Results 31 to 40 of about 2,609 (164)

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

Malware Classification Using Transfer Learning

, 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.
Farhat, Hikmat, Rammouz, Veronica
openaire   +2 more sources

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

Sustainability Challenges to the Steel Industry in a Developing Country: Sanctions and Security Issues at the Forefront

Business Strategy and the Environment, EarlyView.
ABSTRACT This article contributes to sustainability research by investigating the complex, geopolitically induced challenges faced by industrial supply chains under international sanctions. Using Iran's steel industry as a case, it examines sustainability barriers through the lens of stakeholder theory. A mixed methods approach was employed.
Seyed Hamed Moosavirad, Mohammad Moshtari, Ali Esfahbodi   +2 more
wiley   +1 more source

DAEMON: Dataset/Platform-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining

IEEE Access, 2021
Numerous metamorphic and polymorphic malicious variants are generated automatically on a daily basis. In order to do that, malware vendors employ mutation engines that transform the code of a malicious program while retaining its functionality, aiming to
Ron Korine, Danny Hendler
doaj   +1 more source

Data‐Based Detection of Antagonistic Agents in a Robot Swarm Solving a Dynamic Coverage Task

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT Robot swarms can be deployed as moving surveillance systems, for instance, as mobile anti‐poaching systems for monitoring wildlife and detecting poaching activities. Since poachers have an interest in evading detection, robots are at risk of being hijacked and manipulated to behave antagonistically, for example, to prevent the correct ...
Ingeborg Wenger, Peter Eberhard, Henrik Ebel   +2 more
wiley   +1 more source

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

Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles

Software: Practice and Experience, EarlyView.
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, Emil Karlsson, Simin Nadjm‐Tehrani   +2 more
wiley   +1 more source

A Novel Solutions for Malicious Code Detection and Family Clustering Based on Machine Learning

IEEE Access, 2019
Malware has become a major threat to cyberspace security, not only because of the increasing complexity of malware itself, but also because of the continuously created and produced malicious code.
Hangfeng Yang, Shudong Li, Xiaobo Wu, Hui Lu, Weihong Han   +4 more
doaj   +1 more source

An Efficient Boosting-Based Windows Malware Family Classification System Using Multi-Features Fusion

Applied Sciences, 2023
In previous years, cybercriminals have utilized various strategies to evade identification, including obfuscation, confusion, and polymorphism technology, resulting in an exponential increase in the amount of malware that poses a serious threat to ...
Zhiguo Chen, Xuanyu Ren
doaj   +1 more source