Both data ferrying with disruption-tolerant networking (DTN) and mobile cellular base stations constitute important techniques for UAV-aided communication in situations of crises where standard communication infrastructure is unavailable. For optimal use of a limited number of UAVs, we propose providing both DTN and a cellular base station on each UAV. Here, DTN is used for large amounts of low-priority data, while capacity-constrained cell coverage remains reserved for emergency calls or command and control. We optimize cell coverage via a novel optimal transport-based formulation using alternating minimization, while for data ferrying we periodically deliver data between dynamic clusters by solving quadratic assignment problems. In our evaluation, we consider different scenarios with varying mobility models and a wide range of flight patterns. Overall, we tractably achieve optimal cell coverage under quality-of-service costs with DTN-based data ferrying, enabling large-scale deployment of UAV swarms for crisis communication.

Unmanned Aerial Vehicles (UAVs) are already part of everyday life as they are commonly used for disaster response, industrial applications, and smart farming. Resilient and flexible communication is key for the operation of UAVs themselves as well as many applications realized using them, e.g., gathering of data from Internet of Things (IoT) sensors without a direct uplink. Realizing new ideas, optimizations, and what-if analyses are usually lengthy processes to get from theoretical models to the actual real-world implementation. We propose an integrated testbed using emulation, where actual implementations can be evaluated in virtual environments that also simulate the network connectivity. Through the use of a software stack similar to actual UAVs that is built around Linux and the PX4 flight controller, we allow realistic prototyping, automated experiments and interactive missions. Additionally, we enable Hardware-inthe-Loop integration through a wideband Software Defined Radio (SDR)-based channel emulator to experiment with new radio links for more robust UAV control in challenging environments.

The usage of WebAssembly (Wasm) is not only increasing in the web browser, but also as a backend technology on servers. Since Wasm introduces several security issues, like the possibility to obfuscate malicious code and cryptomining, an adequate analysis framework is needed for creating analyses that reveal such issues. Existing state-of-the-art analysis approaches lack in soundness, in fully providing essential information to client analyses, or entail a considerable amount of overhead due to their dynamic nature. To meet this challenge, we developed WasmA a static analysis framework for WebAssembly that determines necessary information needed by static client analyses, like call, control-, and data-flow graphs. In the evaluation we show that WasmA is performant, generic and extensible and thus competitive in comparison to state-of-the art tools. The implementation of a cryptominer detection tool on top of WasmA shows its applicability. WasmA is able to provide the required functionality while having a comparative resource-efficient approach, and as a result WasmA outperforms the state of the art.

In this paper, we present a continuous-time current-mode $\Sigma\triangle$ modulator (CT C-SDM) with finite impulse response (FIR) feedback to increase clock jitter robustness while preserving the anti-aliasing filtering (AAF) property of a CT modulator. The modulator also features a newly proposed DC servo loop (DSL) that extends the modulator’s dynamic range in applications with a large DC bias current. This improvement breaks the intrinsic tradeoff between dynamic range and quantization noise floor of C-SDM for applications which require the digitization of AC currents in the presence of strong DC bias currents. The proposed modulator architecture has been manufactured in 180nm CMOS, and consumes 64mW from a 3. 3V supply. The chip’s measured peak SNR and SNDR are 77 dB and 76.8 dB, respectively. The modulator can accommodate AC signal currents up to 137$\mu \mathrm{A}_{\mathrm{p}\mathrm{p}}$ and features an integrated noise of 6.9 n$\mathrm{A}_{\mathrm{r}\mathrm{m}\mathrm{s}}$ over a 200 kHz bandwidth, corresponding to a noise floor of 15 pA/$\sqrt{\mathrm{H}\mathrm{z}}$. An off-chip implementation of the proposed DSL verifies its positive effect on the modulator’s dynamic range (DR) without deteriorating the modulator’s noise performance and/or stability properties.

Recent studies have revealed that 87 % to 96 % of the Android apps using cryptographic APIs have a misuse which may cause security vulnerabilities. As previous studies did not conduct a qualitative examination of the validity and severity of the findings, our objective was to understand the findings in more depth. We analyzed a set of 936 open-source Java applications for cryptographic misuses. Our study reveals that 88.10 % of the analyzed applications fail to use cryptographic APIs securely. Through our manual analysis of a random sample, we gained new insights into effective false positives. For example, every fourth misuse of the frequently misused JCA class MessageDigest is an effective false positive due to its occurrence in a non-security context. As we wanted to gain deeper insights into the security implications of these misuses, we created an extensive vulnerability model for cryptographic API misuses. Our model includes previously undiscussed attacks in the context of cryptographic APIs such as DoS attacks. This model reveals that nearly half of the misuses are of high severity, e.g., hard-coded credentials and potential Man-in-the-Middle attacks.

During disasters, existing telecommunication infrastructures are often congested or even destroyed. In these situations, mobile devices can form a backup communication network for civilians and emergency services using disruption-tolerant networking (DTN) principles. Unfortunately, such distributed and resource-constrained networks are particularly susceptible to a wide range of attacks such as terrorists trying to cause more harm. In this article, we presentRESCUE, a resilient and secure device-to-device communication framework for emergency scenarios that provides comprehensive protection against common attacks.RESCUEfeatures a minimalistic DTN protocol that, by design, is secure against notable attacks such as routing manipulations, dropping, message manipulations, blackholing, or impersonation. To further protect against message flooding and Sybil attacks, we present a twofold mitigation technique. First, a mobile and distributed certificate infrastructure particularly tailored to the emergency use case hinders the adversarial use of multiple identities. Second, a message buffer management scheme significantly increases resilience against flooding attacks, even if they originate from multiple identities, without introducing additional overhead. Finally, we demonstrate the effectiveness ofRESCUEvia large-scale simulations in a synthetic as well as a realistic natural disaster scenario. Our simulation results show thatRESCUEachieves very good message delivery rates, even under flooding and Sybil attacks.

Background: Previous studies have shown that up to 99.59 % of the Java apps using crypto APIs misuse the API at least once. However, these studies have been conducted on Java and C, while empirical studies for other languages are missing. For example, a controlled user study with crypto tasks in Python has shown that 68.5 % of the professional developers write a secure solution for a crypto task. Aims: To understand if this observation holds for real-world code, we conducted a study of crypto misuses in Python. Method: We developed a static analysis tool that covers common misuses of 5 different Python crypto APIs. With this analysis, we analyzed 895 popular Python projects from GitHub and 51 MicroPython projects for embedded devices. Further, we compared our results with the findings of previous studies. Results: Our analysis reveals that 52.26 % of the Python projects have at least one misuse. Further, some Python crypto libraries' API design helps developers from misusing crypto functions, which were much more common in studies conducted with Java and C code. Conclusion: We conclude that we can see a positive impact of the good API design on crypto misuses for Python applications. Further, our analysis of MicroPython projects reveals the importance of hybrid analyses.

String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code. In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing. We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.

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 demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.

The Go programming language aims to provide memory and thread safety through measures such as automated memory management with garbage collection and a strict type system. However, it also offers a way of circumventing this safety net through the use of the unsafe package. While there are legitimate use cases for unsafe, developers must exercise caution to avoid introducing vulnerabilities like buffer overflows or memory corruption in general. In this work, we present go-geiger, a novel tool for Go developers to quantify unsafe usages in a project's source code and all of its dependencies. Using go-geiger, we conducted a study on the usage of unsafe in the top 500 most popular open-source Go projects on GitHub, including a manual analysis of 1,400 code samples on how unsafe is used. From the projects using Go's module system, 38% directly contain at least one unsafe usage, and 91% contain at least one unsafe usage in the project itself or one of its transitive dependencies. Based on the usage patterns found, we present possible exploit vectors in different scenarios. Finally, we present go-safer, a novel static analysis tool to identify dangerous and common usage patterns that were previously undetected with existing tools.

Computation offloading is often used in mobile cloud, edge, and/or fog computing to cope with resource limitations of mobile devices in terms of computational power, storage, and energy. Computation offloading is particularly challenging in situations where network connectivity is intermittent or error-prone. In this paper, we present OPPLOAD, a novel framework for offloading computational workflows in opportunistic networks. The individual tasks forming a workflow can be assigned to particular remote execution platforms (workers) either preselected ahead of time or decided just in time where a matching worker will automatically be assigned for the next task. Tasks are only assigned to capable workers that announce their capabilities. Furthermore, tasks of a workflow can be executed on multiple workers that are automatically selected to balance the load. Our Python implementation of OPPLOAD is publicly available as open source software. The results of our experimental evaluation demonstrate the feasibility of our approach.

Information-centric disruption-tolerant networks (ICN-DTNs) are useful to re-establish mobile communication in disaster scenarios when telecommunication infrastructures are partially or completely unavailable. In this paper, we present opportunistic named functions, a novel approach to operate ICN-DTNs during emergencies. Affected people and first responders use their mobile devices to specify their interests in particular content and/or application-specific functions that are then executed in the network on the fly, either partially or totally, in an opportunistic manner. Opportunistic named functions rely on user-defined interests and on locally optimal decisions based on battery lifetimes and device capabilities. In the presented emergency scenario, they are used to preprocess, analyze, integrate and transfer information extracted from images produced by smartphone cameras, with the aim of supporting the search for missing persons and the assessment of critical conditions in a disaster area. Experimental results show that opportunistic named functions reduce network congestion and improve battery lifetime in a network of battery-powered sensors, mobile devices, and mobile routers, while delivering crucial information to carry out situation analysis in disasters.

In this paper, we present a holistic approach to provide fault tolerance for distributed reactive programming. Our solution automatically stores and recovers program state to handle crashes, automatically updates and shares distributed parts of the state to provide eventual consistency, and handles errors in a fine-grained manner to allow precise manual control when necessary. By making use of the reactive programming paradigm, we provide these mechanisms without changing the behavior of existing programs and with reasonable performance, as indicated by our experimental evaluation.

Responsive applications such as augmented reality require nearby computational offloading units with low latency. The concept of cloudlets is one promising approach that satisfies these requirements. However, due to their wireless range restrictions cloudlets have always been faced with deployment issues of achieving high spatial coverage. In this paper, we look at the coverage issue from an end-user's perspective instead of an established provider perspective: we first argue that temporal coverage of cloudlet accessibility is preferable to spatial coverage for an end- user's experience considering his daily mobility behavior. Next, we investigate what is necessary to achieve a high temporal coverage for an individual user and to what extent is temporal coverage realizable with concepts like router- based cloudlets. To show our hypothesis and understanding the temporal coverage aspect, we collected two comprehensive datasets, an access points dataset with estimated location information and a human mobility dataset consisting of mobility traces from 30 participants within a major city over 4 weeks. Our analysis results show that high temporal coverage can be achieved by a relatively small set of router-based cloudlets since students mainly stay at two places, their homes and university, which represent a large part of the temporal coverage. The remaining rate at which coverage increases heavily depends on the user's mobility patterns. Our findings can be used to place router-based cloudlets at the right locations and estimate the number needed to achieve a certain temporal coverage in urban environments.

In this paper, we present MiniWorld, a novel distributed network emulator. It is based on full virtualization using QEMU/KVM, offers three network backends for emulating both wired and wireless communication, and provides several mobility patterns as well as distance-based link quality models. A snapshot boot mode is offered for accelerated booting of identical environments and repeating emulation runs. To decrease runtimes, MiniWorld supports distributed emulation across multiple computers, based on a resource-aware virtual machine (VM) scheduler. Experimental results demonstrate the performance of MiniWorld with respect to VM boot times, network bandwidth, round trip times, and topology switching times.

To cope with the varying delay and bandwidth requirements of today's mobile applications, mobile wireless networks can profit from classifying and predicting mobile application traffic. State-of-the-art traffic classification approaches have various disadvantages: port-based classification methods can be circumvented by choosing non-standard ports, protocol fingerprinting can be confused by the use of encryption, and current supervised learning methods for analyzing the statistical properties of network flows try to detect predefined classes, such as e-mail or FTP traffic, learned during training. In this paper, we present a novel approach to unsupervised traffic flow classification using statistical properties of flows and clustering based on a neural auto encoder. A novel time interval based feature vector construction and a semi-automatic cluster labeling method facilitate traffic flow classification independent of known traffic classes. An experimental evaluation on real data captured over a period of four months is presented. The obtained results show that 7 different classes of mobile traffic flows are detected with an average precision of 80% and an average recall of 75%.

During disasters, existing telecommunication infrastructures are often congested or even destroyed. In these situations, mobile devices can be interconnected using wireless ad hoc and disruption-tolerant networking to establish a backup emergency communication system for civilians and emergency services. However, such communication systems entail serious security risks, since adversaries may attempt to steal confidential data, fake notifications of emergency services, or perform denial-of-service (DoS) attacks. In this paper, we present SEDCOS, a secure device-to-device communication system for disaster scenarios. SEDCOS allows new users to join the network during disasters, mitigates flooding DoS attacks, and offers role revocation for detected adversaries to withdraw their permissions and exclude them from group communication. SEDCOS mitigates flooding DoS attacks and offers role revocation for detected adversaries to withdraw their permissions and exclude them from group communication. SEDCOS mitigates flooding DoS attacks and offers role revocation for detected adversaries to withdraw their permissions. We demonstrate the effectiveness of SEDCOS by large-scale network simulations.

CavBase is a database containing information about the three-dimensional geometry and the physicochemical properties of putative protein binding sites. Analyzing CavBase data typically involves computing the similarity of pairs of binding sites. In contrast to sequence alignment, however, a structural comparison of protein binding sites is a computationally challenging problem, making large scale studies difficult or even infeasible. One possibility to overcome this obstacle is to precompute pairwise similarities in an all-against-all comparison, and to make these similarities subsequently accessible to data analysis methods. Pairwise similarities, once being computed, can also be used to equip CavBase with a neighborhood structure. Taking advantage of this structure, methods for problems such as similarity retrieval can be implemented efficiently. In this paper, we tackle the problem of performing an all-against-all comparison using CavBase, consisting of more than 200,000 protein cavities, by means of parallel computation and cloud computing techniques. We present the conceptual design and technical realization of a large-scale study to create a similarity database called CavSimBase. We illustrate how CavSimBase is constructed, is accessed, and is used to answer biological questions by data analysis and similarity retrieval.

Ensuring the software quality of mobile applications with respect to performance, robustness, energy consumption, security and privacy is an important problem for a growing researcher and developer community. In this paper, we present Dynalize, a Platform-as-a-Service cloud for the dynamic analysis of mobile applications. It allows researchers and developers to investigate mobile applications at runtime in a virtual device cloud and to publish the performed analyses as web services. In contrast to existing approaches, it makes use of container virtualization on top of Infrastructure-as-a-Service instances, enabling dynamic provisioning and fast deployment of dynamic analyses. A custom container layout and a novel storage solution on the virtual server layer ensures cost- and runtime-efficient large-scale analyses of thousands of apps. The applicability of Dynalize is demonstrated by a security analysis of about 6,000 Android applications. Experiments on container startup, virtual device to container throughput and different storage back ends show the feasibility of the proposed approach.

Many Android apps have a legitimate need to communicate over the Internet and are then responsible for protecting potentially sensitive data during transit. This paper seeks to better understand the potential security threats posed by benign Android apps that use the SSL/TLS protocols to protect data they transmit. Since the lack of visual security indicators for SSL/TLS usage and the inadequate use of SSL/TLS can be exploited to launch Man-in-the-Middle (MITM) attacks, an analysis of 13,500 popular free apps downloaded from Google's Play Market is presented.

The commercial success of Cloud Computing and recent developments in Grid Computing have brought platform virtualization technology into the field of high performance computing. Virtualization offers both more flexibility and security through custom user images and user isolation. In this paper, we present an approach for combined malware detection and kernel root kit prevention in virtualized Cloud Computing environments. All running binaries in a virtual instance are intercepted and submitted to one or more analysis engines. Besides a complete check against a signature database, live introspection of all system calls is performed to detect yet unknown exploits or malware. Furthermore, to prevent that an intruder retains persistent control over a running instance after a successful compromise, an in-kernel root kit prevention approach is proposed. Only authorized and thus trusted kernel modules are allowed to be loaded during runtime, loading of unauthorized modules is no longer possible. Finally, the performance of the presented solutions is evaluated.