Papers Malware Classification
“Malware Classification” 태그가 달린 논문 175편 · 필터 해제
A Malware Classification Survey on Adversarial Attacks and Defences
As the number and complexity of malware attacks continue to increase, there is an urgent need for effective malware detection systems. While deep learning models are effective at detecting malware, they are vulnerable to…
Adversarial AttackClassificationMalware ClassificationMalware Detection+1High-resolution Image-based Malware Classification using Multiple Instance Learning
This paper proposes a novel method of classifying malware into families using high-resolution greyscale images and multiple instance learning to overcome adversarial binary enlargement. Current methods of visualisation-b…
ClassificationMalware ClassificationMultiple Instance LearningEnhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection
Malware poses a significant security risk to individuals, organizations, and critical infrastructure by compromising systems and data. Leveraging memory dumps that offer snapshots of computer memory can aid the analysis …
Classificationfeature selectionMalware ClassificationNebula: Self-Attention for Dynamic Malware Analysis
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment and logging their actions. Previous work has proposed training machine learning models, i.e., convolutional and long sh…
Malware AnalysisMalware ClassificationMalware DetectionSelf-Supervised LearningMalware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices
With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for mal…
Edge-computingMalware ClassificationMalware DetectionDecoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance
Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dy…
ClassificationMalware ClassificationMalware DetectionImpact of Feature Encoding on Malware Classification Explainability
This paper investigates the impact of feature encoding techniques on the explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a malware classification dataset, we trained an XGBoost model and com…
ClassificationExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Malware ClassificationA Natural Language Processing Approach to Malware Classification
Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov M…
ClassificationFeature EngineeringMalware ClassificationMalware DetectionSteganographic Capacity of Deep Learning Models
As machine learning and deep learning models become ubiquitous, it is inevitable that there will be attempts to exploit such models in various attack scenarios. For example, in a steganographic-based attack, information …
Deep LearningMalware ClassificationInterpreting GNN-based IDS Detections Using Provenance Graph Structural Features
Advanced cyber threats (e.g., Fileless Malware and Advanced Persistent Threat (APT)) have driven the adoption of provenance-based security solutions. These solutions employ Machine Learning (ML) models for behavioral mod…
Anomaly DetectionDecision MakingDescriptiveExplainable Models+3Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection
Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address c…
Malware ClassificationQuantum Machine LearningRecasting Self-Attention with Holographic Reduced Representations
In recent years, self-attention has become the dominant paradigm for sequence modeling in a variety of domains. However, in domains with very long sequence lengths the $\mathcal{O}(T^2)$ memory and $\mathcal{O}(T^2 H)$ c…
Malware ClassificationMalware DetectionQuantum Machine Learning for Malware Classification
In a context of malicious software detection, machine learning (ML) is widely used to generalize to new malware. However, it has been demonstrated that ML models can be fooled or may have generalization problems on malwa…
ClassificationMalware ClassificationQuantum Machine LearningCan Feature Engineering Help Quantum Machine Learning for Malware Detection?
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance. At the same time, many popular ML models used in malware classification are s…
Feature Engineeringfeature selectionMalware ClassificationMalware Detection+1A Comparison of Graph Neural Networks for Malware Classification
Managing the threat posed by malware requires accurate detection and classification techniques. Traditional detection strategies, such as signature scanning, rely on manual analysis of malware to extract relevant feature…
ClassificationGraph ClassificationGraph Neural NetworkMalware ClassificationSequential Embedding-based Attentive (SEA) classifier for malware classification
The tremendous growth in smart devices has uplifted several security threats. One of the most prominent threats is malicious software also known as malware. Malware has the capability of corrupting a device and collapsin…
ClassificationMalware ClassificationMalware DetectionA Dynamic Weighted Federated Learning for Android Malware Classification
Android malware attacks are increasing daily at a tremendous volume, making Android users more vulnerable to cyber-attacks. Researchers have developed many machine learning (ML)/ deep learning (DL) techniques to detect a…
ClassificationFederated LearningMalware ClassificationLempel-Ziv Networks
Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, they are known to be both ineffective and …
Malware ClassificationA Novel Feature Representation for Malware Classification
In this study we have presented a novel feature representation for malicious programs that can be used for malware classification. We have shown how to construct the features in a bottom-up approach, and analyzed the ove…
ClassificationDescriptiveMalware ClassificationSelf-Supervised Vision Transformers for Malware Detection
Malware detection plays a crucial role in cyber-security with the increase in malware growth and advancements in cyber-attacks. Previously unseen malware which is not determined by security vendors are often used in thes…
Binary ClassificationMalware ClassificationMalware DetectionMalware Family Detection+2