KALwEN: a new practical and interoperable key management scheme for body sensor networks

Key management is the pillar of a security architecture. Body sensor networks (BSNs) pose several challenges–some inherited from wireless sensor networks (WSNs), some unique to themselves–that require a new key management scheme to be tailor-made. The challenge is taken on, and the result is KALwEN, a new parameterized key management scheme that combines the best-suited cryptographic techniques in a seamless framework. KALwEN is user-friendly in the sense that it requires no expert knowledge of a user, and instead only requires a user to follow a simple set of instructions when bootstrapping or extending a network. One of KALwEN's key features is that it allows sensor devices from different manufacturers, which expectedly do not have any pre-shared secret, to establish secure communications with each other. KALwEN is decentralized, such that it does not rely on the availability of a local processing unit (LPU). KALwEN supports secure global broadcast, local broadcast, and local (neighbor-to-neighbor) unicast, while preserving past key secrecy and future key secrecy (FKS). The fact that the cryptographic protocols of KALwEN have been formally verified also makes a convincing case. With both formal verification and experimental evaluation, our results should appeal to theorists and practitioners alike

Licensing structured data with ease

In response to the need of a rights expression language (REL), we have proposed LicenseScript, an REL based on multiset rewriting and Prolog. LicenseScript has advantage over existing RELs, in the sense that it has a well-defined semantics. In fact besides semantics, LicenseScript has a lot of other advantages over other RELs. The mission of this paper is twofold: (1) to put a spotlight on these advantages, (2) at the same time justifying some of our design rationales in LicenseScript. We accomplish this by giving examples of licensing models that are greatly facilitated by the use of Prolog as a component of LicenseScript. At the same time showing\ud how LicenseScript makes these non-trivial models viable, we also make LicenseScript a stronger case than it previously might have occurred to be

A formally verified decentralized key management architecture for wireless sensor networks

We present a decentralized key management architecture for wireless sensor networks, covering the aspects of key deployment, key refreshment and key establishment. Our architecture is based on a clear set of assumptions and guidelines. Balance between security and energy consumption is achieved by partitioning a system into two interoperable security realms: the supervised realm trades off simplicity and resources for higher security whereas in the unsupervised realm the vice versa is true. Key deployment uses minimal key storage while key refreshment is based on the well-studied scheme of Abdalla et al. The keying protocols involved use only symmetric cryptography and have all been verified with our constraint solving-based protocol verification tool CoProVe

Key management with group-wise pre-deployed keying and secret sharing pre-deployed keying

In wireless sensor networks, the key deployment problem has received little attention, whereas it is in fact fundamental, heavily involving crucial (scarce) resources of ad-hoc networks, such as memory and energy availability. We first briefly survey the state-of-the art of key deployment strategies that are amenable to ad-hoc network. Then we proposed two possible methods and we shortly investigate their space and computational complexity

Survey and benchmark of block ciphers for wireless sensor networks

Choosing the most storage- and energy-efficient block cipher specifically for wireless sensor networks (WSNs) is not as straightforward as it seems. To our knowledge so far, there is no systematic evaluation framework for the purpose. In this paper, we have identified the candidates of block ciphers suitable for WSNs based on existing literature. For evaluating and assessing these candidates, we have devised a systematic framework that not only considers the security properties but also the storage- and energy-efficency of the candidates. Finally, based on the evaluation results, we have selected the suitable ciphers for WSNs, namely Rijndael for high security and energy efficiency requirements; and MISTY1 for good storage and energy efficiency

LicenseScript: A Novel Digital Rights Language and its Semantics

We propose LicenseScript as a new multiset rewriting/ logic based language for expressing dynamic conditions of use of digital assets such as music, video or private data. LicenseScript differs from other DRM languages in that it caters for the intentional but legal manipulation of data. We believe this feature is the answer to providing the flexibility needed to support emerging usage paradigms of digital data. We provide the language with a simple semantics based on traces

Domain Adaptation for Satellite-Borne Hyperspectral Cloud Detection

The advent of satellite-borne machine learning hardware accelerators has enabled the on-board processing of payload data using machine learning techniques such as convolutional neural networks (CNN). A notable example is using a CNN to detect the presence of clouds in hyperspectral data captured on Earth observation (EO) missions, whereby only clear sky data is downlinked to conserve bandwidth. However, prior to deployment, new missions that employ new sensors will not have enough representative datasets to train a CNN model, while a model trained solely on data from previous missions will underperform when deployed to process the data on the new missions. This underperformance stems from the domain gap, i.e., differences in the underlying distributions of the data generated by the different sensors in previous and future missions. In this paper, we address the domain gap problem in the context of on-board hyperspectral cloud detection. Our main contributions lie in formulating new domain adaptation tasks that are motivated by a concrete EO mission, developing a novel algorithm for bandwidth-efficient supervised domain adaptation, and demonstrating test-time adaptation algorithms on space deployable neural network accelerators. Our contributions enable minimal data transmission to be invoked (e.g., only 1% of the weights in ResNet50) to achieve domain adaptation, thereby allowing more sophisticated CNN models to be deployed and updated on satellites without being hampered by domain gap and bandwidth limitations

Predicting Pre-university Student's Mathematics Achievement

AbstractThis study exploits three methods, namely the Back-propagation Neural Network (BPNN), Classification and Regression Tree (CART), and Generalized Regression Neural Network (GRNN) in predicting the student's mathematics achievement. The first part of this study utilizes enrolment data to predict the student's mid-semester evaluation result, whereas the latter part employs additional data to predict the student's final examination result. The predictive model's accuracy is evaluated using 10-fold cross-validation to identify the best model. The findings reveal that BPNN outperforms other models with an accuracy of 66.67% and 71.11% in predicting the mid-semester evaluation result and the final examination result respectively