Results 41 to 50 of about 4,236,332 (208)

GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking

Applied Sciences, 2023
With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work.
Nannan Xie, Zhaowei Qin, Xiaoqiang Di
semanticscholar   +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

A survey of IoT malware and detection methods based on static features

ICT Express, 2020
Due to a lack of security design as well as the specific characteristics of IoT devices such as the heterogeneity of processor architecture, IoT malware detection has to deal with very unique challenges, especially on detecting cross-architecture IoT ...
Quoc-Dung Ngo, Huy-Trung Nguyen, Van-Hoang Le, Doan-Hieu Nguyen   +3 more
doaj   +1 more source

Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN

IEEE Systems Journal, 2023
The threat of malware in the Internet of Things (IoT) is ever-present given that many IoT systems today rely on the Android operating system. There has been a consistent rise in Android malware recently, with new variants adopting sophisticated detection
Dhanya K. A, V. P., S. Yerima, A. Bashar, Anwin David, A. T., Alan Antony, Ashil K. Shavanas, G. T.   +8 more
semanticscholar   +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

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, S. A. Roseline, S. Geetha, Seifedien Kadry, R. Damaševičius   +4 more
semanticscholar   +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

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

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

MAPAS: a practical deep learning-based android malware detection system

International Journal of Information Security, 2022
A lot of malicious applications appears every day, threatening numerous users. Therefore, a surge of studies have been conducted to protect users from newly emerging malware by using machine learning algorithms.
Jinsung Kim, Younghoon Ban, Eunbyeol Ko, Haehyun Cho, J. Yi   +4 more
semanticscholar   +1 more source