Catálogo de publicaciones - libros

Several policies initiatives around the digital economy stress on one side the centrality of smartphones and mobile applications, and on the other call for attention on the threats to which this ecosystem is exposed to. Lately, a plethora of related works rely on machine learning algorithms to classify whether an application is malware or not, using data that can be extracted from the application itself with high accuracy. However, different parameters can influence machine learning effectiveness. Thus, in this paper we focus on validating the efficiency of such approaches in detecting malware for Android platform, and identifying the optimal characteristics that should be consolidated in any similar approach. To do so, we built a machine learning solution based on features that can be extracted by static analysis of any Android application, such as activities, services, broadcasts, receivers, intent categories, APIs, and permissions. The extracted features are analyzed using statistical analysis and machine learning algorithms. The performance of different sets of features are investigated and compared. The analysis shows that under an optimal configuration an accuracy up to 97% can be obtained.

Many emerging Internet of Things devices, gateways and networks, rely either on mobile networks or on Internet Protocols to support their connectivity. However it is known that both types of networks are susceptible to different types of attacks that can significantly disrupt their operations. In particular 3rd and 4th generation mobile networks experience signalling related attacks, such as signalling storms, that have been a common problem in the last decade. This paper presents a generic model of a mobile network that includes different end user behaviours, including possible attacks to the signalling system. We then suggest two attack detection mechanisms, and evaluate them by analysis and simulation based on the generic mobile network model. Our findings suggest that mobile networks can be modified to be able to automatically detect attacks. Our results also suggest that attack mitigation can be carriedout both via the signalling system and on a “per mobile terminal” basis.

Software security is a matter of major concern for software development enterprises that wish to deliver highly secure software products to their customers. Static analysis is considered one of the most effective mechanisms for adding security to software products. The multitude of static analysis tools that are available provide a large number of raw results that may contain security-relevant information, which may be useful for the production of secure software. Several mechanisms that can facilitate the production of both secure and reliable software applications have been proposed over the years. In this paper, two such mechanisms, particularly the vulnerability prediction models (VPMs) and the optimum checkpoint recommendation (OCR) mechanisms, are theoretically examined, while their potential improvement by using static analysis is also investigated. In particular, we review the most significant contributions regarding these mechanisms, identify their most important open issues, and propose directions for future research, emphasizing on the potential adoption of static analysis for addressing the identified open issues. Hence, this paper can act as a reference for researchers that wish to contribute in these subfields, in order to gain solid understanding of the existing solutions and their open issues that require further research.