Results 101 to 110 of about 11,847 (201)

Backdoor Attack and Defense Methods for AI–Based IoT Intrusion Detection System

IET Information Security, Volume 2025, Issue 1, 2025.
The Internet of Things (IoT) is an emerging technology that has attracted significant attention and triggered a technical revolution in recent years. Numerous IoT devices are directly connected to the physical world, such as security cameras and medical equipment, making IoT security a critical issue.
Bowen Ma, Jiangwei Shi, Ning Zhu, Chen Fang, Yongjin Hu, Bohui Wang   +5 more
wiley   +1 more source

Android malware detection using random forest algorithm

Proceedings of the Nigerian Society of Physical Sciences
The proliferation of mobile devices and their dependence on the android OS has made them prime targets for cybercriminals, leading to an escalating threat of malware.
Samson Isaac, Abdullahi Tanimu, Mohammed Shamsuddeen Tukur , Jacob Isaac, Amina Bala Ja'afaru   +4 more
doaj   +1 more source

Intelligent Pattern Recognition Using Equilibrium Optimizer With Deep Learning Model for Android Malware Detection

IEEE Access
Android malware recognition is the procedure of mitigating and identifying malicious software (malware) planned to target Android operating systems (OS) that are extremely utilized in smartphones and tablets.
Mohammed Maray, Mashael Maashi, Haya Mesfer Alshahrani, Sumayh S. Aljameel, Sitelbanat Abdelbagi, Ahmed S. Salama   +5 more
doaj   +1 more source

An Improved Malicious Application Detection in Social Networks (MADSN)

مجلة جامعة الزيتونة, 2021
Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware ...
Nagmden Nasser, Adel Abosdel
doaj  

Android Malware Detection Systems Review

Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2017
With the smartphones entering our lives, the number of smartphones continues to increase day by day. The reason why smartphones are in so demand is that people can easily do what they want. According to IDC's 2016 Q2 report, Android dominated the smartphone market with an 87.6% share [1].
Ömer Kiraz, İbrahim Alper Doğru
openaire   +1 more source

A Lightweight malware detection technique based on hybrid fuzzy simulated annealing clustering in Android apps

Egyptian Informatics Journal
The growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware.
Collins Chimeleze, Norziana Jamil, Nazik Alturki, Zuhaira Muhammad Zain   +3 more
doaj   +1 more source

A Combination Method for Android Malware Detection Based on Control Flow Graphs and Machine Learning Algorithms

IEEE Access, 2019
Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware.
Zhuo Ma, Haoran Ge, Yang Liu, Meng Zhao, Jianfeng Ma   +4 more
doaj   +1 more source

Android malware detection with unbiased confidence guarantees

Neurocomputing, 2018
The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without
Papadopoulos, Harris, Georgiou, Nestoras, Eliades, Charalambos, Konstantinidis, Andreas   +3 more
openaire   +2 more sources

Android Malware Detection using ML

International Journal of Advanced Research in Science, Communication and Technology
Android devices are more prone of malware attacks due to its open-source nature. This makes it easier for installing applications from various sources, which can lead to major security issues. Machine learning learns from examples. It studies data from apps both good and bad and understands its characteristics.
null Pradhipa S, null Poornima, null Indu A, null Reena K   +3 more
openaire   +1 more source

AAGAN: Android Malware Generation System Based on Generative Adversarial Network

Vietnam Journal of Computer Science
With the rapid evolution of mobile malware, especially Android malware, machine learning (ML)-based Android malware detection systems have drawn massive attention. Although ML algorithms have recently led to many vital breakthroughs in malware detection,
Doan Minh Trung, Nghi Hoang Khoa, Phan The Duy, Van-Hau Pham, Nguyen Tan Cam   +4 more
doaj   +1 more source