Results 11 to 20 of about 1,405 (158)
Adaptive secure malware efficient machine learning algorithm for healthcare data
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed +8 more
wiley +1 more source
IEEE Access, 2023 In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic techniques for visualization-based malware classification and detection.
Mohamad Mulham Belal +1 more
doaj +1 more source
Mathematics, 2020 The importance of cybersecurity has recently been increasing. A malware coder writes malware into normal executable files. A computer is more likely to be infected by malware when users have easy access to various executables.
Sejun Jang, Shuyu Li, Yunsick Sung
doaj +1 more source
Malware Visualization Techniques
International Journal of Applied Mathematics Electronics and Computers, 2020 Malware basically means malicious software that can be an intrusive program code or anything that is designed to perform malicious operations on system and executes malicious actions such as clandestine, listening, monitoring, saving, and deleting without the user's knowledge and consent.
Ahmet Efe, Saleh Hussin S. Hussin
openaire +3 more sources
IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
Applied Sciences, 2022 The smart factory environment has been transformed into an Industrial Internet of Things (IIoT) environment, which is an interconnected and open approach.
Ho-myung Kim, Kyung-ho Lee
doaj +1 more source
Malware Behaviour Visualization
Jurnal Teknologi, 2014 The number of unique malware variants released each year is on the rise. Researchers may often need to use manual static and dynamic analysis to study new malware samples. Manual analysis of malware samples takes time. The more time taken to analyse a malware sample, the larger the damage that a malware can inflict.
Syed Zainudeen Mohd Shaid +1 more
openaire +1 more source
An Efficient DenseNet-Based Deep Learning Model for Malware Detection
Entropy, 2021 Recently, there has been a huge rise in malware growth, which creates a significant security threat to organizations and individuals. Despite the incessant efforts of cybersecurity research to defend against malware threats, malware developers discover ...
Jeyaprakash Hemalatha +4 more
doaj +1 more source
IEEE Access, 2020 Today, with the continuous promotion and development of IoT and 5G technology, Cyberspace has become an important pillar of economic and social development, and also a foundational domain of national security.
Yuntao Zhao +5 more
doaj +1 more source
Intelligent Vision-Based Malware Detection and Classification Using Deep Random Forest Paradigm
IEEE Access, 2020 Malware is a rapidly increasing menace to modern computing. Malware authors continually incorporate various sophisticated features like code obfuscations to create malware variants and elude detection by existing malware detection systems.
S. Abijah Roseline +3 more
doaj +1 more source
Features Engineering for Malware Family Classification Based API Call
Computers, 2022 Malware is used to carry out malicious operations on networks and computer systems. Consequently, malware classification is crucial for preventing malicious attacks.
Ammar Yahya Daeef +2 more
doaj +1 more source