Results 51 to 60 of about 1,517 (174)

Android Malware Familial Classification Based on DEX File Section Features

IEEE Access, 2020
The rapid proliferation of Android malware is challenging the classification of the Android malware family. The traditional static method for classification is easily affected by the confusion and reinforcement, while the dynamic method is expensive in ...
Yong Fang, Yangchen Gao, Fan Jing, Lei Zhang   +3 more
doaj   +1 more source

Governing Cybersecurity in the Digital Age: Mapping Comprehensive Policy Mixes With the Nodality‐Authority‐Treasure‐Organization Lens

Review of Policy Research, Volume 43, Issue 4, July 2026.
ABSTRACT The accelerated digitalisation of society has amplified cybersecurity threats and revealed their cross‐sectoral nature. Yet, the policy instruments used to address these challenges remain insufficiently examined. This study conducts a scoping review of 980 academic articles (2007–2024) and applies Hood's NATO framework (Nodality, Authority ...
Benedetta Cotta, Maria Stella Righettini
wiley   +1 more source

Research on visualization-based classification of malicious code

Dianzi Jishu Yingyong
The design of new malicious code is becoming increasingly complex, and traditional recognition and detection methods can no longer meet current requirements.
Ding Quan, Ding Borui, Zha Zhengpeng, Liu Deyang   +3 more
doaj   +1 more source

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, Volume 8, Issue 6, June 2026.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor, Shtwai Alsubai, Monji Mohamed Zaidi, Natalia Kryvinska, Abdullah Al Hejaili, Sidra Abbas   +5 more
wiley   +1 more source

OntoLogX: Ontology‐Guided Knowledge Graph Extraction From Cybersecurity Logs With Large Language Models

Advanced Intelligent Systems, Volume 8, Issue 6, June 2026.
OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti, Idilio Drago, Anisa Rula, Devis Bianchini, Federico Cerutti   +4 more
wiley   +1 more source

Generating Pattern‐Based Datasets for Cyber Attack Detection Using Machine‐Learning Techniques

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
The aim of this work is to review the state of the art in the design, generation, and labeling of attack pattern datasets for training of detection systems based on machine learning. ABSTRACT This work aims to review the state of the art in the design, generation, and labeling of attack pattern datasets for the training of detection systems based on ...
Pedro Díaz García, Diego López Abril, Javier Bermejo Higuera, Víctor Gonzalez Llorente, Miguel Angel Sicilia Urbán   +4 more
wiley   +1 more source

From Ambiguous Queries to Verifiable Insights: A Task‐Driven Framework for LLM‐Powered SOC Analysis⋆

CAAI Transactions on Intelligence Technology, Volume 11, Issue 3, Page 646-666, June 2026.
ABSTRACT Security operations centre (SOC) analysts must investigate alerts, correlate threat intelligence and interpret heterogeneous telemetry under tight timing constraints. Although large language models (LLMs) offer strong understanding capabilities, directly applying them to SOC environments remains challenging due to semantic ambiguity in analyst
Huan Zhang, Haiyan Wang, Hao Tan, Liyi Zeng, Jingnan Li, Zhaoquan Gu   +5 more
wiley   +1 more source

Method of anti-confusion texture feature descriptor for malware images

Tongxin xuebao, 2018
It is a new method that uses image processing and machine learning algorithms to classify malware samples in malware visualization field.The texture feature description method has great influence on the result.To solve this problem,a new method was ...
Yashu LIU, Zhihai WANG, Hanbing YAN, Yueran HOU, Yukun LAI   +4 more
doaj   +2 more sources

A Survey of Visualization Systems for Malware Analysis.

, 2015
published
Markus Wagner 0008, Fabian Fischer 0001, Robert Luh, Andrea Haberson, Alexander Rind, Daniel A. Keim, Wolfgang Aigner   +6 more
openaire   +2 more sources

Accelerated‐USE: A Benchmark Framework for GPU‐Driven Graph Neural Network Training

Concurrency and Computation: Practice and Experience, Volume 38, Issue 9, May 2026.
ABSTRACT Graph processing is used in many domains to extract knowledge from real‐world data. With the rise of deep neural networks and scaled compute infrastructure in artificial intelligence (AI), specialized techniques emerged to leverage graphs in applications such as recommendation systems and social networks.
Lucas de Angelo Martins Ribeiro, Willian Borges de Lemos, Vanessa Telles da Silva, Marcelo de Gomensoro Malheiros, Nelson Lopes Duarte Filho, Marcelo Rita Pias   +5 more
wiley   +1 more source