The application of Intelligent Transport Systems (ITS) transmits data with road-to-vehicle communication (RVC) and inter-vehicle communication (IVC). Digital signature is essential to provide security for RVC and IVC. The public key certificate is used to verify that a public key belongs to an individual prover such as user or terminal. A certificate revocation list (CRL) is used for verifying validity of the public key certificate. A certificate authority (CA) publishes a CRL and distributes it to vehicles. CRL distribution traffic disturbs ITS application traffic because of sharing wireless channel between them. To distribute it on low bit rate will help to ease the disturbance. Although multiplex transmitting is effective in reliable communication, a duplication of received packets is waste of bandwidth as a consequence. This paper proposes a CRL distribution scheme based on random network coding which can reduce duplicate packets. The simulation results show that the number of duplicate packets of the proposed scheme is less than that of a simple error correction (EC)-based scheme and the proposed one can distribute CRL to more vehicles than EC-based ones.

This paper proposes a component ranking method to identify important components which have great impact on the system reliability. This method, which is opposite to an existing method, believes components which frequently invoke other components have more impact than others and employs component invocation structures and invocation frequencies for making important component ranking. It can strongly support for improving the reliability of software systems, especially large-scale systems. Extensive experiments are provided to validate this method and draw performance comparison.

A plasma lighting system (PLS) using a solid-state (SS) radio frequency (RF) power amplifier (PA) is one of the promising lighting systems due to its excellent light characteristics and power efficiency. To improve the efficacy and reduce the adjacent channel interference of the PLS, a method to generate a band-limited pulsed-RF signal using the limited number of multi-tone signals is proposed. A 2.49 GHz PLS with a 300W gallium-nitride (GaN) SSPA is implemented, and it is used to verify the proposed method. The PLS using the proposed method shows better performance compared with those using conventional pulsed-RF signal.

This paper presents a hierarchical-masked image filtering method for privacy-protection. Cameras are widely used for various applications, e.g., crime surveillance, environment monitoring, and marketing. However, invasion of privacy has become a serious social problem, especially regarding the use of surveillance cameras. Many surveillance cameras point at many people; thus, a large amount of our private information of our daily activities are under surveillance. However, several surveillance cameras currently on the market and related research often have a complicated or institutional masking privacy-protection functionality. To overcome this problem, a Hierarchical-Masked image Filtering (HMF) method is proposed, which has unmaskable (mask reversal) capability and is applicable to current surveillance camera systems for privacy-information protection and can satisfy privacy-protection related requirements. This method has five main features: unmasking of the original image from only the masked image and a cipher key, hierarchical-mask level control using parameters for the length of a pseudorandom number, robustness against malicious attackers, fast processing on an embedded processor, and applicability of mask operation to current surveillance camera systems. Previous studies have difficulty in providing these features. To evaluate HMF on actual equipment, an HMF-based prototype system is developed that mainly consists of a USB web camera, ultra-compact single board computer, and notebook PC. Through experiments, it is confirmed that the proposed method achieves mask level control and is robust against attacks. The increase in processing time of the HMF-based prototype system compared with a conventional non-masking system is only about 1.4%. This paper also reports on the comparison of the proposed method with conventional privacy protection methods and favorable responses of people toward the HMF-based prototype system both domestically and abroad. Therefore, the proposed HMF method can be applied to embedded systems such as those equipped with surveillance cameras for protecting privacy.

Free-space optical (FSO) communication, which offers better data rate at a lower cost compared to radio-frequency (RF) backhauls, and is much easier to setup and maintain than optical cables, is gaining attention as an attractive substitute. Average capacity is one of the main performances metrics to understand the connectivity and data rates of a communication system but the performance analysis for mixed RF/FSO link is not straightforward as the RF link and the FSO link experiences different atmospheric perturbations. In this paper, we have investigated the ergodic capacity of a dual-hop mixed RF/FSO communication system realized with an average power scaling (APS) based amplify and forward (AF) relay. Assuming moderate to strong atmospheric turbulence, the FSO link is modeled by gamma-gamma distribution while it is assumed that the RF link experiences multipath Rayleigh fading. Simple analytical methods have been devised for obtaining concise closed-form expressions for ergodic capacity under four different rate/ power adaptation policies and are validated through extensive Monte Carlo simulations.

Qplus-AIR is a real-time operating system for avionics, and its safety and correctness should be analyzed and guaranteed. We performed model checking a version of Qplus-AIR with the Times model checker and identified one abnormal case that might result in safety-critical situations.

Recently, there has been an increase in use-after-free (UAF) vulnerabilities, which are exploited using a dangling pointer that refers to a freed memory. In particular, large-scale programs such as browsers often include many dangling pointers, and UAF vulnerabilities are frequently exploited by drive-by download attacks. Various methods to prevent UAF attacks have been proposed. However, only a few methods can effectively prevent UAF attacks during runtime with low overhead. In this paper, we propose HeapRevolver, which is a novel UAF attack-prevention method that delays and randomizes the timing of release of freed memory area by using a memory-reuse-prohibited library, which prohibits a freed memory area from being reused for a certain period. The first condition for reuse is that the total size of the freed memory area is beyond the designated size. The threshold for the conditions of reuse of the freed memory area can be randomized by HeapRevolver. Furthermore, we add a second condition for reuse in which the freed memory area is merged with an adjacent freed memory area before release. Furthermore, HeapRevolver can be applied without modifying the target programs. In this paper, we describe the design and implementation of HeapRevolver in Linux and Windows, and report its evaluation results. The results show that HeapRevolver can prevent attacks that exploit existing UAF vulnerabilities. In addition, the overhead is small.

With the expected increase in popularity of device-to-device (D2D) services, the importance of implementing an LTE simulator that enables fast and accurate evaluations of D2D related technologies is clear. In this paper, we report on a network simulator, D2dSim, with the aim of realizing an LTE-Advanced network that utilizes the D2D feature, i.e., in which direct transmission between mobile stations (MSs) is enabled. Using NS-3, one of the most popular network simulation platforms, D2dSim could become one of the first realistic open-source D2D-capable environments, providing realistic and standard-compliant implementations of a subset of Proximity-based Services complying with the LTE-A standard.

Smart education environment, that is a learning environment utilizing the Information Communication Technology (ICT), has attracted a great deal of attention. In order to expand this environment, we need a system that can establish the learning environment armed cloud systems to reduce a significant strain on teaching staff. The important issue for such system is extensibility because the system should be adapted to many kinds of original digital learning material with minimum modification. Therefore, this paper proposes “An Application Framework for Smart Education System: SES Framework”. In this Smart Education System, multi-aspect information concerning to a technical term embedded in the original digital learning material can be retrieved from different social media automatically. They can be also displayed on multi-screen devices according to user's operation. It is implemented based on “Transforming Model” which enables the migration of the original digital learning material to the smart education environment. It also has an easy operation flow for trainees named “three-step selection flow”. SES Framework derived from Model-View-Controller (MVC) pattern is based on the system architecture that enables triple mashup against the original digital learning material, external social media, and screen devices in front of users. All these functionalities have been implemented on cloud systems. We show SES Framework through the implementation example. We also demonstrate the effectiveness of SES Framework by indicating the system modification case study.

An operating system is an essential piece of software that manages hardware and software resources. Thus, attacks on an operating system kernel using kernel rootkits pose a particularly serious threat. Detecting an attack is difficult when the operating system kernel is infected with a kernel rootkit. For this reason, handling an attack will be delayed causing an increase in the amount of damage done to a computer system. In this paper, we propose Kernel Rootkits Guard (KRGuard), which is a new method to detect kernel rootkits that monitors branch records in the kernel space. Since many kernel rootkits make branches that differ from the usual branches in the kernel space, KRGuard can detect these differences by using the hardware features of commodity processors. Our evaluation shows that KRGuard can detect kernel rootkits that involve new branches in the system call handler processing with small overhead.

Protecting critical files in operating system is very important to system security. With the increasing adoption of Virtual Machine Introspection (VMI), designing VMI-based monitoring tools become a preferential choice with promising features, such as isolation, stealthiness and quick recovery from crash. However, these tools inevitably introduce high overhead due to their operation-based characteristic. Specifically, they need to intercept some file operations to monitor critical files once the operations are executed, regardless of whether the files are critical or not. It is known that file operation is high-frequency, so operation-based methods often result in performance degradation seriously. Thus, in this paper we present CFWatcher, a target-based real-time monitoring solution to protect critical files by leveraging VMI techniques. As a target-based scheme, CFWatcher constraints the monitoring into the operations that are accessing target files defined by users. Consequently, the overhead depends on the frequency of target files being accessed instead of the whole filesystem, which dramatically reduces the overhead. To validate our solution, a prototype system is built on Xen with full virtualization, which not only is able to monitor both Linux and Windows virtual machines, but also can take actions to prevent unauthorized access according to predefined policies. Through extensive evaluations, the experimental results demonstrate that the overhead introduced by CFWatcher is acceptable. Especially, the overhead is very low in the case of a few target files.

This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.

Ransomware becomes more and more threatening nowadays. In this paper, we propose CLDSafe, a novel and efficient file backup system against ransomware. It keeps shadow copies of files and provides secure restoration using cloud storage when a computer is infected by ransomware. After our system measures file similarities between a new file on the client and an old file on the server, the old file on the server is backed up securely when the new file is changed substantially. And then, only authenticated users can restore the backup files by using challenge-response mechanism. As a result, our proposed solution will be helpful in recovering systems from ransomware damage.

Conventional feature-rich parsers based on manually tuned features have achieved state-of-the-art performance. However, these parsers are not good at handling long-term dependencies using only the clues captured by a prepared feature template. On the other hand, recurrent neural network (RNN)-based parsers can encode unbounded history information effectively, but they perform not well for small tree structures, especially when low-frequency words are involved, and they cannot use prior linguistic knowledge. In this paper, we propose a simple but effective framework to combine the merits of feature-rich transition-based parsers and RNNs. Specifically, the proposed framework incorporates RNN-based scores into the feature template used by a feature-rich parser. On English WSJ treebank and SPMRL 2014 German treebank, our framework achieves state-of-the-art performance (91.56 F-score for English and 83.06 F-score for German), without requiring any additional unlabeled data.

While conventional studies on real-time systems have mostly considered the real-time constraint of real-time systems only, recent research initiatives are trying to incorporate a security constraint into real-time scheduling due to the recognition that the violation of either of two constrains can cause catastrophic losses for humans, the system, and even environment. The focus of most studies, however, is the single-criticality systems, while the security of mixed-criticality systems has received scant attention, even though security is also a critical issue for the design of mixed-criticality systems. In this paper, we address the problem of the information leakage that arises from the shared resources that are used by tasks with different security-levels of mixed-criticality systems. We define a new concept of the security constraint employing a pre-flushing mechanism to cleanse the state of shared resources whenever there is a possibility of the information leakage regarding it. Then, we propose a new non-preemptive real-time scheduling algorithm and a schedulability analysis, which incorporate the security constraint for mixed-criticality systems. Our evaluation demonstrated that a large number of real-time tasks can be scheduled without a significant performance loss under a new security constraint.

As a result of large-scale and complicated of ICT (Information and Communication Technology), the technologies of operations and management of ICT infrastructure and systems are changing to adopt the variation and diversity of usage and communication characteristics. In retrospect, operations and management technologies cover widely from the support of telecommunications operations and remote monitoring for maintaining network equipments, to an integrated network management framework for configuration, monitoring, testing and controls. Recently, the spread of network virtualization technologies enable agility, integrity, and resilience to ICT services. Operations and management technologies will be more important in future, for the support of integrated management of ICT infrastructure including computing resource, and automation of service operations. In this paper, we review research activities of the Technical Committee on Information and Communications Management (ICM) with discussions of individual research category. We then describe the state-of-the-art topics and future directions in the area of ICM.

This paper looks at the history of research in the Technical Committee on Information Networks from the time of its inception to the present and provides an overview of the latest research in this area based on the topics discussed in recent meetings of the committee. It also presents possible future developments in the field of information networks.

Vocabulary acquisition based on the traditional pen-and-paper approach is outdated, and has been superseded by the multimedia-supported approach. In a multimedia-supported foreign language learning environment, a learning material comprised of a still-image, a text, and the corresponding sound data is considered to be the most effective way to memorize a noun. However, extraction of an appropriate still image for a noun has always been a challenging and time-consuming process for learners. Learners' burden would be reduced if a system could extract an appropriate image for representing a noun. Therefore, the present study purposed to extract an appropriate image for each noun in order to assist foreign language learners in acquisition of foreign vocabulary. This study presumed that, a learning material created with the help of an appropriate image would be more effective in recalling memory compared to the one created with an inappropriate image. As the first step to finding appropriate images for nouns, concrete nouns have been considered as the subject of investigation. Therefore, this study, at first proposed a definition of an appropriate image for a concrete noun. After that, an image re-ranking algorithm has been designed and implemented that is able to extract an appropriate image from a finite set of corresponding images for each concrete noun. Finally, immediate-after, short- and long-term learning effects of those images with regard to learners' memory retention rates have been examined by conducting immediate-after, delayed and extended delayed posttests. The experimental result revealed that participants in the experimental group significantly outperformed the control group in their long-term memory retention, while no significant differences have been observed in immediate-after and in short-term memory retention. This result indicates that our algorithm could extract images that have a higher learning effect. Furthermore, this paper briefly discusses an on-demand learning system that has been developed to assist foreign language learners in creation of vocabulary learning materials.

With rapid growth of producing high-resolution digital contents such as Full HD, 4K, and 8K movies, the demand for low cost and high throughput sharing of content files is increasing at digital content productions. In order to meet this demand, we have proposed DRIP (Distributed chunks Retrieval and Integration Procedure), a storage and retrieval mechanism for large file sharing using forward error correction (FEC) and global dispersed storage. DRIP was confirmed that it contributes to low cost and high throughput sharing. This paper describes the design and implementation of Content Espresso, a distributed large file sharing system for digital content productions using DRIP, and presents performance evaluations. We set up experimental environment using 79 physical machines including 72 inexpensive storage servers, and evaluate file metadata access performance, file storage/retrieval performance, FEC block size, and system availability by emulating global environments. The results confirm that Content Espresso has capability to deal with 15,000 requests per second, achieves 1 Gbps for file storage, and achieves more than 3 Gbps for file retrieval. File storage and retrieval performance are not significantly affected by the network conditions. Thus, we conclude that Content Espresso is capable of a global scale file sharing system for digital content productions.

In the era of e-commerce, purchase behavior prediction is one of the most important issues to promote both online companies' sales and the consumers' experience. The previous researches usually use traditional features based on the statistics and temporal dynamics of items. Those features lead to the loss of detailed items' information. In this study, we propose a novel kind of features based on temporally popular items to improve the prediction. Experiments on the real-world dataset have demonstrated the effectiveness and the efficiency of our proposed method. Features based on temporally popular items are compared with traditional features which are associated with statistics, temporal dynamics and collaborative filter of items. We find that temporally popular items are an effective and irreplaceable supplement of traditional features. Our study shed light on the effectiveness of the combination of popularity and temporal dynamics of items which can widely used for a variety of recommendations in e-commerce sites.