Ahsan Wajahat¹, Jingsha He¹, Nafei Zhu¹, Tariq Mahmood^2,3, Tanzila Saba², Amjad Rehman Khan², Faten S. Alamri^4,*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530

Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This… More >

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.… 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… 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… 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,… 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… More >

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2147-2163, 2023, DOI:10.32604/csse.2023.028227

Abstract Android devices are popularly available in the commercial market at different price levels for various levels of customers. The Android stack is more vulnerable compared to other platforms because of its open-source nature. There are many android malware detection techniques available to exploit the source code and find associated components during execution time. To obtain a better result we create a hybrid technique merging static and dynamic processes. In this paper, in the first part, we have proposed a technique to check for correlation between features and classify using a supervised learning approach to avoid… More >

K. Santosh Jhansi^1,2,*, P. Ravi Kiran Varma², Sujata Chakravarty³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6327-6345, 2022, DOI:10.32604/cmc.2022.030878

Abstract Malware Security Intelligence constitutes the analysis of applications and their associated metadata for possible security threats. Application Programming Interfaces (API) calls contain valuable information that can help with malware identification. The malware analysis with reduced feature space helps for the efficient identification of malware. The goal of this research is to find the most informative features of API calls to improve the android malware detection accuracy. Three swarm optimization methods, viz., Ant Lion Optimization (ALO), Cuckoo Search Optimization (CSO), and Firefly Optimization (FO) are applied to API calls using auto-encoders for identification of most influential More >

V. Joseph Raymond^1,2,*, R. Jeberson Retna Raj¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2413-2429, 2023, DOI:10.32604/iasc.2023.030527

Abstract In recent days the usage of android smartphones has increased extensively by end-users. There are several applications in different categories banking/finance, social engineering, education, sports and fitness, and many more applications. The android stack is more vulnerable compared to other mobile platforms like IOS, Windows, or Blackberry because of the open-source platform. In the Existing system, malware is written using vulnerable system calls to bypass signature detection important drawback is might not work with zero-day exploits and stealth malware. The attackers target the victim with various attacks like adware, backdoor, spyware, ransomware, and zero-day exploits… More >

Pagnchakneat C. Ouk¹, Wooguil Pak^2,*

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 381-398, 2022, DOI:10.32604/cmc.2022.024540

Abstract Although Android becomes a leading operating system in market, Android users suffer from security threats due to malwares. To protect users from the threats, the solutions to detect and identify the malware variant are essential. However, modern malware evades existing solutions by applying code obfuscation and native code. To resolve this problem, we introduce an ensemble-based malware classification algorithm using malware family grouping. The proposed family grouping algorithm finds the optimal combination of families belonging to the same group while the total number of families is fixed to the optimal total number. It also adopts… More >