Results 91 to 100 of about 5,547 (215)
Hidden Markov Models for Malware Classification
, 2013 Malware is a software which is developed for malicious intent. Malware is a rapidly evolving threat to the computing community. Although many techniques for malware classification have been proposed, there is still the lack of a comprehensible and useful
Annachhatre, Chinmayee
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MtNet: A Multi-Task Neural Network for Dynamic Malware Classification [PDF]
, 2020 . In this paper, we propose a new multi-task, deep learning architecture for malware classification for the binary (i.e. malware versus benign) malware classification task. All models are trained with data extracted from dynamic analysis of malicious and
Jack W Stokes, Wenyi Huang
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GRASE: Granulometry Analysis With Semi Eager Classifier to Detect Malware.
International Journal of Interactive Multimedia and Artificial IntelligenceTechnological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT).
Mahendra Deore +3 more
doaj +1 more source
On Predicting Vulnerability Severity Using In‐Context Learning: An Industrial Case Study
Journal of Software: Evolution and Process, Volume 38, Issue 6, June 2026.ABSTRACT Modern software systems require earlier and more scalable vulnerability severity assessment to reduce exposure to high‐impact security flaws. Security analysts typically assign CVSS scores, but this manual triage does not scale with the growth of disclosed vulnerabilities and often depends on cloud LLM services that raise confidentiality ...
Daniel Rodriguez‐Cardenas +10 more
wiley +1 more source
, 2021 With the ever-increasing threat of malware attacks, building an effective malware classifier to detect malware promptly is of utmost importance. Malware is constantly growing and evolving with the use of sophisticated obfuscation techniques.
Khandhar, Shubham (author)
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MACHINE LEARNING APPLICATIONS IN MALWARE CLASSIFICATION: A METAANALYSIS LITERATURE REVIEW
, 2023 With a text mining and bibliometrics approach, this study reviews the literature on the evolution of malware classification using machine learning.
Nelson, Tjada +2 more
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Mi-maml: classifying few-shot advanced malware using multi-improved model-agnostic meta-learning
CybersecurityMalware classification has been successful in utilizing machine learning methods. However, it is limited by the reliance on a large number of high-quality labeled datasets and the issue of overfitting. These limitations hinder the accurate classification
Yulong Ji, Kunjin Zou, Bin Zou
doaj +1 more source
Efficient malware detection using NLP and deep learning model
Alexandria Engineering JournalMalware has emerged as a significant challenge in contemporary society, growing in tandem with technological advancements. Consequently, the classification of malware has become a pressing concern for various services.
Umesh Gupta +6 more
doaj +1 more source
Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles
Software: Practice and Experience, Volume 56, Issue 6, Page 615-642, June 2026.ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom +2 more
wiley +1 more source
Exploring discriminatory features for automated malware classification
, 2013 . The ever-growing malware threat in the cyber space calls for tech-niques that are more effective than widely deployed signature-based detection systems and more scalable than manual reverse engineering by forensic experts.
Nathan Brown, Deguang Kong, Guanhua Yan
core +1 more source