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Static detection technologies based on signature-based approaches that are widely used in Android platform to detect malicious applications. It can accurately detect malware by extracting signatures from test data and then comparing the…

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…

The amount of Android malware has increased greatly during the last few years. Static analysis is widely used in detecting such malware by analyzing the code without execution. The effectiveness of current tools relies on the app model as…

With the increasing user base of Android devices and advent of technologies such as Internet Banking, delicate user data is prone to be misused by malware and spyware applications. As the app developer community increases, the quality…

This paper introduces a malware detection system for smartphones based on studying the dynamic behavior of suspicious applications. The main goal is to prevent the installation of the malicious software on the victim systems. The approach…

The Android OS has become the most popular mobile operating system leading to a significant increase in the spread of Android malware. Consequently, several static and dynamic analysis systems have been developed to detect Android malware.…

A growing number of threats to Android phones creates challenges for malware detection. Manually labeling the samples into benign or different malicious families requires tremendous human efforts, while it is comparably easy and cheap to…

Android malware detection has been extensively studied using both traditional machine learning (ML) and deep learning (DL) approaches. While many state-of-the-art detection models, particularly those based on DL, claim superior performance,…

With the rapid advancement of machine learning (ML), ML-based Android malware detection has gained significant popularity due to its ability to automatically learn malicious patterns from Android apps. However, the lack of an in-depth and…

Machine learning based solutions have been successfully employed for automatic detection of malware on Android. However, machine learning models lack robustness to adversarial examples, which are crafted by adding carefully chosen…

AI methods have been proven to yield impressive performance on Android malware detection. However, most AI-based methods make predictions of suspicious samples in a black-box manner without transparency on models' inference. The expectation…

In the past decade, the cyber-crime related to mobile devices has increased. Mobile devices, especially the ones running on Android operating system are particularly interesting to malware creators, as the users often keep the biggest…

Hardware-based malware detectors (HMDs) are a key emerging technology to build trustworthy computing platforms, especially mobile platforms. Quantifying the efficacy of HMDs against malicious adversaries is thus an important problem. The…

Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy. Various system-based and third-party libraries for Android…

Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages malware developer to penetrate the market with malicious apps to annoy and…

There is an increase in global malware threats. To address this, an encryption-type ransomware has been introduced on the Android operating system. The challenges associated with malicious threats in phone use have become a pressing issue…

As mobile and smart connectivity continue to grow, malware presents a permanently evolving threat to different types of critical domains such as health, logistics, banking, and community segments. Different types of malware have dynamic…

The goal of this paper is to analyze the behavior and intent of recent types of privacy invasive Android adware. There are two recent trends in this area: more financial motives instead of ego motives, and the development of more dynamic…

The Android Open Source Project (AOSP) is probably the most used and customized operating system for smartphones and IoT devices worldwide. Its market share and high adaptability makes Android an interesting operating system for many…

Machine learning (ML) based approach is considered as one of the most promising techniques for Android malware detection and has achieved high accuracy by leveraging commonly-used features. In practice, most of the ML classifications only…