Results 31 to 40 of about 2,185,837 (169)
Design of an Automated Malware Analysis System [PDF]
, 2010 Malware, malicious software written for the purpose of causing computer-related damage, has existed for many years. Only recently have the monetary advantages of perpetrating malware begun to gain interest from increasingly malicious groups.
Nabholz, Bradley J
core +1 more source
MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification
IEEE AccessMalware classification with supervised learning requires a large dataset, which needs an expensive and time-consuming labeling process. In this paper, we explore the efficacy of self-supervised learning techniques for malware classification.
Setia Juli Irzal Ismail +4 more
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An Efficient Malware Classification Method Based on the AIFS-IDL and Multi-Feature Fusion
Information, 2022 In recent years, the presence of malware has been growing exponentially, resulting in enormous demand for efficient malware classification methods.
Xuan Wu, Yafei Song
doaj +1 more source
Efficient Deep Learning Network With Multi-Streams for Android Malware Family Classification
IEEE Access, 2022 It is important to effectively detect, mitigate, and defend against Android malware attacks, because Android malware has long represented a major threat to Android app security.
Hyun-Il Kim +3 more
doaj +1 more source
VAE‐Based SPC for Botnet Detection in IoT Systems
Quality and Reliability Engineering International, EarlyView.ABSTRACT The rapid proliferation of Internet of Things (IoT) devices has created new security risks, particularly botnet infections. This paper proposes an unsupervised monitoring framework that combines a β$\beta$‐variational autoencoder (VAE) with multivariate statistical process control (SPC) for botnet detection in IoT networks.
Jorge Proaño +3 more
wiley +1 more source
Normal‐Mode‐Aware Augmented Kernel One‐Class SVM for Heterogeneous Normality Modeling
IEEJ Transactions on Electrical and Electronic Engineering, EarlyView.Despite the rapid advances in anomaly detection, many existing methods assume that normal data are homogeneous and can be modeled by a single one‐class boundary. However, in many practical scenarios, normal data are heterogeneous and consist of multiple latent modes, making conventional latent‐space kernels insufficient to characterize normality.
Wenqian Yu, Jiaying Wu, Jinglu Hu
wiley +1 more source
Boutique Malware – Custom made for e-business [PDF]
, 2010 Malware are typically known through extensive publicity in the media when incidents such as infection by Conficker on the computers around the globe. Such Malware infects all who are vulnerable to its bite.
Fung, C.C., Pan, J.Y.
core
Similarity-Based Malware Classification Using Graph Neural Networks
Applied Sciences, 2022 This work proposes a novel malware identification model that is based on a graph neural network (GNN). The function call relationship and function assembly content obtained by analyzing the malware are used to generate a graph that represents the ...
Yu-Hung Chen +2 more
doaj +1 more source
Classification of Malware Images Using Fine-Tunned ViT
Sakarya University Journal of Computer and Information SciencesMalware detection and classification have become critical tasks in ensuring the security and integrity of computer systems and networks. Traditional methods of malware analysis often rely on signature-based approaches, which struggle to cope with the ...
Özal Yıldırım, Oğuzhan Katar
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Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
Applied Sciences, 2021 Malware family classification is grouping malware samples that have the same or similar characteristics into the same family. It plays a crucial role in understanding notable malicious patterns and recovering from malware infections.
Minki Kim +5 more
doaj +1 more source