The wireless body area networks (WBANs) have emerged as a new method for e-healthcare. Without being measured face-to-face, the medical workers can give guidance to patients in a real-time way. WBANs can greatly improve the healthcare quality. The personal information and medical data are stored and processed in sensors. The security and privacy are two vital issues. In this paper, we design an attribute-based encryption scheme for fine-grained access control in WBANs. In our scheme, a user can decrypt a ciphertext if the attributes related with a ciphertext satisfy the user's access structure. The users can be revoked if necessary. Therefore, the security and privacy of patients can be protected. Our scheme provides confidentiality, security, and resistance to collusion attack. We analyze the correctness, security, and energy consumption of the scheme.
It is essential to design a protocol to allow sensor nodes to attest to their trustworthiness for missioncritical applications based on Wireless Sensor Networks (WSNs). However, it is a challenge to evaluate the trustworthiness without appropriate hardware support. Hence, we present a hardware-based remote attestation protocol to tackle the problem within WSNs. In our design, each sensor node is equipped with a Trusted Platform Module (TPM) which plays the role of a trusted anchor. We start with the formulation of remote attestation and its security. The complete protocol for both single-hop and multi-hop attestations is then demonstrated. Results show the new protocol is effective, efficient, and secure.
In the era of big data, feature selection is an essential process in machine learning. Although the class imbalance problem has recently attracted a great deal of attention, little effort has been undertaken to develop feature selection techniques. In addition, most applications involving feature selection focus on classification accuracy but not cost, although costs are important. To cope with imbalance problems, we developed a cost-sensitive feature selection algorithm that adds the cost-based evaluation function of a filter feature selection using a chaos genetic algorithm, referred to as CSFSG. The evaluation function considers both feature-acquiring costs (test costs) and misclassification costs in the field of network security, thereby weakening the influence of many instances from the majority of classes in large-scale datasets. The CSFSG algorithm reduces the total cost of feature selection and trades off both factors. The behavior of the CSFSG algorithm is tested on a large-scale dataset of network security, using two kinds of classifiers: C4.5 andk-nearest neighbor (KNN). The results of the experimental research show that the approach is efficient and able to effectively improve classification accuracy and to decrease classification time. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.