Results 71 to 80 of about 46,889 (202)

Cyberattacks on Small Banks and the Impact on Local Banking Markets

Journal of Money, Credit and Banking, EarlyView.
Abstract Cyberattacks on small banks have direct and spillover effects in local markets. Following successful cyberattacks, hacked small banks experience a decline in deposit growth rates. This effect of cyberattacks is not observed in hacked large banks.
FABIAN GOGOLIN, IVAN LIM, FRANCESCO VALLASCAS   +2 more
wiley   +1 more source

Malware detection techniques for mobile devices

, 2017
Mobile devices have become very popular nowadays, due to its portability and high performance, a mobile device became a must device for persons using information and communication technologies. In addition to hardware rapid evolution, mobile applications
Amro, Bela
core   +1 more source

Design and Performance Analysis of an Anti-Malware System Based on Generative Adversarial Network Framework

IEEE Access
The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data.
Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, Farrukh Aslam Khan, Muhammad Rizwan, Aftab Ali   +5 more
doaj   +1 more source

A Survey for Deep Reinforcement Learning Based Network Intrusion Detection

Applied AI Letters, Volume 7, Issue 2, June 2026.
This paper surveys deep reinforcement learning (DRL) for network intrusion detection, evaluating model efficiency, minority attack detection, and dataset imbalance. Findings show DRL achieves state‐of‐the‐art results on public datasets, sometimes surpassing traditional deep learning.
Wanrong Yang, Alberto Acuto, Yihang Zhou, Dominik Wojtczak   +3 more
wiley   +1 more source

Malware Detection using Machine Learning and Deep Learning

, 2019
Research shows that over the last decade, malware has been growing exponentially, causing substantial financial losses to various organizations. Different anti-malware companies have been proposing solutions to defend attacks from these malware.
A Nappa, I Santos, MM Masud, R Moskovitch, SK Sahay, Y Ye, Y Ye   +6 more
core   +1 more source

BlockDroid: detection of Android malware from images using lightweight convolutional neural network models with ensemble learning and blockchain for mobile devices [PDF]

PeerJ Computer Science
Due to the increase in the volume and diversity of malware targeting Android systems, research on detecting this harmful software is steadily growing. Traditional malware detection studies require significant human intervention and resource consumption ...
Emre Şafak, İbrahim Alper Doğru, Necaattin Barışçı, İsmail Atacak   +3 more
doaj   +2 more sources

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

CAR‐T Cells: Current Status, Challenges, and Future Prospects

MedComm, Volume 7, Issue 5, May 2026.
This graphical abstract outlines the current status, challenges, and future prospects of CAR‐T cells. The biological basis of CAR‐T cell therapy is the elegant redirection of adaptive immunity. Its initial successes have exposed a landscape of multifaceted challenges.
Aya Sedky Adly, Guillaume Cartron, Afnan Sedky Adly, Jean‐Christophe Egea, Pierre‐Yves Collart Dutilleul, Mahmoud Sedky Adly, Martin Villalba   +6 more
wiley   +1 more source

Malware traffic detection based on type II fuzzy recognition

Frontiers in Physics
In recent years, a surge in malicious network incidents and instances of network information theft has taken place, with malware identified as the primary culprit.
Weisha Zhang, Jiajia Liu, Jimin Peng, Qiang Liu, Kun Yu   +4 more
doaj   +1 more source

Veto-based Malware Detection

2012 Seventh International Conference on Availability, Reliability and Security, 2012
Malicious software (malware) represents a threatto the security and privacy of computer users. Traditionalsignature-based and heuristic-based methods are unsuccessfulin detecting some forms of malware. This paper presents amalware detection approach based on supervised learning.
Shahzad, Raja Khurram, Lavesson, Niklas
openaire   +4 more sources