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As the smartphone market leader, Android has been a prominent target for malware attacks. The number of malicious applications (apps) identified for it has increased continually over the past decade, creating an immense challenge for all…

Mobile application developers are required to disclose how they collect, use, and share user data in compliance with privacy regulations. To support transparency, major app marketplaces have introduced standardized disclosure mechanisms. In…

密码学与安全 · 计算机科学 2026-04-20 Luca Ferrari , Billel Habbati , Meriem Guerar , Mariano Ceccato , Luca Verderame

Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a…

密码学与安全 · 计算机科学 2026-05-29 Daniel Pulido-Cortázar , Daniel Gibert , Felip Manyà

Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning(ML) techniques have been shown to be effective at detecting malware for Android, a comprehensive…

Although there are over 1,600,000 third-party Android apps in the Google Play Store, little has been conclusively shown about how their individual (and collective) permission usage has evolved over time. Recently, Android 6 overhauled the…

密码学与安全 · 计算机科学 2016-08-11 Vincent F. Taylor , Ivan Martinovic

Resource leak bugs in Android apps are pervasive and can cause serious performance degradation and system crashes. In recent years, several resource leak detection techniques have been proposed to assist Android developers in correctly…

软件工程 · 计算机科学 2016-11-28 Yepang Liu , Lili Wei , Chang Xu , Shing-Chi Cheung

Due to Android's open source feature and low barriers to entry for developers, millions of developers and third-party organizations have been attracted into the Android ecosystem. However, over 90 percent of mobile malware are found…

密码学与安全 · 计算机科学 2019-06-26 Bin Zhao

Android is the most popular operating system with billions of active devices. Unfortunately, its popularity and openness makes it attractive for unwanted apps, i.e., malware and potentially unwanted programs (PUP). In Android, app…

密码学与安全 · 计算机科学 2020-10-21 Platon Kotzias , Juan Caballero , Leyla Bilge

For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the…

密码学与安全 · 计算机科学 2019-09-10 Ji Wang , Qi Jing , Jianbo Gao

The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper…

密码学与安全 · 计算机科学 2016-07-28 Suleiman Y. Yerima , Sakir Sezer , Igor Muttik

Human values such as integrity, privacy, curiosity, security, and honesty are guiding principles for what people consider important in life. Such human values may be violated by mobile software applications (apps), and the negative effects…

软件工程 · 计算机科学 2022-03-16 Humphrey O. Obie , Idowu Ilekura , Hung Du , Mojtaba Shahin , John Grundy , Li Li , Jon Whittle , Burak Turhan

It is well-known that Android malware constantly evolves so as to evade detection. This causes the entire malware population to be non-stationary. Contrary to this fact, most of the prior works on Machine Learning based Android malware…

密码学与安全 · 计算机科学 2017-07-07 Annamalai Narayanan , Mahinthan Chandramohan , Lihui Chen , Yang Liu

With the rapid technological advancement, security has become a major issue due to the increase in malware activity that poses a serious threat to the security and safety of both computer systems and stakeholders. To maintain stakeholders,…

Stack Overflow incentive system awards users with reputation scores to ensure quality. The decentralized nature of the forum may make the incentive system prone to manipulation. This paper offers, for the first time, a comprehensive study…

软件工程 · 计算机科学 2024-08-02 Iren Mazloomzadeh , Gias Uddin , Foutse Khomh , Ashkan Sami

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A…

密码学与安全 · 计算机科学 2016-08-03 Suleiman Y. Yerima , Sakir Sezer , Gavin McWilliams , Igor Muttik

Since Android has become a popular software platform for mobile devices recently; they offer almost the same functionality as personal computers. Malwares have also become a big concern. As the number of new Android applications tends to be…

密码学与安全 · 计算机科学 2020-06-05 Muhammad Zuhair Qadir , Atif Nisar Jilani , Hassam Ullah Sheikh

The rise in popularity of the Android platform has resulted in an explosion of malware threats targeting it. As both Android malware and the operating system itself constantly evolve, it is very challenging to design robust malware…

We report the findings of a reimplementation of 18 foundational studies in feature-based machine learning for Android malware detection, published during the period 2013-2023. These studies are reevaluated on a level playing field using a…

机器学习 · 计算机科学 2026-01-22 Ali Muzaffar , Hani Ragab Hassen , Hind Zantout , Michael A Lones

Third-party security apps are an integral part of the Android app ecosystem. Many users install them as an extra layer of protection for their devices. There are hundreds of such security apps, both free and paid in Google Play Store and…

密码学与安全 · 计算机科学 2020-07-09 Weixian Yao , Yexuan Li , Weiye Lin , Tianhui Hu , Imran Chowdhury , Rahat Masood , Suranga Seneviratne

In this paper, we seek to better understand Android obfuscation and depict a holistic view of the usage of obfuscation through a large-scale investigation in the wild. In particular, we focus on four popular obfuscation approaches:…

密码学与安全 · 计算机科学 2018-01-08 Shuaike Dong , Menghao Li , Wenrui Diao , Xiangyu Liu , Jian Liu , Zhou Li , Fenghao Xu , Kai Chen , Xiaofeng Wang , Kehuan Zhang