Results 41 to 50 of about 1,811,594 (209)

Malware Classification Using Transfer Learning

CoRR, 2021
With the rapid growth of the number of devices on the Internet, malware poses a threat not only to the affected devices but also their ability to use said devices to launch attacks on the Internet ecosystem. Rapid malware classification is an important tools to combat that threat.
Hikmat Farhat, Veronica Rammouz
openaire   +3 more sources

AFAgarap/malware-classification v0.1-alpha

, 2017
<p>Code implementation of "Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification"</p ...
Abien Fred Agarap
core   +1 more source

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, Daehan Kim, Changha Hwang, Seongje Cho, Sangchul Han, Minkyu Park   +5 more
doaj   +1 more source

BinSlayer: Accurate Comparison of Binary Executables [PDF]

, 2013
As the volume of malware inexorably rises, comparison of binary code is of increasing importance to security analysts as a method of automatically classifying new malware samples; purportedly new examples of malware are frequently a simple evolution of ...
Martial Bourquin, Bourquin, Martial, Andy King, Robbins, Edward, King, Andy, Edward Robbins   +5 more
core   +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

Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
Mobile devices have been spreading at great rate in recent years. Not only smartphone, but also tablets and IoT devices, are gaining an increasingly place in our everyday lives. This is the reason why attackers are developing more and more aggressive techniques with the aim to exfiltrate our sensitive and private information.
Mercaldo F., Saracino A.
openaire   +4 more sources

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   +2 more sources

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   +4 more sources

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

Mapping Corporate Environment, Social, and Governance Discourses: Analysis of Korean Companies' Sustainability Reports (2014–2024)

Corporate Social Responsibility and Environmental Management, EarlyView.
ABSTRACT Corporations increasingly use Environmental, Social, and Governance (ESG) reports to articulate their commitments, priorities, and performance in sustainability governance. This study examines how Korean firms have configured and reconfigured their sustainability discourses across industries and time using 634 sustainability reports (2014–2024)
Taedong Lee, Sinjae Kang, Dian Wahyu Utami, Bohae Na   +3 more
wiley   +1 more source