相关论文: Exploring Runtime Evolution in Android: A Cross-Ve…

With the continuous growth in the usage of Android apps, ensuring their security has become critically important. An increasing number of malicious apps adopt anti-analysis techniques to evade security measures. Although some research has…

Android is an open software platform for mobile devices with a large market share in the smartphone sector. The openness of the system as well as its wide adoption lead to an increasing amount of malware developed for this platform. ANANAS…

It is well known that antivirus engines are vulnerable to evasion techniques (e.g., obfuscation) that transform malware into its variants. However, it cannot be necessarily attributed to the effectiveness of these evasions, and the limits…

The reliability of mobile devices is a challenge for vendors, since the mobile software stack has significantly grown in complexity. In this paper, we study how to assess the impact of faults on the quality of user experience in the Android…

A fundamental building block of a mobile application is the ability to persist program data between different invocations. Referred to as \emph{persistence}, this functionality is commonly implemented by means of persistence frameworks.…

Mobile phones have been playing a very significant role in our daily activities for the last decade. With the increase need for these devices, people are now more reliant on their smartphone applications for their daily tasks and many…

Using the evidence collection and analysis methodology for Android devices proposed by Martini, Do and Choo, we examined and analyzed seven popular Android cloud-based apps. Firstly, we analyzed each app in order to see what information…

Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around one million new malware samples per quarter, and it was reported…

Memory forensics is a powerful technique commonly adopted to investigate compromised machines and to detect stealthy computer attacks that do not store data on non-volatile storage. To employ this technique effectively, the analyst has to…

Machine learning (ML) in real-world systems must contend with concept drift, adversarial actors, and a spectrum of potential features with varying costs and benefits. Malware naturally exhibits all of these complexities, but for the same…

As in other cybersecurity areas, machine learning (ML) techniques have emerged as a promising solution to detect Android malware. In this sense, many proposals employing a variety of algorithms and feature sets have been presented to date,…

The increasing need for the examination of evidence from mobile and portable gadgets increases the essential need to establish dependable measures for the investigation of these gadgets. Many differences exist while detailing the…

Background. Evidence suggests that mobile applications are not thoroughly tested as their desktop counterparts. In particular GUI testing is generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e.…

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given…

Sophisticated malware families exploit the openness of the Android platform to infiltrate IoT networks, enabling large-scale disruption, data exfiltration, and denial-of-service attacks. This systematic literature review (SLR) examines…

This demo paper presents the technical details and usage scenarios of $\mu$SE: a mutation-based tool for evaluating security-focused static analysis tools for Android. Mutation testing is generally used by software practitioners to assess…

Dealing with the evolution of operating systems is challenging for developers of mobile apps, who have to deal with frequent upgrades that often include backward incompatible changes of the underlying API framework. As a consequence of…

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…

In the forensic field of digital technology, there has been a great deal of investigation into the decoding of navigation systems of the brand TomTom. As TomTom is the market leader in navigation systems, a large number of these devices are…

Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them,…