Analysis and evaluation of SafeDroid v2.0, a framework for detecting malicious Android applications

Android smartphones have become a vital component of the daily routine of millions of people, running a plethora of applications available in the official and alternative marketplaces. Although there are many security mechanisms to scan and filter malicious applications, malware is still able to reach the devices of many end-users. In this paper, we introduce the SafeDroid v2.0 framework, that is a flexible, robust, and versatile open-source solution for statically analysing Android applications, based on machine learning techniques. The main goal of our work, besides the automated production of fully sufficient prediction and classification models in terms of maximum accuracy scores and minimum negative errors, is to offer an out-of-the-box framework that can be employed by the Android security researchers to efficiently experiment to find effective solutions: the SafeDroid v2.0 framework makes it possible to test many different combinations of machine learning classifiers, with a high degree of freedom and flexibility in the choice of features to consider, such as dataset balance and dataset selection. The framework also provides a server, for generating experiment reports, and an Android application, for the verification of the produced models in real-life scenarios. An extensive campaign of experiments is also presented to show how it is possible to efficiently find competitive solutions: the results of our experiments confirm that SafeDroid v2.0 can reach very good performances, even with highly unbalanced dataset inputs and always with a very limited overhead

SafeDroid: A Distributed Malware Detection Service for Android

Android platform has become a primary target for malware. In this paper we present SafeDroid, an open source distributed service to detect malicious apps on Android by combining static analysis and machine learning techniques. It is composed by three micro-services, working together, combining static analysis and machine learning techniques. SafeDroid has been designed as a user friendly service, providing detailed feedback in case of malware detection. The detection service is optimized to be lightweight and easily updated. The feature set on which the micro-service of detection relies on on has been selected and optimized in order to focus only on the most distinguishing characteristics of the Android apps. We present a prototype to show the effectiveness of the detection mechanism service and the feasibility of the approach

Impact assessment of the catastrophic earthquakes of 6 February 2023 in Turkey and Syria via the exploitation of satellite datasets

Turkey due to its location within the collision zone between the Eurasian, African and Arabian Plates, is a region prone to earthquakes. The country mostly lies on the Anatolian micro-plate, bounded by two major strike-slip fault zones, i.e., the North and the East Anatolian Fault. On 6 February 2023, the activation of a large segment of the East Anatolian Fault generated two earthquakes of 7.8 and 7.5 magnitude, in southern Turkey. The seismic risk is greater along the plate boundaries, however due to the frequency of earthquake occurrence throughout Turkey, detailed seismic risk maps are crucial and need to be continuously updated towards operational purposes, and as the optimal means towards decision making for disaster risk reduction. Extensive Synthetic Aperture Radar (SAR) satellite image analysis was performed to determine ground displacements caused by the seismic sequence in the wider area around the two epicenters. Pre-seismic line of sight displacements, as well as co-seismic deformation, were estimated, providing critical information about the surface rupture and the overall ground deformation in the affected areas. Earthquakes can induce landslides and other ground displacements causing extensive damage to buildings and infrastructure. Therefore, optical (e.g., Sentinel-2, PlanetScope) and SAR (Sentinel-1) imagery were exploited as a useful tool for assessing the impact of earthquakes on the ground. The monitoring and mapping of these changes, in conjunction with SAR analysis, as well as information on building infrastructure and population density, highlight the overall damage assessment in the region, thus, allowing a better understanding of the impact of earthquakes while providing a more effective response and recovery efforts for decision makers and local authorities towards disaster risk reduction