138,867 research outputs found
A taxonomy of asymmetric requirements aspects
The early aspects community has received increasing attention among researchers and practitioners, and has grown a set of meaningful terminology and concepts in recent years, including the notion of requirements aspects. Aspects at the requirements level present stakeholder concerns that crosscut the problem domain, with the potential for a broad impact on questions of scoping, prioritization, and architectural design. Although many existing requirements engineering approaches advocate and advertise an integral support of early aspects analysis, one challenge is that the notion of a requirements aspect is not yet well established to efficaciously serve the community. Instead of defining the term once and for all in a normally arduous and unproductive conceptual unification stage, we present a preliminary taxonomy based on the literature survey to show the different features of an asymmetric requirements aspect. Existing approaches that handle requirements aspects are compared and classified according to the proposed taxonomy. In addition,we study crosscutting security requirements to exemplify the taxonomy's use, substantiate its value, and explore its future directions
On the Activity Privacy of Blockchain for IoT
Security is one of the fundamental challenges in the Internet of Things (IoT)
due to the heterogeneity and resource constraints of the IoT devices. Device
classification methods are employed to enhance the security of IoT by detecting
unregistered devices or traffic patterns. In recent years, blockchain has
received tremendous attention as a distributed trustless platform to enhance
the security of IoT. Conventional device identification methods are not
directly applicable in blockchain-based IoT as network layer packets are not
stored in the blockchain. Moreover, the transactions are broadcast and thus
have no destination IP address and contain a public key as the user identity,
and are stored permanently in blockchain which can be read by any entity in the
network. We show that device identification in blockchain introduces privacy
risks as the malicious nodes can identify users' activity pattern by analyzing
the temporal pattern of their transactions in the blockchain. We study the
likelihood of classifying IoT devices by analyzing their information stored in
the blockchain, which to the best of our knowledge, is the first work of its
kind. We use a smart home as a representative IoT scenario. First, a blockchain
is populated according to a real-world smart home traffic dataset. We then
apply machine learning algorithms on the data stored in the blockchain to
analyze the success rate of device classification, modeling both an informed
and a blind attacker. Our results demonstrate success rates over 90\% in
classifying devices. We propose three timestamp obfuscation methods, namely
combining multiple packets into a single transaction, merging ledgers of
multiple devices, and randomly delaying transactions, to reduce the success
rate in classifying devices. The proposed timestamp obfuscation methods can
reduce the classification success rates to as low as 20%
Incorporating Security Behaviour into Business Models Using a Model Driven Approach
There has, in recent years, been growing interest in Model Driven Engineering (MDE), in which models are the primary design artifacts and transformations are applied to these models to generate refinements leading to usable implementations over specific platforms. There is also interest in factoring out a number of non-functional aspects, such as security, to provide reusable solutions applicable to a number of different applications. This paper brings these two approaches together, investigating, in particular, the way behaviour from the different sources can be combined and integrated into a single design model. Doing so involves transformations that weave together the constraints from the various aspects and are, as a result, more complex to specify than the linear pipelines of transformations used in most MDE work to date. The approach taken here involves using an aspect model as a template for refining particular patterns in the business model, and the transformations are expressed as graph rewriting rules for both static and behaviour elements of the models
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