Rahul Gupta¹, Kapil Sharma^1,*, R. K. Garg²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4283-4301, 2024, DOI:10.32604/cmc.2024.046890

Abstract The prevalence of smartphones is deeply embedded in modern society, impacting various aspects of our lives. Their versatility and functionalities have fundamentally changed how we communicate, work, seek entertainment, and access information. Among the many smartphones available, those operating on the Android platform dominate, being the most widely used type. This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years. Therefore, there is an urgent need to develop new methods for detecting Android malware. The literature contains numerous works related to Android malware detection. As far as our understanding… More >

Mamona Arshad¹, Ahmad Karim¹, Salman Naseer², Shafiq Ahmad³, Mejdal Alqahtani³, Akber Abid Gardezi⁴, Muhammad Shafiq^5,*, Jin-Ghoo Choi⁵

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2123-2135, 2023, DOI:10.32604/cmc.2022.028680

Abstract The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e., games applications, entertainment, online banking, social network sites, etc., and also allow the end users to perform a variety of activities. Because of activities, mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information, phishing, spamming, Distributed Denial of Services (DDoS), and malware dissemination. Botnet applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area. A… More >

Nida Aslam^1,*, Irfan Ullah Khan², Salma Abdulrahman Bader², Aisha Alansari³, Lama Abdullah Alaqeel², Razan Mohammed Khormy², Zahra Abdultawab AlKubaish², Tariq Hussain^4,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721

Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to classify Android applications into malware… More >

Madallah Alruwaili^1,*, Muhammad Hameed Siddiqi¹, Kamran Farid², Mohammad Azad¹, Saad Alanazi¹, Asfandyar Khan², Abdullah Khan²

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2293-2311, 2023, DOI:10.32604/csse.2023.040312

Abstract Numerous types of research on healthcare monitoring systems have been conducted for calculating heart rate, ECG, nasal/oral airflow, temperature, light sensor, and fall detection sensor. Different researchers have done different work in the field of health monitoring with sensor networks. Different researchers used built-in apps, such as some used a small number of parameters, while some other studies used more than one microcontroller and used senders and receivers among the microcontrollers to communicate, and outdated tools for study development. While no efficient, cheap, and updated work is proposed in the field of sensor-based health monitoring systems. Therefore, this study developed… More >

Thien-Phuc Doan, Ngoc-Tu Chau, Jungsoo Park, Souhwan Jung^*

Computer Systems Science and Engineering, Vol.46, No.3, pp. 3059-3073, 2023, DOI:10.32604/csse.2023.038664

Abstract Nowadays, with the significant growth of the mobile market, security issues on the Android Operation System have also become an urgent matter. Trusted execution environment (TEE) technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security. However, for Android, TEE technologies still contain restrictions and limitations. The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE. Another issue of hardware-based TEE is the cross-platform problem. Since every mobile device supports different TEE vendors, it becomes an obstacle for developers to migrate their trusted applications to other… More >

Younghoon Ban, Jeong Hyun Yi, Haehyun Cho^*

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555

Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their behaviors by conducting the familial… More >

Le Duc Thuan^1,2,*, Pham Van Huong², Hoang Van Hiep¹, Nguyen Kim Khanh¹

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2741-2759, 2023, DOI:10.32604/csse.2023.033420

Abstract Android malware has exploded in popularity in recent years, due to the platform’s dominance of the mobile market. With the advancement of deep learning technology, numerous deep learning-based works have been proposed for the classification of Android malware. Deep learning technology is designed to handle a large amount of raw and continuous data, such as image content data. However, it is incompatible with discrete features, i.e., features gathered from multiple sources. Furthermore, if the feature set is already well-extracted and sparsely distributed, this technology is less effective than traditional machine learning. On the other hand, a wide learning model can… More >

Munkhtsetseg¹, Jeongwook Seo^2,*

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2989-3003, 2023, DOI:10.32604/csse.2023.033342

Abstract In social science, health care, digital therapeutics, etc., smartphone data have played important roles to infer users’ daily lives. However, smartphone data collection systems could not be used effectively and widely because they did not exploit any Internet of Things (IoT) standards (e.g., oneM2M) and class labeling methods for machine learning (ML) services. Therefore, in this paper, we propose a novel Android IoT lifelog system complying with oneM2M standards to collect various lifelog data in smartphones and provide two manual and automated class labeling methods for inference of users’ daily lives. The proposed system consists of an Android IoT client… More >

Muhammad Waqar¹, Sabeeh Fareed¹, Ajung Kim^2,*, Saif Ur Rehman Malik³, Muhammad Imran¹, Muhammad Usman Yaseen¹

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4399-4415, 2023, DOI:10.32604/cmc.2023.032984

Abstract The Android Operating System (AOS) has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things (IoT). Due to the high popularity and reliability of AOS for IoT, it is a target of many cyber-attacks which can cause compromise of privacy, financial loss, data integrity, unauthorized access, denial of services and so on. The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are developed to conceal their malicious… More >

Lojain Nahhas¹, Marwan Albahar^1,*, Abdullah Alammari², Anca Jurcut³

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316

Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification and classification of Android malicious… More >