Results 11 to 20 of about 2,609 (164)

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, Abdullah Lakhan, Dilovan Asaad Zebari, Karrar Hameed Abdulkareem, Jan Nedoma, Radek Martinek, Usman Tariq, Majed Alhaisoni, Prayag Tiwari   +8 more
wiley   +1 more source

DroidDetectMW: A Hybrid Intelligent Model for Android Malware Detection

Applied Sciences, 2023
Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect.
Fatma Taher, Omar AlFandi, Mousa Al-kfairy, Hussam Al Hamadi, Saed Alrabaee   +4 more
doaj   +1 more source

On Deceiving Malware Classification with Section Injection

Machine Learning and Knowledge Extraction, 2023
We investigate how to modify executable files to deceive malware classification systems. This work’s main contribution is a methodology to inject bytes across a malware file randomly and use it both as an attack to decrease classification accuracy but ...
Adeilson Antonio da Silva, Mauricio Pamplona Segundo   +1 more
doaj   +1 more source

ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

Applied Sciences, 2020
Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes.
Zhijie Tang, Peng Wang, Junfeng Wang
doaj   +1 more source

Global-Local Attention-Based Butterfly Vision Transformer for Visualization-Based Malware Classification

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, Divya Meena Sundaram   +1 more
doaj   +1 more source

Deep learning based Sequential model for malware analysis using Windows exe API Calls [PDF]

PeerJ Computer Science, 2020
Malware development has seen diversity in terms of architecture and features. This advancement in the competencies of malware poses a severe threat and opens new research dimensions in malware detection.
Ferhat Ozgur Catak, Ahmet Faruk Yazı, Ogerta Elezaj, Javed Ahmed   +3 more
doaj   +2 more sources

Efficient Windows malware identification and classification scheme for plant protection information systems

Frontiers in Plant Science, 2023
Due to developments in science and technology, the field of plant protection and the information industry have become increasingly integrated, which has resulted in the creation of plant protection information systems.
Zhiguo Chen, Zhiguo Chen, Shuangshuang Xing, Shuangshuang Xing, Xuanyu Ren, Xuanyu Ren   +5 more
doaj   +1 more source

Malware-on-the-Brain: Illuminating Malware Byte Codes With Images for Malware Classification

IEEE Transactions on Computers, 2023
Malware is a piece of software that was written with the intent of doing harm to data, devices, or people. Since a number of new malware variants can be generated by reusing codes, malware attacks can be easily launched and thus become common in recent years, incurring huge losses in businesses, governments, financial institutes, health providers, etc.
Fangtian Zhong, Zekai Chen, Minghui Xu, Guoming Zhang, Dongxiao Yu, Xiuzhen Cheng   +5 more
openaire   +2 more sources

Efficient Malware Classification by Binary Sequences with One-Dimensional Convolutional Neural Networks

Mathematics, 2022
The rapid increase of malware attacks has become one of the main threats to computer security. Finding the best way to detect malware has become a critical task in cybersecurity. Previous work shows that machine learning approaches could be a solution to
Wei-Cheng Lin, Yi-Ren Yeh
doaj   +1 more source

FastText-Based Local Feature Visualization Algorithm for Merged Image-Based Malware Classification Framework for Cyber Security and Cyber Defense

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