Results 41 to 50 of about 5,547 (215)

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

MalSSL—Self-Supervised Learning for Accurate and Label-Efficient Malware Classification

IEEE Access
Malware 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, Hendrawan, Budi Rahardjo, Tutun Juhana, Yasuo Musashi   +4 more
doaj   +1 more source

Discriminant malware distance learning on structuralinformation for automated malware classification [PDF]

ACM SIGMETRICS Performance Evaluation Review, 2013
In this work, we explore techniques that can automatically classify malware variants into their corresponding families. Our framework extracts structural information from malware programs as attributed function call graphs, further learns discriminant malware distance metrics, finally adopts an ensemble of classifiers for automated malware ...
Deguang Kong, Guanhua Yan
openaire   +1 more source

On the Limitations of Continual Learning for Malware Classification

CoRR, 2022
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 vulnerabilities. On a typical day, antivirus vendors receive hundreds of thousands of unique pieces of software, both malicious and benign, and ...
Mohammad Saidur Rahman 0002, Scott E. Coull, Matthew Wright 0001   +2 more
openaire   +3 more sources

Classification of packet contents for malware detection

, 2011
Many existing schemes for malware detection are signature-based. Although they can effectively detect known malwares, they cannot detect variants of known malwares or new ones.
Lhee, Kyung-suk, Irfan Ahmed, Ahmed, Irfan, Kyung-suk Lhee   +3 more
core   +1 more source

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

Advanced Intelligent Systems, EarlyView.
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

Android malware family classification based on resource consumption over time

, 2017
The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years.
Baldoni, R., Ciccotelli, Claudio, Roberto Baldoni, Ucci, Daniele, Luca Massarelli, Massarelli, L, Claudio Ciccotelli, Baldoni, Roberto, Ucci, D., Massarelli, Luca, Querzoni, L., Leonardo Querzoni, Ciccotelli, C, Leonardo Aniello, Daniele Ucci, Querzoni, Leonardo, Aniello, L, Aniello, Leonardo   +17 more
core   +1 more source

Malware Classification Using Ensemble Classifiers [PDF]

, 2018
Antimalware offers detection mechanism to detect and take appropriate action against malware detected. To evade detection, malware authors had introduced polymorphism to malware.
Mohd Hanafi Ahmad Hijazi, Tan Choon Beng, Yuto Lim, James Mountstephen, Kashif Nisar, Lim, Yuto, James Mountstephens   +6 more
core   +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, Moonyoung Kang, Seong-Je Cho, Sang-Il Choi   +3 more
doaj   +1 more source

AI‐Assisted IoT‐Enabled ECG Monitoring: Integrating Foundational and Generative AI Tools for Sustainable Smart Healthcare—Recent Trends

AI &Innovation, EarlyView.
ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury, Kandarpa Kumar Sarma, Lachit Dutta   +2 more
wiley   +1 more source