Achieving Privacy-Preserving DSSE for Intelligent IoT Healthcare System

As the product of combining Internet of Things (IoT), cloud computing, and traditional healthcare, Intelligent IoT Healthcare (IIoTH) brings us a lot of convenience, meanwhile security and privacy issues have attracted great attention. Dynamic searchable symmetric encryption (DSSE) technique can make the user search the dynamic healthcare information from IIoTH system under the condition that the privacy is protected. In this article, a novel privacy-preserving DSSE scheme for IIoTH system is proposed. It is the first DSSE scheme designed for personal health record (PHR) files database with forward security. We construct the secure index based on hash chain and realize trapdoor updates for resisting file injection attacks. In addition, we realize fine-grained search over encrypted PHR files database of attribute-value type. When the user executes search operations, he/she gets only a matched attribute value instead of the whole file. As a result, the communication cost is reduced and the disclosure of patient's privacy is minimized. The proposed scheme also achieves attribute access control, which allows users have different access authorities to attribute values. The specific security analysis and experiments show the security and the efficiency of the proposed scheme

An efficient combined deep neural network based malware detection framework in 5G environment

While Android smartphones are widely used in 5G networks, third-party application platforms are facing a rapid increase in the screening of applications for market launch. However, on the one hand, due to the receipt of excessive applications for listing, the review requires a lot of time and computing resources. On the other hand, due to the multi-selectivity of Android application features, it is difficult to determine the best feature combination as a criterion for distinguishing benign and malicious software. To address these challenges, this paper proposes an efficient malware detection framework based on deep neural network called DLAMD that can face large-scale samples. An efficient detection framework is designed, which combines the pre-detection phase of rapid detection and the deep detection phase of deep detection. The Android application package (APK) is analyzed in detail, and the permissions and opcodes feature that can distinguish benign from malicious are quickly extracted from the APK. Besides, to obtain the feature subset that can distinguish the attributes most, the random forest with good effect is selected for importance selection and the convolutional neural network (CNN) which automatically extracted the hidden pattern inside features is selected for feature selection. In the experiment, real data from shared malware collection and third-party application download platforms are used to verify the high efficiency of the proposed method. The results show that the comprehensive classification index F1-score of DLAMD can reach 95.69%

On the Exploration of Adaptive Mechanisms Providing Reliability in Clustered WSNs for Power Plant Monitoring

Wireless Sensor Networks (WSNs) are used in almost every sensing and detection environment instead of wired devices in the current world, all the more in power plant monitoring applications. In such a kind of environment, providing reliability is a challenging task, since WSN makes use of low powered sensors. There are many existing works that provide reliable transmission in WSN (predominantly via multipath routing). However, most of the existing works take additional delay, excessive packet loss, and energy consumption, and hence they provide less packet delivery and throughput. Adaptive Priority Routing (APR) is first proposed during the initial design to provide efficiency in next hop selection. APR computes the priority value for selecting the intermediate nodes during the data transmission in order to improve the packet delivery, throughput, and energy efficiency. In addition to this, APR is developed into QAPR protocol to provide reliability which can operate in two modes, representing distance mode and representing quality of service (QoS) mode. The proposed work is simulated in both flat topology and hierarchical topologies and the simulation analysis shows that the reliability is increased significantly in comparison with existing works

Privacy-Protection Scheme Based on Sanitizable Signature for Smart Mobile Medical Scenarios

With the popularization of wireless communication and smart devices in the medical field, mobile medicine has attracted more and more attention because it can break through the limitations of time, space, and objects and provide more efficient and quality medical services. However, the characteristics of a mobile smart medical network make it more susceptible to security threats such as data integrity damage and privacy leakage than those of traditional wired networks. In recent years, many digital signature schemes have been proposed to alleviate some of these challenges. Unfortunately, traditional digital signatures cannot meet the diversity and privacy requirements of medical data applications. In response to this problem, this paper uses the unique security attributes of sanitizable signatures to carry out research on the security and privacy protection of medical data and proposes a data security and privacy protection scheme suitable for smart mobile medical scenarios. Security analysis and performance evaluation show that our new scheme effectively guarantees data security and user privacy while greatly reducing computation and communication costs, making it especially suitable for mobile smart medical application scenarios

BBAAS: Blockchain-Based Anonymous Authentication Scheme for Providing Secure Communication in VANETs

Smart driving has become conceivable due to the rapid growth of vehicular ad hoc networks. VANETs are considered as the main platform for providing safety road information and instant vehicle communication. Nevertheless, due to the open wireless nature of communication channels, VANET is susceptible to security attacks by malicious users. For this reason, secure anonymous authentication schemes are essential in VANETs. However, when vehicles reach a new roadside unit (RSU) coverage area, the vehicles need to perform reauthentication with the current RSU, which significantly diminishes the efficiency of the entire VANET. Therefore, the introduction of blockchain technology has created opportunities for VANETs to resolve the aforementioned challenges. Due to the decentralized nature of blockchain technology, rapid reauthentication of vehicles is achieved in this paper through secure authentication code transfer between the consecutive RSUs. The security strength of the proposed blockchain-based anonymous authentication scheme against various harmful security attacks is proven in the security analysis section to ensure that it provides better security. In addition, blockchain, as presented in the performance analysis section, is used to substantially diminish the computational cost compared to conventional authentication schemes