Privacy strategies and privacy patterns are fundamental concepts of the privacy-by-design engineering approach. While they support a privacy-aware development process for IT systems, the concepts used by malicious, privacy-threatening parties are generally less understood and known. We argue that understanding the “dark side”, namely how personal data is abused, is of equal importance. In this paper, we introduce the concept of privacy dark strategies and privacy dark patterns and present a framework that collects, documents, and analyzes such malicious concepts. In addition, we investigate from a psychological perspective why privacy dark strategies are effective. The resulting framework allows for a better understanding of these dark concepts, fosters awareness, and supports the development of countermeasures. We aim to contribute to an easier detection and successive removal of such approaches from the Internet to the benefit of its users.

Cooperative Intelligent Transportation Systems (cITS) are a promising technology to enhance driving safety and efficiency. Vehicles communicate wirelessly with other vehicles and infrastructure, thereby creating a highly dynamic and heterogeneously managed ad-hoc network. It is these network properties that make it a challenging task to protect integrity of the data and guarantee its correctness. A major component is the problem that traditional security mechanisms like PKI-based asymmetric cryptography only exclude outsider attackers that do not possess key material. However, because attackers can be insiders within the network (i.e., possess valid key material), this approach cannot detect all possible attacks. In this survey, we present misbehavior detection mechanisms that can detect such insider attacks based on attacker behavior and information analysis. In contrast to well-known intrusion detection for classical IT systems, these misbehavior detection mechanisms analyze information semantics to detect attacks, which aligns better with highly application-tailored communication protocols foreseen for cITS. In our survey, we provide an extensive introduction to the cITS ecosystem and discuss shortcomings of PKI-based security. We derive and discuss a classification for misbehavior detection mechanisms, provide an in-depth overview of seminal papers on the topic, and highlight open issues and possible future research trends.

Vehicular networks are a very promising technology to increase traffic safety and efficiency, and to enable numerous other applications in the domain of vehicular communication. Proposed applications for VANETs have very diverse properties and often require nonstandard communication protocols. Moreover, the dynamics of the network due to vehicle movement further complicates the design of an appropriate comprehensive communication system. In this article we collect and categorize envisioned applications from various sources and classify the unique network characteristics of vehicular networks. Based on this analysis, we propose five distinct communication patterns that form the basis of almost all VANET applications. Both the analysis and the communication patterns shall deepen the understanding of VANETs and simplify further development of VANET communication systems.

Knowledge about a network protocol to understand the communication between entities is necessary for vulnerability research, penetration testing, malware analysis, network reconnaissance, and network modeling. Traffic analysis is one approach to infer a protocol, and this approach has specific challenges, tasks, methods, and solutions. In this survey, we collect tools presented by prior research in the field of protocol reverse engineering by static traffic trace analysis. We dissect each tool to discern the individual mechanisms and the algorithms on which they are based, then categorize and contrast the mechanisms and algorithms used in static traffic trace analysis to discuss how successfully they were applied in each case. To structure our discussion about the tools, we compared classification schemes for protocol reverse engineering. We present and discuss an explicit process model for static traffic trace analysis to reveal the common structure of the decomposed tools and frameworks from previous research. Via discussions of the algorithms applied within each tool, we show relations between tools, methods, and the process for each process task. We validate our model by applying it to each of the tools, then provide an outline of the utility of protocol reverse engineering. Beginning with the process description, we deduce which solutions and algorithms have already been investigated and where challenges remain to determine how new solutions may be researched in the future. Across the entire field of protocol reverse engineering, few implementations of tools and frameworks are publicly available, which remains a prevalent problem.

In-network aggregation mechanisms for vehicular ad hoc networks (VANETs) aim at improving communication efficiency by summarizing information that is exchanged between vehicles. Summaries are calculated, while data items are generated in and forwarded through the network. Due to its high bandwidth saving potential, aggregation is a vital building block for many of the applications envisioned in VANETs. At the same time, the specific environment of VANETs calls for novel approaches to aggregation, which address their challenging requirements. In this paper, we survey and structure this active research field. We propose a generic model to describe and classify the proposed approaches, and we identify future research challenges.

Vehicular networks (VANETs) are a growing research area with a large number of use cases. Foreseen applications include safety applications, traffic efficiency enhancements, and infotainment services. To make future deployment successful, it is imperative that all applications are matched with proper security mechanisms. Current proposals mostly focus on entity authorization by establishing a public key infrastructure. Such proactive security efficiently excludes nonauthorized entities from the network. However, in the face of insider attackers possessing valid key material, we need to consider data-centric methods to complement entity-centric trust. A promising approach for consistency checks, particularly in multihop scenarios, is to exploit redundant information dissemination. If information is received from both honest and malicious vehicles, chances are that attacks can be detected. In this paper, we propose three graph-based metrics to gauge the redundancy of dissemination protocols. We apply our metrics to a baseline protocol, a geocast protocol, and an aggregation protocol using extensive simulations. In addition, we point out open issues and applications of the metrics, such as colluding attackers and eviction of attacker nodes based on detected attacks. Results show that Advanced Adaptive Geocast behaves almost optimally from a routing efficiency point of view but fails to offer sufficient redundancy for data consistency mechanisms in many scenarios. The simulated aggregation protocol shows sufficient redundancy to facilitate data consistency checking.