On Visual Hallmarks of Robustness to Adversarial Malware
CoRR, 2018 A central challenge of adversarial learning is to interpret the resulting hardened model. In this contribution, we ask how robust generalization can be visually discerned and whether a concise view of the interactions between a hardened decision map and input samples is possible.
Alex Huang +3 more
openaire +2 more sources
A Classification System for Visualized Malware Based on Multiple Autoencoder Models
IEEE Access, 2021 In this paper, we propose a classification system that uses multiple autoencoder models for identifying malware images. It is crucial to accurately classify malware before we can deploy appropriate countermeasures to prevent them from spreading.
Jongkwan Lee, Jongdeog Lee
doaj +1 more source
Review of malware detection and classification visualization techniques
网络与信息安全学报, 2023 With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection ...
Jinwei WANG +4 more
doaj +3 more sources
Classification and Analysis of Android Malware Images Using Feature Fusion Technique
IEEE Access, 2021 The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market.
Jaiteg Singh +5 more
doaj +1 more source
A Deep Learning Framework for Three-Dimensional Malware Image Classification
Applied SciencesThe rapid growth of sophisticated malware, including polymorphic, metamorphic, and zero-day threats, has made traditional signature-based and heuristic detection methods increasingly insufficient in modern desktop computing environments. As cyber threats
Muharrem Aslantas +3 more
doaj +1 more source
Efficient Malware Analysis Using Subspace-Based Methods on Representative Image Patterns
IEEE Access, 2023 In this paper, we propose a new framework for classifying and visualizing malware files using subspace-based methods. The rise of advanced malware poses a significant threat to internet security, increasing the pressure on traditional cybersecurity ...
Djafer Yahia M Benchadi +2 more
doaj +1 more source
MalVis: Large-Scale Bytecode Visualization Framework for Explainable Android Malware Detection
Journal of Cybersecurity and PrivacyAs technology advances, developers continually create innovative solutions to enhance smartphone security. However, the rapid spread of Android malware poses significant threats to devices and sensitive data.
Saleh J. Makkawy +2 more
doaj +1 more source
Cybercopters Swarm: Immersive analytics for alerts classification based on periodic data
Frontiers in Virtual Reality, 2023 This paper assesses the usefulness of an interactive and navigable 3D environment to help decision-making in cybersecurity. Malware programs frequently emit periodic signals in network logs; however, normal periodical network activities, such as software
Nicolas Delcombel +2 more
doaj +1 more source
Enhancing Malware Analysis Using Data Visualization Through Shared Code and Attribute Analysis
IEEE AccessMalware analysis is a crucial area of cybersecurity, focusing on identifying, categorizing, and studying malicious software to prevent it from posing a threat to computer systems.
Narayandas Sai Ramana Vashista +1 more
doaj +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