Results 11 to 20 of about 3,123 (222)

Automated Android Malware Detection Using User Feedback. [PDF]

Sensors (Basel), 2022
The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed
Duque J, Mendes G, Nunes L, de Almeida A, Serrão C.   +4 more
europepmc   +5 more sources

MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

IET Software
Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices.
Bo Yang, Mengbo Li, Li Li, Huai Liu
doaj   +2 more sources

ANDROID MALWARE DETECTION USING HAML

International Journal of Advanced Research in Computer Science, 2017
Mahima Choudhary
openalex   +3 more sources

Android Malware Detection Based on Sensitive Patterns

Telecommunication Systems, 2022
Abstract In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry.
Hui Li, Hailing Zhang, Xue Chen, Dan Liao, Ming Zhang   +4 more
openalex   +2 more sources

Trends in Android Malware Detection [PDF]

Journal of Digital Forensics, Security and Law, 2013
This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and
Kaveh Shaerpour, Ali Dehghantanha, Ramlan Mahmod   +2 more
openaire   +4 more sources

Deep Android Malware Detection [PDF]

Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, 2017
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
McLaughlin, Niall; id_orcid 0000-0002-0917-9145, Martinez del Rincon, Jesus; id_orcid 0000-0002-9574-4138, Kang, BooJoong, Yerima, Suleiman, Miller, Paul, Sezer, Sakir, Safaeisemnani, Yeganeh, Trickel, Erik, Zhao, Ziming, Doupé, Adam, Joon Ahn, Gail   +10 more
openaire   +4 more sources

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

Empirical Analysis of Forest Penalizing Attribute and Its Enhanced Variations for Android Malware Detection

Applied Sciences, 2022
As a result of the rapid advancement of mobile and internet technology, a plethora of new mobile security risks has recently emerged. Many techniques have been developed to address the risks associated with Android malware.
Abimbola G. Akintola, Abdullateef O. Balogun, Luiz Fernando Capretz, Hammed A. Mojeed, Shuib Basri, Shakirat A. Salihu, Fatima E. Usman-Hamza, Peter O. Sadiku, Ghaniyyat B. Balogun, Zubair O. Alanamu   +9 more
doaj   +1 more source

A Systematic Literature Review of Android Malware Detection Using Static Analysis

IEEE Access, 2020
Android malware has been in an increasing trend in recent years due to the pervasiveness of Android operating system. Android malware is installed and run on the smartphones without explicitly prompting the users or without the user's permission, and it ...
Ya Pan, Xiuting Ge, Chunrong Fang, Yong Fan   +3 more
doaj   +1 more source

Android malware category detection using a novel feature vector-based machine learning model

Cybersecurity, 2023
Malware attacks on the Android platform are rapidly increasing due to the high consumer adoption of Android smartphones. Advanced technologies have motivated cyber-criminals to actively create and disseminate a wide range of malware on Android ...
Hashida Haidros Rahima Manzil, S. Manohar Naik   +1 more
doaj   +1 more source