Papers Malware Classification
“Malware Classification” 태그가 달린 논문 175편 · 필터 해제
On the Limitations of Continual Learning for Malware Classification
Malicious software (malware) classification offers a unique challenge for continual learning (CL) regimes due to the volume of new samples received on a daily basis and the evolution of malware to exploit new vulnerabili…
Continual LearningGeneral ClassificationIncremental LearningMalware ClassificationOn deceiving malware classification with section injection
We investigate how to modify executable files to deceive malware classification systems. This work's main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decre…
ClassificationMalware ClassificationDesigning Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification
In recent years, deep Convolutional Neural Networks (CNNs) have shown great potential in malware classification. CNNs, which are originally designed for image processing, identify malware binaries visualised as images. D…
Evolutionary AlgorithmsMalware ClassificationAI-based Malware and Ransomware Detection Models
Cybercrime is one of the major digital threats of this century. In particular, ransomware attacks have significantly increased, resulting in global damage costs of tens of billion dollars. In this paper, we train and tes…
Malware ClassificationMalware DetectionGenerative Adversarial Networks and Image-Based Malware Classification
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 repr…
BIG-bench Machine LearningClassificationMalware ClassificationMalware DetectionRepresentation learning with function call graph transformations for malware open set recognition
Open set recognition (OSR) problem has been a challenge in many machine learning (ML) applications, such as security. As new/unknown malware families occur regularly, it is difficult to exhaust samples that cover all the…
Malware ClassificationOpen Set LearningRepresentation LearningBackdooring Explainable Machine Learning
Explainable machine learning holds great potential for analyzing and understanding learning-based systems. These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealt…
BIG-bench Machine LearningMalware ClassificationMalceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection
We propose the Malceiver, a hierarchical Perceiver model for Android malware detection that makes use of multi-modal features. The primary inputs are the opcode sequence and the requested permissions of a given Android A…
Android Malware DetectionMalware ClassificationMalware DetectionBayesian Deep Learning for Graphs
The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, ther…
Deep LearningGraph ClassificationGraph LearningMalware ClassificationGraph Neural Network-based Android Malware Classification with Jumping Knowledge
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK). Android function call graphs (FCGs) consist of a set of program functions and their inter-proc…
Android Malware DetectionGraph Neural NetworkMalware ClassificationMalware DetectionComprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures
Modern computing systems have led cyber adversaries to create more sophisticated malware than was previously available in the early days of technology. Dated detection techniques such as Anti-Virus Software (AVS) based o…
BIG-bench Machine LearningEnsemble LearningMalware ClassificationAn Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification
Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to …
Malware ClassificationMalware Classification Using Static Disassembly and Machine Learning
Network and system security are incredibly critical issues now. Due to the rapid proliferation of malware, traditional analysis methods struggle with enormous samples. In this paper, we propose four easy-to-extract and s…
BIG-bench Machine LearningClassificationMalware ClassificationBenchmark Static API Call Datasets for Malware Family Classification
Nowadays, malware and malware incidents are increasing daily, even with various antivirus systems and malware detection or classification methodologies. Machine learning techniques have been the main focus of the securit…
ClassificationMalware ClassificationMalware DetectionA Comprehensive Study on Learning-Based PE Malware Family Classification Methods
Driven by the high profit, Portable Executable (PE) malware has been consistently evolving in terms of both volume and sophistication. PE malware family classification has gained great attention and a large number of app…
ClassificationMalware ClassificationPoison Forensics: Traceback of Data Poisoning Attacks in Neural Networks
In adversarial machine learning, new defenses against attacks on deep learning systems are routinely broken soon after their release by more powerful attacks. In this context, forensic tools can offer a valuable compleme…
Data PoisoningMalware ClassificationDRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems
Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for Deep Routing) for data-scarce domains l…
Deep ClusteringMalware ClassificationMalware DetectionMalware Classification Using Transfer Learning
With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malwar…
ClassificationDeep LearningMalware ClassificationTransfer LearningMalware Classification Using Deep Boosted Learning
Malicious activities in cyberspace have gone further than simply hacking machines and spreading viruses. It has become a challenge for a nations survival and hence has evolved to cyber warfare. Malware is a key component…
ClassificationMalware ClassificationTransfer LearningData Augmentation for Opcode Sequence Based Malware Detection
In this paper we study data augmentation for opcode sequence based Android malware detection. Data augmentation has been successfully used in many areas of deep-learning to significantly improve model performance. Typica…
Android Malware DetectionData AugmentationDiversityLanguage Modeling+4