Internet of Things (IoT) devices are becoming more widespread not only in areas such as smart homes and smart cities but also in research and office environments. The sheer number, heterogeneity, and limited patch availability provide significant challenges for the security of both office networks and the Internet in general. The systematic estimation of device risks, which is essential for mitigation decisions, is currently a skill-intensive task that requires expertise in network vulnerability scanning, as well as manual effort in firmware binary analysis. This article introduces SAFER, 1 the Security Assessment Framework for Embedded-device Risks, which enables a semi-automated risk assessment of IoT devices in any network. SAFER combines information from network device identification and automated firmware analysis to estimate the current risk associated with the device. Based on past vulnerability data and vendor patch intervals for device models, SAFER extrapolates those observations into the future using different automatically parameterized prediction models. Based on that, SAFER also estimates an indicator for future security risks. This enables users to be aware of devices exposing high risks in the future. One major strength of SAFER over other approaches is its scalability, achieved through significant automation. To demonstrate this strength, we apply SAFER in the network of a large multinational organization, to systematically assess the security level of hundreds of IoT devices on large-scale networks. Results indicate that SAFER successfully identified 531 out of 572 devices leading to a device identification rate of 92.83 %, analyzed 825 firmware images, and predicted the current and future security risk for 240 devices.

Cooperative Intelligent Transport Systems (C-ITS) is a new upcoming technology that aims at increasing road safety and reducing traffic accidents. C-ITS is based on peer-to-peer messages sent on the Vehicular Ad hoc NETwork (VANET). VANET messages are currently authenticated using digital keys from valid certificates. However, the authenticity of a message is not a guarantee of its correctness. Consequently, a misbehavior detection system is needed to ensure the correct use of the system by the certified vehicles. Although a large number of studies are aimed at solving this problem, the results of these studies are still difficult to compare, reproduce and validate. This is due to the lack of a common reference dataset. For this reason, the original VeReMi dataset was created. It is the first public misbehavior detection dataset allowing anyone to reproduce and compare different results. VeReMi is used in a number of studies and is currently the only dataset in its field. In this Paper, we extend the dataset by adding realistic a sensor error model, a new set of attacks and larger number of data points. Finally, we also provide benchmark detection metrics using a set of local detectors and a simple misbehavior detection mechanism.

Protocol reverse engineering based on traffic traces infers the behavior of unknown network protocols by analyzing observable network messages. To perform correct deduction of message semantics or behavior analysis, accurate message type identification is an essential first step. However, identifying message types is particularly difficult for binary protocols, whose structural features are hidden in their densely packed data representation. We leverage the intrinsic structural features of binary protocols and propose an accurate method for discriminating message types.Our approach uses a similarity measure with continuous value range by comparing feature vectors where vector elements correspond to the fields in a message, rather than discrete byte values. This enables a better recognition of structural patterns, which remain hidden when only exact value matches are considered. We combine Hirschberg alignment with DBSCAN as cluster algorithm to yield a novel inference mechanism. By applying novel autoconfiguration schemes, we do not require manually configured parameters for the analysis of an unknown protocol, as required by earlier approaches.Results of our evaluations show that our approach has considerable advantages in message type identification result quality and also execution performance over previous approaches.

The purpose of multi-source fusion is to combine information from more than two evidence sources, or subjective opinions from multiple actors. For subjective logic, a number of different fusion operators have been proposed, each matching a fusion scenario with different assumptions. However, not all of these operators are associative, and therefore multi-source fusion is not well-defined for these settings. In this paper, we address this challenge, and define multi-source fusion for weighted belief fusion (WBF) and consensus & compromise fusion (CCF). For WBF, we show the definition to be equivalent to the intuitive formulation under the bijective mapping between subjective logic and Dirichlet evidence PDFs. For CCF, since there is no independent generalization, we show that the resulting multi-source fusion produces valid opinions, and explain why our generalization is sound. For completeness, we also provide corrections to previous results for averaging and cumulative belief fusion (ABF and CBF), as well as belief constraint fusion (BCF), which is an extension of Dempster's rule. With our generalizations of fusion operators, fusing information from multiple sources is now well-defined for all different fusion types defined in subjective logic. This enables wider applicability of subjective logic in applications where multiple actors interact.

Driving behavior is a major factor in traffic safety applications. Abnormal behavior, such as extremely aggressive or passive driving, can endanger both the driver and other traffic participants. Most driving behavior analysis approaches to date rely on classification, which requires labeled data for both normal driving and various types of anomalous behavior. We propose an approach that detects anomalous driving patterns based on outlier detection, which does not require such data. Apart from the required data set, existing approaches have difficulties dealing with changing behavior that overlaps with normal behavior (e.g., aggressive drivers still stop in traffic jams). We introduce a post-processing step that significantly improves results in this regard. The approach is evaluated using simulations based on the realistic LuST traffic scenario, and shows reliable detection of anomalous vehicles with low false positive rates.

With the omnipresence of the Internet of Things and poorly secured devices with it in combination with high bandwidth networks, Distributed Denial of Service (DDoS) attacks have become one of the biggest threats for network security. With high bandwidth attacks flooding network infrastructure, the pressure to secure the attack targets shifts more and more to the network operators. Often without direct access to the target, the operators are asked to secure their clients. We propose a framework based on Software-Defined Networking (SDN) and the Bro Security Monitor that can mitigate attacks purely within the network infrastructure. In our evaluation, we show that our framework can reliably mitigate several different attack scenarios, including SYN flooding and HTTP flooding.

The increasing availability of 10G Ethernet network capabilities challenges existing transport layer protocols. As 10G connections gain momentum outside of backbone networks, the choice of appropriate TCP congestion control algorithms becomes even more relevant for networked applications running in environments such as data centers. Therefore, we provide an extensive overview of relevant TCP congestion control algorithms for high-speed environments leveraging 10G. We analyzed and evaluated six TCP variants using a physical network testbed, with a focus on the effects of propagation delay and significant drop rates. The results indicate that of the algorithms compared, BIC is most suitable when no legacy variant is present, CUBIC is suggested otherwise.

The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.

Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication aims to increase safety, efficiency, and comfort of drivers. Vehicles periodically broadcast their current status, such as position, velocity, and other information. Received information is stored in a local knowledge base, often called world model, and used for application decisions. Because of the potential impact, V2V communication is an interesting target for malicious attackers. Message integrity protection using cryptographic signatures only protects against outsider attackers. In addition to signatures, misbehavior detection mechanisms comparable to intrusion detection systems (IDS) are needed to detect insider attackers. Given the complexity and large number of foreseen V2V and V2I applications, misbehavior detection cannot be a one-size-fits-all solution. In this paper, we present a flexible framework that can combine a range of different misbehavior detection mechanisms by modeling their outputs using subjective logic. We demonstrate the feasibility of our framework by using a combination of existing detection mechanisms to increase their misbehavior detection results.

In vehicular ad-hoc networks (VANETs), a use case for mobile ad-hoc networks (MANETs), the ultimate goal is to let vehicles communicate using wireless message exchange to provide safety, traffic efficiency, and entertainment applications. Especially traffic efficiency applications benefit from wide-area message dissemination, and aggregation of information is an important tool to reduce bandwidth requirements and enable dissemination in large areas. The core idea is to exchange high quality summaries of the current status rather than forwarding all individual messages. Securing aggregation schemes is important, because they may be used for decisions about traffic management, as well as traffic statistics used in political decisions concerning road safety and availability. The most important challenge for security is that aggregation removes redundancy and the option to directly verify signatures on atomic messages. Existing proposals are limited, because they require roads to be segmented into small fixed-size regions, beyond which aggregation cannot be performed. In this paper, we introduce SeDyA, a scheme that allows more dynamic aggregation compared to existing work, while also providing stronger security guarantees. We evaluate SeDyA against existing proposals to show the benefits in terms of information accuracy, bandwidth usage, and resilience against attacks.