Results 31 to 40 of about 1,195 (175)

FAM: Featuring Android Malware for Deep Learning-Based Familial Analysis

IEEE Access, 2022
To handle relentlessly emerging Android malware, deep learning has been widely adopted in the research community. Prior work proposed deep learning-based approaches that use different features of malware, and reported a high accuracy in malware detection,
Younghoon Ban, Sunjun Lee, Dokyung Song, Haehyun Cho, Jeong Hyun Yi   +4 more
doaj   +1 more source

An Android Malicious Code Detection Method Based on Improved DCA Algorithm

Entropy, 2017
Recently, Android malicious code has increased dramatically and the technology of reinforcement is increasingly powerful. Due to the development of code obfuscation and polymorphic deformation technology, the current Android malicious code static ...
Chundong Wang, Zhiyuan Li, Liangyi Gong, Xiuliang Mo, Hong Yang, Yi Zhao   +5 more
doaj   +1 more source

DroidDetector: Android Malware Characterization and Detection Using Deep Learning

Tsinghua Science and Technology, 2016
Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign
Zhenlong Yuan, Yongqiang Lu, Yibo Xue
doaj   +1 more source

Gauss-Mapping Black Widow Optimization With Deep Extreme Learning Machine for Android Malware Classification Model

IEEE Access, 2023
Nowadays, the malware on the Android platform is found to be increasing. With the prevalent use of code obfuscation technology, the precision of antivirus software and classical detection techniques is low.
Ghadah Aldehim, Munya A. Arasi, Majdi Khalid, Sumayh S. Aljameel, Radwa Marzouk, Heba Mohsen, Ishfaq Yaseen, Sara Saadeldeen Ibrahim   +7 more
doaj   +1 more source

SAMADroid: A Novel 3-Level Hybrid Malware Detection Model for Android Operating System

IEEE Access, 2018
For the last few years, Android is known to be the most widely used operating system and this rapidly increasing popularity has attracted the malware developer's attention.
Saba Arshad, Munam A. Shah, Abdul Wahid, Amjad Mehmood, Houbing Song, Hongnian Yu   +5 more
doaj   +1 more source

A machine learning technique for Android malicious attacks detection based on API calls [PDF]

Decision Science Letters
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and ...
Mousa AL-Akhras, Saud Alghamdi, Hani Omar, Hazzaa Alshareef   +3 more
doaj   +1 more source

Efficient Deep Learning Network With Multi-Streams for Android Malware Family Classification

IEEE Access, 2022
It is important to effectively detect, mitigate, and defend against Android malware attacks, because Android malware has long represented a major threat to Android app security.
Hyun-Il Kim, Moonyoung Kang, Seong-Je Cho, Sang-Il Choi   +3 more
doaj   +1 more source

A Comprehensive Systematic Review of Cyber‐Physical Systems Security: Threats, Challenges, and Defense in ICS/OT Environments

Engineering Reports, Volume 8, Issue 7, July 2026.
Overview of the paper organization, illustrating the hierarchical structure of cybersecurity domains in ICS and CPS, including attack analysis, security approaches, offensive tactics, career guidance, and concluding discussions. ABSTRACT The convergence of operational technology (OT) with IP‐based information systems has exposed industrial control ...
M. A. Khalifa, Ahmad Taher Azar, Walid El‐Shafai   +2 more
wiley   +1 more source

Finding Minimum‐Cost Explanations for Predictions Made by Tree Ensembles

Software: Practice and Experience, Volume 56, Issue 6, Page 615-642, June 2026.
ABSTRACT The ability to reliably explain why a machine learning model arrives at a particular prediction is crucial when used as decision support by human operators of critical systems. The provided explanations must be provably correct, and preferably without redundant information, called minimal explanations.
John Törnblom, Emil Karlsson, Simin Nadjm‐Tehrani   +2 more
wiley   +1 more source

Android Fragmentation in Malware Detection

Computers & Security, 2019
Abstract Differences between Android versions affect not only application developers but also make the task of securing Android harder, as it is not easy to keep track of updates. In this paper, we first systematically analyze the Android framework, which includes APIs and enforced manifest permissions to realize the inconsistency currently exists in
Long Nguyen-Vu, Jinung Ahn, Souhwan Jung
openaire   +1 more source