Swedish Institute of Computer Science Publications Database
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On Improving Resistance to Denial of Service and Key Provisioning Scalability of the DTLS Handshake
No full textDTLS is a transport layer security protocol designed to provide secure communication over unreliable datagram protocols. Before starting to communicate, a DTLS client and server perform a specific handshake in order to establish a secure session and agree on a common security context. However, the DTLS handshake is affected by two relevant issues. First, the DTLS server is vulnerable to a specific Denial of Service (DoS) attack aimed at forcing the establishment of several half open sessions. This may exhaust memory and network resources on the server, so making it less responsive or even unavailable to legitimate clients. Second, although it is one of the most efficient key provisioning approaches adopted in DTLS, the pre-shared key provisioning mode does not scale well with the number of clients, it may result in scalability issues on the server side, and it complicates key re-provisioning in dynamic scenarios. This paper presents a single and efficient security architecture which addresses both issues, by substantially limiting the impact of DoS, and reducing the number of keys stored on the server side to one unit only. Our approach does not break the existing standard and does not require any additional message exchange between DTLS client and server. Our experimental results show that our approach requires a shorter amount of time to complete a handshake execution, and consistently reduces the time a DTLS server is exposed to a DoS instance. We also show that it considerably improves a DTLS server in terms of service availability and robustness against DoS attack
A Text Analyser of Crowdsourced Online Sources for Knowledge Discovery
No full textIn the last few years, Twitter has become the centre of crowdsourced-generated content. Numerous tools exist to analyse its content to lead to knowledge discovery. However, most of them focus solely on the content and ignore user features. Selecting and analysing user features such as user activity and relationships lead to the discovery of authorities and user communities. Such a discovery can provide an additional perspective to crowdsourced data and increase understanding of the evolution of the trends for a given topic. This work addresses the problem by introducing a dedicated software tool developed, the Text Analyser of Crowdsourced Online Sources (TACOS). TACOS is a social relationship search tool that given a search term, analyses user features and discovers authorities and user communities for that term. For knowledge representation, it visualises the output in a graph, for increased readability. In order to show the applicability of TACOS, we have chosen a real example and aimed through two case studies to discover and analyse a specific type of user communities
Use Cases for Authentication and Authorization in Constrained Environments
No full textConstrained devices are nodes with limited processing power, storage space, and transmission capacities. In many cases, these devices do not provide user interfaces, and they are often intended to interact without human intervention. This document includes a collection of representative use cases for authentication and authorization in constrained environments. These use cases aim at identifying authorization problems that arise during the life cycle of a constrained device and are intended to provide a guideline for developing a comprehensive authentication and authorization solution for this class of scenarios. Where specific details are relevant, it is assumed that the devices use the Constrained Application Protocol (CoAP) as a communication protocol. However, most conclusions apply generally
FOCUS: Robust Visual Codes for Everyone
No full textVisual codes are used to embed digital data in physical objects, or they are shown in video sequences to transfer data over screen/camera links. Existing codes either carry limited data to make them robust against a range of channel conditions (e.g., low camera quality or long distances), or they support a high data capacity but only work over a narrow range of channel conditions. We present Focus, a new code design that does not require this explicit trade-off between code capacity and the reader’s channel quality. Instead,Focus builds on concepts from OFDM to encode data at different levels of spatial detail. This enables each reader to decode as much data from a code as its channel quality allows. We build a prototype of Focus devices and evaluate it experimentally. Our results show that Focus gracefully adapts to the reader’s channel, and that it provides a significant performance improvement over recently proposed designs, including Strata and PixNet
The aesthetics of heat: Guiding Awareness with Thermal Stimuli
No full textIn this paper we discuss the design process and results from a design exploration on the use of thermal stimuli in body awareness exercises. A user-study was performed on an interactive prototype in the form of an interactive heat mat. The paper brings forth an alternative understanding of heat as a design material that extends the common understanding of thermal stimuli in HCI as a communication modality to instead bring the aesthetic and experiential properties to the fore. Findings account for felt body experiences of thermal stimuli and a number of design qualities related to heat as a design material are formulated, pointing to experiential qualities concerning the felt body, subjectivity and subtleness as well as material qualities concerning materiality, inertia and heat transfe
Demo: Opportunistic IoT for Monitoring of Grazing Cattle
No full textPrecision livestock farming and other agricultural applications are considered to have great potential to utilise the many benefits of IoT technology. It is however important to maintain low cost and energy consumption to make it feasible in a very competitive market, making opportunistic and D2D communication interesting options. The demo will show a prototype of a system for monitoring grazing cattle in large free-range areas. We will demonstrate the mobile devices collecting data about the animals and their encounters with each other as well as the cloud platform that allows the farmer to access the data for more efficient operation of the farm
Data Integration Using Machine Learning
No full textToday, enterprise integration and cross-enterprise collaboration is becoming evermore important. The Internet of things, digitization and globalization are pushing continuous growth in the integration market. However, setting up integration systems today is still largely a manual endeavor. Most probably, future integration will need to leverage more automation in order to keep up with demand. This paper presents a first version of a system that uses tools from artificial intelligence and machine learning to ease the integration of information systems, aiming to automate parts of it. Three models are presented and evaluated for precision and recall using data from real, past, integration projects. The results show that it is possible to obtain F0.5 scores in the order of 80% for models trained on a particular kind of data, and in the order of 60%-70% for less specific models trained on a several kinds of data. Such models would be valuable enablers for integration brokers to keep up with demand, and obtain a competitive advantage. Future work includes fusing the results from the different models, and enabling continuous learning from an operational production system
Notes On Agile and Safety-Critical Development
No full textAgile approaches have been highly influential to the software engineering practices in many organizations, and are increasingly being applied in larger companies, and for developing systems outside the pure software domain. To understand more about the current state of agile, its applications to safety-critical systems, and the consequences on innovation and large organizations, a seminar was organized in Stockholm in 2014. This paper gives an overview of the topics discussed at that seminar, a summary of the main results and suggestions for future work as input to a research agenda for agile development of safety-critical software
Towards Earlier Fault Detection by Value-Driven Prioritization of Test Cases Using Fuzzy TOPSIS
No full textIn industrial software testing, development projects typically set up and maintain test suites containing large numbers of test cases. Executing a large number of test cases can be expensive in terms of effort and wall-clock time. Moreover, indiscriminate execution of all available test cases typically lead to sub-optimal use of testing resources. On the other hand, selecting too few test cases for execution might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. In this paper, we propose a multi-criteria decision making approach for prioritizing test cases in order to detect faults earlier. This is achieved by applying the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) decision making technique combined with fuzzy principles. Our solution is based on important criteria such as fault detection probability, execution time, complexity, and other test case properties. By applying the approach on a train control management subsystem from Bombardier Transportation in Sweden, we demonstrate how it helps, in a systematic way, to identify test cases that can lead to early detection of faults while respecting various criteria