A Survey on Security for Mobile Devices

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has signicantly increased due to the dierent form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research eld is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011. We focus on high-level attacks, such those to user applications, through SMS/MMS, denial-of-service, overcharging and privacy. We group existing approaches aimed at protecting mobile devices against these classes of attacks into dierent categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach

Schutz der Privatsphäre in kontext- und ortsbezogenen Diensten

Mit der immensen Verbreitung von Smartphones als leistungsstarke, mobile Endgeräte nimmt auch die Nutzung kontext- und insbesondere ortsbezogener Dienste stetig zu. Derartige Anwendungen vereinfachen die Interaktion mit dem eigenen Endgerät oder externen Systemen, ermöglichen neuartige Nutzungserlebnisse und innovative Dienste, die auf den aktuellen Nutzungskontext zugeschnitten sind. Bei einem Großteil der hierfür an Dritte kommunizierten Informationen handelt es sich jedoch um persönliche Daten, deren unkontrollierte Herausgabe aus Sicht der Privatsphäre problematisch erscheint. In der vorliegenden Arbeit werden drei unterschiedliche Möglichkeiten zum Datenschutz von Kontextinformationen vorgestellt. Allen Verfahren ist gemein, dass sie im Gegensatz zu vielen bestehenden Systemen ohne die Existenz einer als vertrauenswürdig deklarierten dritten Partei auskommen. Stattdessen wird jeweils eine rein clientseitige Durchsetzung von Privatsphärepräferenzen angestrebt, wodurch eine personalisierte Dienstnutzung ermöglicht und die Gefahr eines zentralen Datenlecks vermieden wird. Der erste Ansatz beschäftigt sich damit, dem Benutzer ein effektives, allgemeingültiges Werkzeug zur feingranularen, situations- und rezipientenabhängigen Verwaltung von Kontextinformationen zur Verfügung zu stellen. Es wird ein Ontologie-basiertes Kontextmodell entwickelt, auf dessen Grundlage die Definition und konsistente Durchsetzung situationsabhängiger Freigaberegeln möglich ist. Zudem wird eine vollständige Systemarchitektur zur Kontextverwaltung sowie deren Integration in ein mobiles Betriebssystem beschrieben. Der zweite Ansatz ermöglicht die privatsphäreschonende Umsetzung der verkehrsadaptiven Online-Routenplanung. Unter Verwendung standardmäßig zur Verfügung stehender Dienstschnittstellen wird dafür gesorgt, dass keine externe Komponente den exakten Start- und Zielpunkt einer Routenanfrage in Erfahrung bringen kann. Anhand einer umfangreichen Evaluation werden der Trade-Off zwischen Privatsphäre, Kommunikationsaufwand und Dienstqualität untersucht und verschiedene Optimierungsmöglichkeiten aufgezeigt. Als drittes wird ein umfassendes Konzept zur Herstellung von Standortanonymität vorgestellt, das sich generisch für die privatsphärekonforme Positionsfreigabe in unterschiedlichen Ausprägungen ortsbezogener Dienste eignet. Hierfür werden die topologiebasierte Erstellung k-anonymer Verschleierungszonen sowie verschiedene Freigabestrategien entwickelt, die auch die zeitliche Korrelation aufeinanderfolgender Ortsangaben berücksichtigen. Dies ermöglicht den effektiven Schutz persönlicher Daten selbst bei kontinuierlichen Positionsupdates gegenüber einem Angreifer mit umfangreichem Kartenwissen.With the widespread prevalence of smartphones as powerful ultra-mobile devices, also the usage of context-aware applications and location-based services continually grows. Such applications improve the way a user interacts with his own device and external systems. Furthermore, they enable previously unknown user experiences and offer innovative services tailored to the user's current situation. The majority of context information that has to be communicated to external parties in order to use such services, however, is considered personal data. From a privacy oriented perspective, the release of this kind of information hence has to be controlled and leakage must be prevented. This work presents three different means for protecting a user's context information. In contrast to many existing approaches, each of the proposed systems has been designed to operate without the existence of an omniscient, trusted third party acting as an anonymizer. Instead, enforcement of a user's privacy preferences is executed locally on the user's device, which allows for personalized services and avoids the perils of a central privacy bottleneck. The first approach proposes an effective and generally applicable tool allowing the user to manage his context information in a fine-grained, context-aware and recipient-dependent way. To this end, a new ontology-based context model will be developed, which forms the foundation for the definition and assertion of situation-dependent access control rules set up by the user. Additionally, the overall system architecture as well as its integration into a modern mobile operating system will be described. The second approach presents a client-side implementation for using traffic-adaptive online route planning services in a privacy-preserving manner. Only using the unmodified standard query interfaces of existing services, the system assures that no external party is able to learn the exact endpoints of the user's route request. By means of empirical evaluation on the actual road network, the trade off between privacy, communication overhead, and quality of service will be analyzed. Also, different optimizations will be discusssed. Thirdly, a holistic concept for continuously protecting a user's location privacy will be presented, which is generally applicable to the release of location information and all different kinds of location-based services. A topology-aware creation of k-anonymous cloaking regions will be developed as well as different strategies for the release of location information, which also take into account the spatiotemporal correlation of successive location updates. These allow for an effective protection of a user's location privacy even for continuous location updates and in face of a strong attacker with extensive map knowledge

Forensic Tracking and Surveillance

Digital forensics is an emerging field that has uniquely brought together academics, practitioners and law enforcement. Research in this area was inspired by the numerous challenges posed by the increased sophistication of criminal tools. Traditionally, digital forensics has been confined to the extraction of digital evidence from electronic devices. This direct extraction of digital evidence, however, no longer suffices. Indeed, extracting completely raw data without further processing and/or filtering is, in some cases, useless. These problems can be tackled by the so-called ``computational forensics" where the reconstructs evidence are undertaken further processing. One important application of computational forensics is criminal tracking, which we collectively call ``forensic tracking" and is the main subject of this thesis. This thesis adopts an algorithmic approach to investigate the feasibility of conducting forensic tracking in various environments and settings. Unlike conventional tracking, forensic tracking has to be passive such that the target (who is usually a suspect) should not be aware of the tracking process. We begin by adopting pedestrian setting and propose several online (real-time) forensic tracking algorithms to track a single or multiple targets passively. Beside the core tracking algorithms, we also propose other auxiliary algorithms to improve the robustness and resilience of tracking. We then extend the scope and consider vehicular forensic tracking, where we investigate both online and offline tracking. In online vehicular tracking, we also propose algorithms for motion prediction to estimate the near future movement of target vehicles. Offline vehicular tracking, on the other hand, entails the post-hoc extraction and probabilistic reconstruction of vehicular traces, which we adopt Bayesian approach for. Finally, the contributions of the thesis concludes with building an algorithmic solution for multi-modal tracking, which is a mixed environment combining both pedestrian and vehicular settings