Results 51 to 60 of about 12,725 (233)

IoT Malware Detection with Machine Learning [PDF]

, 2022
Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these IoT devices can also contain security vulnerabilities, which allow attackers to compromise them using malware.
Buttyán Levente, Ferenc Rudolf
core  

Android Malware Category and Family Identification Using Parallel Machine Learning [PDF]

Journal of Information Technology Management, 2022
Android malware is one of the most dangerous threats on the Internet.  It has been on the rise for several years.  As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc.
Ahmed Hashem El Fiky, Mohamed Ashraf Madkour, Ayman El Shenawy   +2 more
doaj   +1 more source

Malware Detection

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT, 2023
The "Malware Detection on Application using Machine Learning" project is a focused initiative aimed at enhancing the security of mobile applications through advanced detection mechanisms. As the threat landscape for mobile app-based malware continues to evolve, this project leverages the power of machine learning to develop robust and adaptive ...
openaire   +1 more source

Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review

Telematics and Informatics Reports
The ever-increasing growth of online services and smart connectivity of devices have posed the threat of malware to computer system, android-based smart phones, Internet of Things (IoT)-based systems.
Santosh K. Smmarwar, Govind P. Gupta, Sanjay Kumar   +2 more
doaj   +1 more source

SMASH: A Malware Detection Method Based on Multi-Feature Ensemble Learning

IEEE Access, 2019
With the increasing variants of malware, it is of great significance to detect malware and ensure system security effectively. The existing malware dynamic detection methods are vulnerable to evasion attacks.
Yusheng Dai, Hui Li, Yekui Qian, Ruipeng Yang, Min Zheng   +4 more
doaj   +1 more source

Evaluation and classification of obfuscated Android malware through deep learning using ensemble voting mechanism

Scientific Reports, 2023
With the rise in popularity and usage of Android operating systems, malicious applications are targeted by applying innovative ways and techniques.
Sana Aurangzeb, Muhammad Aleem
doaj   +1 more source

Research on Application of Attention-CNN in Malware Detection

Jisuanji kexue yu tansuo, 2021
The attack of malware has become one of the most major threats to the Internet. What??s more, the existing malware data are huge and have multiple features. In order to extract the characteristics better and master the behaviors of malware, Attention-CNN
MA Dan, WAN Liang, CHENG Qiqin, SUN Zhiqiang
doaj   +1 more source

Intensive Malware Detection Approach based on Data Mining

Journal of Applied Engineering and Technological Science, 2023
Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via
Israa Ezzat Salem, Karim Hashim Al-Saedi
doaj   +1 more source

vinayakumarr/Android-Malware-Detection v1

, 2019
Android malware detection using static and dynamic ...
Vinayakumar R
core   +1 more source

On the Effectiveness of Perturbations in Generating Evasive Malware Variants

IEEE Access, 2023
Malware variants are generated using various evasion techniques to bypass malware detectors, so it is important to understand what properties make them evade malware detection techniques.
Beomjin Jin, Jusop Choi, Jin B. Hong, Hyoungshick Kim   +3 more
doaj   +1 more source