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Over the past years, literature has shown that attacks exploiting the microarchitecture of modern processors pose a serious threat to the privacy of mobile phone users. This is because applications leave distinct footprints in the…
Business processes are bound to evolve as a form of adaption to changes, and such changes are referred as process drifts. Current process drift detection methods perform well on clean event log data, but the performance can be tremendously…
Data provenance is a valuable tool for detecting and preventing cyber attack, providing insight into the nature of suspicious events. For example, an administrator can use provenance to identify the perpetrator of a data leak, track an…
The popularity of the Mobile Database is increasing day by day as people need information even on the move in the fast changing world. This database technology permits employees using mobile devices to connect to their corporate networks,…
Mobile apps are predominantly integrated with cloud services to benefit from enhanced functionalities. Adopting authentication using secrets such as API keys is crucial to ensure secure mobile-cloud interactions. However, developers often…
This paper proposes a novel visual model for web applications security monitoring. Although an automated intrusion detection system can shield a web application from common attacks, it usually cannot detect more complicated break-ins. So, a…
Powered by the rising popularity of deep learning techniques on smartphones, on-device deep learning models are being used in vital fields like finance, social media, and driving assistance. Because of the transparency of the Android…
Many Android applications collect data from users. The European Union's General Data Protection Regulation (GDPR) requires vendors to faithfully disclose which data their apps collect. This task is complicated because many apps use…
Securing browsers in mobile devices is very challenging, because these browser apps usually provide browsing services to other apps in the same device. A malicious app installed in a device can potentially obtain sensitive information…
This work investigates how to monitor access to Android zero-permission sensors which could cause privacy leakage to users. Moreover, monitoring such sensitive access allows security researchers to characterize potential sensor abuse…
Android's open-source nature facilitates widespread smartphone accessibility, particularly in price-sensitive markets. System and vendor applications that come pre-installed on budget Android devices frequently operate with elevated…
Mobile applications (apps) have become deeply personal, constantly demanding access to privacy-sensitive information in exchange for more personalized user experiences. Such privacy-invading practices have generated major multidimensional…
Deep learning has emerged as a promising technology for achieving Android malware detection. To further unleash its detection potentials, software visualization can be integrated for analyzing the details of app behaviors clearly. However,…
Many Android applications collect data from users. When they do, they must protect this collected data according to the current legal frameworks. Such data protection has become even more important since the European Union rolled out the…
Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning algorithms must not only be able to handle massive volumes of rapidly arriving data, but also adapt…
This paper reviews work published between 2002 and 2022 in the fields of Android malware, clone, and similarity detection. It examines the data sources, tools, and features used in existing research and identifies the need for a…
In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first derive a model to measure the perceived privacy risk based on…
Given a stream of entries over time in a multi-dimensional data setting where concept drift is present, how can we detect anomalous activities? Most of the existing unsupervised anomaly detection approaches seek to detect anomalous events…
Despite the continued research and progress in building secure systems, Android applications continue to be ridden with vulnerabilities, necessitating effective detection methods. Current strategies involving static and dynamic analysis…
The Android operating system has become the most popular operating system for smartphones and tablets leading to a rapid rise in malware. Sophisticated Android malware employ detection avoidance techniques in order to hide their malicious…