnected digital landscape, the proliferation of malware poses a significant threat to the security and stability of computer networks and systems worldwide. As the complexity of malicious tactics, techniques, and…
For the task of change detection (CD) in remote sensing images, deep convolution neural networks (CNNs)-based methods have recently aggregated transformer modules to improve the capability of global feature extraction. However, they suffer…
Feature means countenance, remote sensing scene objects with similar characteristics, associated to interesting scene elements in the image formation process. They are classified into three types in image processing, that is low, middle and…
The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective.…
A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis while more complex…
Anti-cancer drug sensitivity prediction using deep learning models for individual cell line is a significant challenge in personalized medicine. REFINED (REpresentation of Features as Images with NEighborhood Dependencies) CNN…
Texture is a visual attribute largely used in many problems of image analysis. Currently, many methods that use learning techniques have been proposed for texture discrimination, achieving improved performance over previous handcrafted…
For efficient malware removal, determination of malware threat levels, and damage estimation, malware family classification plays a critical role. In this paper, we extract features from malware executable files and represent them as images…
Texture classification is an important and challenging problem in many image processing applications. While convolutional neural networks (CNNs) achieved significant successes for image classification, texture classification remains a…
Android malware has become an increasingly critical threat to organizations, society and individuals, posing significant risks to privacy, data security and infrastructure. As malware continues to evolve in terms of complexity and…
With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous. This ease of digital image capture and sharing is also accompanied…
In today's world, image processing plays a crucial role across various fields, from scientific research to industrial applications. But one particularly exciting application is image captioning. The potential impact of effective image…
Deep learning techniques have become prominent in modern fault diagnosis for complex processes. In particular, convolutional neural networks (CNNs) have shown an appealing capacity to deal with multivariate time-series data by converting…
The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative…
Recent researches have shown that machine learning based malware detection algorithms are very vulnerable under the attacks of adversarial examples. These works mainly focused on the detection algorithms which use features with fixed…
While deep convolutional neural networks (CNNs) have shown a great success in single-label image classification, it is important to note that real world images generally contain multiple labels, which could correspond to different objects,…