Search

User-centric data sharing is essential to encourage citizens' active participation in the digital economy. One key to smart cities, a form of the digital economy, is the promotion of public use of citizen data. Nevertheless, it is not easy to utilize data without citizens’ consent. In this study, we took a technological approach to these issues. User-managed access (UMA) is a well-known framework for delegating resource access rights to others on the Internet. In UMA, authorization mechanisms are designed to be centralized so that resource owners can centrally manage access rights for various resources stored in different domains. However, the lack of transparency in the authorization mechanism is a barrier to its implementation in large-scale systems such as smart cities. In this study, we developed a blockchain-based cross-domain authorization architecture that enables a resource-sharing ecosystem in which organizations that wish to utilize data can freely trade with each other. The proposed architecture solves the transparency problem that conventional authorization systems have had by designing the authorization mechanism on blockchain technology. We implemented the proposed architecture as smart contracts and evaluated its processing performance. The resultant time required for delegating access rights and accessing resources was less than 500 ​ms. Furthermore, we found that the fluctuation in the processing time overhead was small. Based on these results, we concluded that performance degradation with the proposed architecture is minor.

Data mining entails the discovery of unexpected but reusable knowledge from large unorganized datasets. Among the many available data-mining algorithms, association rule mining (ARM) is very common. It was developed to aggregate all data into one site and subsequently mine them. In recent years, organizations in different fields have been required to collaborate to create new value. However, data mining among and within organizations has raised privacy and confidentiality concerns. In our scheme, parties cannot share anything other than the number of records, including the candidate itemset. This study focuses on the private-set intersection instead of the scalar product and shows that this intersection enables organizations to execute ARM on vertically partitioned data, allowing flexible information sharing while preserving privacy without increasing communication and computation costs. Furthermore, we focus on the fact that the number of protocol rounds among parties can be reduced and present three use cases in which the proposed scheme works more effectively than the existing schemes.

Content retrieval is becoming an emerging application on an integrated network structure referred to as Software Defined Network and Vehicular Ad hoc Network (SDVN). The SDVN is expected to flexibly provide efficient packet routing without deploying road side units. In such the infrastructure-less environment, however, it is challenging to retrieve contents from dynamic networks clustered with vehicles on roadways. As a solution to the problem, we propose a new network design by conjoining Named Data Network (NDN) with SDVN (NDN-SDVN). The new NDN-SDVN is designed as to improve stableness of the cluster formation and hence enables to efficiently perform the content retrieval over Vehicle-to-Vehicle (V2V) communications. Note that one of the most important problems in the V2V is how to select a cluster head. The proposed NDN-SDN adaptively determines the cluster head by using the SDN controller under the maximum Quality of Service (QoS) criterion, while reserving an assistance head based on a Moth Flame Optimization (MFO) algorithm to immediately recompose a new cluster with the back-off time. Moreover, the content retrieval in the NDN-SDVN can be performed over both intra- and inter-cluster communications. To this end, a Kalman filter with Neural Network (KF-NN) is applied for selecting a certain number of preferable vehicles in 1-hop in order to mitigate the broadcast storm problem that often occurs in an NDN. Content retrieval is initiated by sending interest packets including two additional fields of the timestamp and the target MAC address. The head vehicle of the SDN controller maintains ontology regarding the content information cashed by other vehicles, which significantly improves the packet delivery ratio. This NDN-SDVN design is developed on the OMNeT++ simulator and their advantageous results are evaluated in terms of the head lifetime, the cluster lifetime, the satisfactory rate, the latency and the packet delivery ratio.

With the growing diversity of cyberattacks in recent years, anomaly-based intrusion detection systems that can detect unknown attacks have attracted significant attention. Furthermore, a wide range of studies on anomaly detection using machine learning and deep learning methods have been conducted. However, many machine learning and deep learning-based methods require significant effort to design the detection feature values, extract the feature values from network packets, and acquire the labeled data used for model training. To solve the aforementioned problems, this paper proposes a new model called DOC-IDS, which is an intrusion detection system based on Perera’s deep one-class classification. The DOC-IDS, which comprises a pair of one-dimensional convolutional neural networks and an autoencoder, uses three different loss functions for training. Although, in general, only regular traffic from the computer network subject to detection is used for anomaly detection training, the DOC-IDS also uses multi-class labeled traffic from open datasets for feature extraction. Therefore, by streamlining the classification task on multi-class labeled traffic, we can obtain a feature representation with highly enhanced data discrimination abilities. Simultaneously, we perform variance minimization in the feature space, even on regular traffic, to further improve the model’s ability to discriminate between normal and abnormal traffic. The DOC-IDS is a single deep learning model that can automatically perform feature extraction and anomaly detection. This paper also reports experiments for evaluating the anomaly detection performance of the DOC-IDS. The results suggest that the DOC-IDS offers higher anomaly detection performance while reducing the load resulting from the design and extraction of feature values.

Vehicle ad hoc network is the key technology for a future Internet of vehicles and intelligent transport system. However, involvement of vast number of vehicles in Internet of vehicles limits the performance of vehicle ad hoc network. To tackle this problem, a novel vehicle ad hoc network architecture with two different technologies such as software-defined networking and named-data networking is proposed in this article. In the proposed software-defined networking controlled vehicular named-data networking, IP addressing issue is resolved by named-data networking and global view of the network is attained by software-defined networking. Emergency data dissemination is initiated with packet classification. For packet classification, policy-based bifold classifier is proposed in roadside unit and supported by evolved interest packet. Subsequently, best disseminator selection is carried out by trustworthy weighted graph scheme based on novel weight value, which is computed by considering significant metrics. Content retrieval is accomplished by roadside unit and assisted by a controller. Location of content producer is obtained from a controller and optimal route is selected by roadside unit. Optimal route selection is performed by roadside unit for both content retrieval and vehicle-to-vehicle communication using novel region-based hybrid cuckoo search algorithm. Hybrid algorithm combines cuckoo search and particle swarm optimization algorithm to perform efficient route selection. Involvement of software-defined networking controller supports numerous users by providing a global view of the network, which includes network status and traffic information. Extensive simulation in NS-3 assures better interest satisfaction rate, interest satisfaction delay, forwarder interest packets, average hop count, and gain of scalability in software-defined networking controlled vehicular named-data networking than traditional vehicle ad hoc network.

Concomitant with the increasing density of semiconductors, various attacks that threaten the integrity and security of dynamic random access memory (DRAM) have been devised. Among these, a side-channel attack called RAMBleed is a prolific one that utilizes a general user-level account without special rights to read secret information. Studies have reported that it can be used to obtain OpenSSH secret keys. However, a technique for deriving the Rivest–Shamir–Adleman (RSA) secret keys used in OpenSSL under realistic parameters and environments has not been reported. We propose a method that uses RAMBleed to obtain OpenSSL secret keys and demonstrate its efficacy using the example of an Apache server. The proposed method exploits the fact that, in the operation of an Apache server that uses OpenSSL, the RSA private keys are deployed on DRAM at a set time. Although the result of reading this secret information contains a few errors, error-free secret information is obtainable when it is used with RSA cryptanalysis techniques. We performed a series of attacks incorporating RAMBleed and eventually retrieved the OpenSSL RSA private key, indicating that secret information is obtainable with high probability. The proposed method can easily and externally be executed without administrator privileges on a server using DRAM that is vulnerable to RAMBleed, showing that RAMBleed is also a major threat to OpenSSL.

The Internet of connected vehicles (IoCV) has made people more comfortable and safer while driving vehicles. This technology has made it possible to reduce road casualties; however, increased traffic and uncertainties in environments seem to be limitations to improving the safety of environments. In this paper, driver behavior is analyzed to provide personalized assistance and to alert surrounding vehicles in case of emergencies. The processes involved in this research are as follows. (i) Initially, the vehicles in an environment are clustered to reduce the complexity in analyzing a large number of vehicles. Multi-criterion-based hierarchical correlation clustering (MCB-HCC) is performed to dynamically cluster vehicles. Vehicular motion is detected by edge-assisted road side units (E-RSUs) by using an attention-based residual neural network (AttResNet). (ii) Driver behavior is analyzed based on the physiological parameters of drivers, vehicle on-board parameters, and environmental parameters, and driver behavior is classified into different classes by implementing a refined asynchronous advantage actor critic (RA3C) algorithm for assistance generation. (iii) If the driver’s current state is found to be an emergency state, an alert message is disseminated to the surrounding vehicles in that area and to the neighboring areas based on traffic flow by using jelly fish search optimization (JSO). If a neighboring area does not have a fog node, a virtual fog node is deployed by executing a constraint-based quantum entropy function to disseminate alert messages at ultra-low latency. (iv) Personalized assistance is provided to the driver based on behavior analysis to assist the driver by using a multi-attribute utility model, thereby preventing road accidents. The proposed driver behavior analysis and personalized assistance model are experimented on with the Network Simulator 3.26 tool, and performance was evaluated in terms of prediction error, number of alerts, number of risk maneuvers, accuracy, latency, energy consumption, false alarm rate, safety score, and alert-message dissemination efficiency.

Information-Centric Networking advocates ubiquitous in-network caching to enhance content distribution. Nonsafety application in vehicular communications is emerging beyond the initial safety application. However, content distribution based on TCP/IP Internet service in vehicular networks suffers from typical issue of low delivery ratio in urban environments, where high buildings block or attenuate the radio propagation as well as short radio coverage range. In order to solve this issue to deliver proximity marketing files, in this paper we propose in-network caching scheme in vehicular networks in accordance with traffic features, in which every vehicle is treated as either a subscriber to request a file or as a cache node to supply other nodes so as to accelerate file transmission effectively. Cache strategy of leave copy everywhere is uncoordinated and distributed, which fits the random and dynamic vehicular network. The performance evaluation is carried out by comparing the proposed scheme with the legacy solution of TCP/IP based scheme using simulation tools of OMNeT++ and Veins and SUMO, which is supplied with real-world urban map associated with random but reasonable traffic routes generated by our designed software for every vehicle. The simulation results validate the proposed scheme in four aspects: robustness resisting obstacle buildings, reliability and scalability in different traffic loads, low utilization ratios of RSUs and Internet resource, and efficiency of cache functions.