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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…

Android Permission Model and Application (app) analysis has consistently remained the focus of the investigation of research groups and stakeholders of the Android ecosystem since it was launched in 2008. Even though the Android smartphone…

Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated…

Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic status. Their results highlight that current security…

The popularity of Android system, not only in the handset devices but also in IoT devices, makes it a very attractive destination for malware. Indeed, malware is expanding at a similar rate targeting such devices that rely, in most cases,…

Vulnerabilities in Android smartphone chipsets have severe consequences, as recent real-world attacks have demonstrated that adversaries can leverage vulnerabilities to execute arbitrary code or exfiltrate confidential information. Despite…

Mobile health (mHealth) applications (apps) have gained significant popularity over the last few years due to its tremendous benefits, such as lowering healthcare cost and increasing patient awareness. However, the sensitivity of healthcare…

As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an…

Information hiding techniques are increasingly utilized by the current malware to hide its existence and communication attempts. In this paper we highlight this new trend by reviewing the most notable examples of malicious software that…

As mobile app usage continues to rise, so does the generation of extensive user interaction data, which includes actions such as swiping, zooming, or the time spent on a screen. Apps often collect a large amount of this data and claim to…

Security of mobile apps affects the security of their users. This has fueled the development of techniques to automatically detect vulnerabilities in mobile apps and help developers secure their apps; specifically, in the context of Android…

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,…

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for some device manufacturers to unveil the underlying malice within apps. We propose…

The advancements in the digitization world has revolutionized the automotive industry. Today's modern cars are equipped with internet, computers that can provide autonomous driving functionalities as well as infotainment systems that can…

The login functionality, being the gateway to app usage, plays a critical role in both user experience and application security. As Android apps increasingly incorporate login functionalities, they support a variety of authentication…

With the popularity of Android growing exponentially, the amount of malware has significantly exploded. It is arguably one of the most viral problems on mobile platforms. Recently, various approaches have been introduced to detect Android…

Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We…

Machine learning (ML) has gained significant adoption in Android malware detection to address the escalating threats posed by the rapid proliferation of malware attacks. However, recent studies have revealed the inherent vulnerabilities of…

Despite the growing threat posed by Android malware, the research community is still lacking a comprehensive view of common behaviors and trends exposed by malware families active on the platform. Without such view, the researchers incur…

This paper presents a multidisciplinary approach to analyzing data from Telegram for early warning information regarding cyber threats. With the proliferation of hacktivist groups utilizing Telegram to disseminate information regarding…