Abstract As digital transformation accelerates, data generated in a convergence information and communication technology (ICT) environment must be secured. This data includes confidential information such as personal and financial information, so attackers spread malware in convergence ICT environments to steal this information. To protect convergence ICT environments from diverse cyber threats, deep learning models have been utilized for malware detection. However, accurately detecting rapidly generated variants and obfuscated malware is challenging. This study proposes a three‐tier malware detection (TMaD) scheme that utilizes a cloud‐fog‐edge collaborative architecture to analyse multi‐view features of executable files and detect malware. TMaD performs signature‐based malware detection at the edge device tier, then sends executables detected as unknown or benign to the fog tier. The fog tier conducts static analysis on non‐obfuscated executables and those transferred from the previous tier to detect variant malware. Subsequently, TMaD sends executables detected as benign in the fog tier to the cloud tier, where dynamic analysis is performed on obfuscated executables and those detected as benign to identify obfuscated malware. An evaluation of TMaD's detection performance resulted in an accuracy of 94.78%, a recall of 0.9794, a precision of 0.9535, and an f1‐score of 0.9663. This performance demonstrates that TMaD, by analysing executables across several tiers and minimizing false negatives, exhibits superior detection performance compared to existing malware detection models.

Wireless sensor network plays a crucial role in sensing everything in Internet of Everything (IoE) applications. Network reliability, which measures the ability of the network to satisfy specific requirements, is one of the core factors influencing the quality of service of the network and a vital support for ensuring the normal operation of IoE applications. Existing reliability evaluation methods are mainly based on minimum cutsets or paths, which are inefficient and not suitable for large-scale networks. Furthermore, most work either focuses on coverage functionality or connectivity functionality, lacking energy awareness. To address these limitations, this article proposes a multidimensional target full-coverage reliability (TFCR). TFCR comprehensively considers various factors affecting network reliability. To evaluate TFCR, a graph-empowered confident information coverage (CIC) and signal-to-interference and noise ratio (SINR)-based energy-aware reliability algorithm (CSERA) is proposed. This algorithm evaluates network coverage based on the CIC model. Additionally, graph neural networks and the SINR-based fade tail connectivity (FTC) model are used to evaluate network connectivity functionality. CSERA balances computational accuracy and efficiency, providing reliability evaluation values within an acceptable margin of error. Extensive simulations and comparative experiments from multiple perspectives demonstrate the superiority of the proposed method CSERA over existing approaches.

The container resource autoscaling techniques offer scalability and continuity for microservices operating in cloud-native computing environments. However, they manage resources inefficiently, causing resource waste and overload under complex workload patterns. In addition, these techniques fail to prevent oscillations caused by dynamic workloads, increasing the operational complexity. Therefore, we propose an adaptive resource autoscaling scheme (ARAScaler) to ensure the stability and resource efficiency of microservices with minimal scaling events. ARAScaler predicts future workloads using enhanced TimeMixer (ETimeMixer) applied with the convolutional method. Additionally, ARAScaler segments the predicted workload to identify burst, nonburst, dynamic, and static states and scales by calculating the optimal number of container instances for each identified state. The offline simulation results using seven cloud-workload trace datasets demonstrate the high prediction accuracy of ETimeMixer and the superior scaling performance of ARAScaler. The ARAScaler achieved a resource utilization of approximately 70% or higher with few updates and recorded the fewest resource overload instances compared to existing container resource autoscaling techniques.

With the steady increase in the demand for Internet of Things (IoT) devices in diverse industries, such as manufacturing, medical care, and transportation infrastructure, the production of malware tailored for Smart IoT environments is also increasing. Accordingly, various malware detection studies are being conducted to detect not only known malware but also variant malware. However, it is difficult to detect malware transformed in a way that hides malicious behavior by changing and deleting bytes or modifying the assembly code. Therefore, in this study, we propose a malware detection for static security service (Mal3S) scheme that provides a secure Smart IoT environment by accurately detecting various types of malware. Mal3S extracts bytes, opcodes, API calls, strings, and dynamic link libraries (DLLs) through static analysis and then generates five types of images. Images of various sizes are trained on a multi spatial pyramid pooling network (SPP-net) model to detect malware. When evaluating the performance of Mal3S using three malware datasets, the average detection accuracy was 98.02% and the classification accuracy was 98.43%, showing better performance than existing malware detection techniques. In addition, Mal3S has demonstrated effective generalization capabilities for various types of malware.

The healthcare industry is collecting considerable patient and medical data by using Internet of Things (IoT) devices.Consequently, ransomware attacks to encrypt healthcare systems or leak such data have increased recently.Many studies are aiming to predict ransomware behaviours early to protect the healthcare IoT environment from such attacks.However, previous studies analysed ransomware behaviours for long periods of time, and systems would already get infected and encrypted meanwhile.To avoid this problem, this study proposes an early prediction scheme of ransomware behaviour (EPS-Ran) to reduce the likelihood of systems being infected during behavioural analysis.EPS-Ran analyses behaviours for 30 s to extract the opcode and API calls sequence.The extracted behaviour features are entered into a hybrid deep learning model that combines the bidirectional gated recurrent unit (Bi-GRU) model and the temporal convolutional network (TCN) model to predict a future 90 s API calls sequence.The MAE, MSE, and RMSE of the prediction performance of EPS-Ran were measured to be 0.3438, 0.5648, and 0.6342, respectively.EPS-Ran predicted ransomware behaviours early with a low error rate even when the analysis time was reduced from 120 s to 30 s.

The container resource autoscaling technique provides scalability to cloud services composed of microservice architecture in a cloud-native computing environment. However, the service efficiency is reduced as the scaling is delayed because dynamic loads occur with various workload patterns. Furthermore, estimating the efficient resource size for the workload is difficult, resulting in resource waste and overload. Therefore, this study proposes high-performance resource management (HiPerRM), which stably and elastically manages container resources to ensure service scalability and efficiency even under rapidly changing dynamic loads. HiPerRM forecasts future workloads using a sample convolutional and interaction network (SCINet) model applied with the reversible instance normalization (RevIN) method. HiPerRM generates a resource request with an elastic size based on the forecasted CPU and memory usage, and then efficiently adjusts the pod's resource request and the number of replicas via HiPerRM's VPA (Hi-VPA) and HiPerRM's HPA (Hi-HPA). As a result of evaluating the performance of HiPerRM, the average resource utilization was improved by approximately 3.96–34.06% compared to conventional autoscaling techniques, even when the resource size was incorrectly estimated for various workloads, and there were relatively fewer overloads.

In this article, we propose the hybrid malware detection scheme, HyMalD, with bidirectional long short-term memory (Bi-LSTM) and the spatial pyramid pooling network (SPP-Net). Its purpose is to protect Internet of Things (IoT) devices and minimize the damage caused by infection through obfuscated malware. HyMalD performs the static and dynamic analyses logically simultaneously to detect obfuscated malware, which is impossible to do using static analysis alone. First, it extracts static features of the opcode sequence using a reconstructed dataset according to the obfuscation and extracts the application programming interface (API) call sequence dynamically. The extracted features are trained through the Bi-LSTM and SPP-Net models, which HyMalD uses to detect and classify IoT malware. The performance of HyMalD was evaluated, and its detection accuracy was 92.5%. The false-negative rate (FNR) of HyMalD was 7.67%. Thus, HyMalD detects IoT malware more accurately and with a lower FNR than in the static analysis, which had 92.09% detection accuracy and 9.97% FNR.

Summary: Next-generation sequencing (NGS) technologies have led to the accumulation of high-throughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals. We have developed a web application AGORA for the fast, user-friendly and improved annotations of organellar genomes. Annotator for Genes of Organelle from the Reference sequence Analysis (AGORA) annotates genes based on a basic local alignment search tool (BLAST)-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon-intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence and visualization of gene map by OGDRAW. Availability and implementation: Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/. The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. Supplementary information: Supplementary data are available at Bioinformatics online.

Technological advances over the last decade in the field of wireless communications have resulted in the improvement of small and low cost sensor nodes outfitted with wireless communication abilities capable of establishing wireless sensor network (WSN). Due to the expansion of Internet of Things (IoT), there are many areas in IoT application where WSN applications are found. These applications generally impose severe constraints on the lifetime of the WSN, which is expected to last several years. It is necessary to diminish the overall energy consumption of the sensor node and to find an additional source of energy for achieving this objective. On the other hand, due to the imminent crisis of the radio frequency spectrum, light fidelity (LiFi) offers many key benefits and effective solutions for these issues that have been postured in the most recent decade. In this paper, we propose a novel EH-HL model for future smart homes and industries based on the integration of energy harvesting WSN (EH-WSN) and hybrid LiFi/WiFi communication techniques. The proposed model is capable of efficiently transmitting data at high speed for bidirectional multidevice and by harvesting energy, we provide the power to the sensor nodes. To synchronize multidevice transmissions, transmit data and provide low-cost wireless communication, we used the color beams of the red, green, and blue LEDs. The result of the evaluation shows that the hybrid communication scheme is proposed in the EH-HL model. It also offers superior performance and achieves a data rate of 25 Mb/s for multiaccess/multiusers.

Cloud computing provides a useful metaphor for combining capability at different scales. Such environments may therefore consist of devices ranging from handheld smart phones to supercomputers, to serve communities ranging from individuals to whole industries. Cloud computing requirements are also widely regarded as a key enabler for next generation network environments and are expected to drive the creation of both jobs and commercial products. Cloud computing is also sometimes referred synonymously as next generation data centers, with an architecture consisting of network of virtual services (hardware, database, user-interface, and application logic) so that users are able to deploy and access applications globally and on demand at competitive costs depending on user driven quality of service requirements. Developers with innovative ideas for new Internet services no longer require large capital outlays in hardware to deploy their services, or human expense to operate it 1-7. This technology is being driven by and used in a wide range of academic, research, and commercial application areas. This use is producing important new practical experience in a variety of different problem domains in each of these areas. There are also new computational methods, such as mobile agents, cellular automata, and massively parallel neural networks, which are particularly suited to concurrent execution. As an enabler, this technology is leading to a rapid growth in both scientific and information applications that will, in turn, enable additional requirements for cloud computing technologies to be identified. These will impact academia, business, and education 4-6, 8-12. Given this context, this special issue calls for high-quality research papers in the development of cloud computing technology for mobile environments. In particular, the special issue showcased the most recent achievements and developments in the realm of cloud computing. Original research articles were solicited covering theoretical studies, practical applications, new communication technology, and experimental prototypes. All submitted papers were peer reviewed and selected on the basis of both their quality and their relevance to the theme of this special issue. The published papers are expected to focus on novel approaches for advanced cloud computing of mobile environments for future computing environments and to present high-quality results for tackling problems arising from the ever-growing advanced cloud computing technologies and services for future computing environments. We have received many manuscripts. Only five manuscripts with high quality were finally selected for this special issue. Each manuscript selected was blindly reviewed by at least three reviewers consisting of guest editors and external reviewers. We present a brief overview of each manuscript in the following. Recent advances in cloud computing for mobile environments have created new topics of interest that included the following: (1) cloud economics in mobile and pervasive environments; (2) cloud computing programming and application development; (3) scalability, discovery of services, and data in cloud computing infrastructures; (4) trust and clouds for mobile infrastructure; (5)client-cloud computing challenges; (6) grid computing services and applications for cloud deployments; (7) virtualization, modeling, and metadata in grid; (8) cloud monitoring, control, and management; (9) traffic and load balancing in multi-vendor cloud environments; (10) user profile for cloud environments; (11) performance and security in pervasive and ubiquitous cloud environments; (12) fault tolerance, resilience, survivability, and robustness in cloud environments; and (13) geographical constraints for deploying clouds. And also, the special issues include manuscripts of applications of cloud computing for mobile environments as follows: (1) concurrent solutions to specific problems in academia, industry, and society; (2) concurrent algorithms and computational methods; (3) programming environments, operating systems, tools, concurrent languages, compilers, and interpreters; (4) performance prediction, analysis, models, and results; (5) applications, algorithms, and software technologies arising from the World Wide Web; (6) unification of computing and communication and unification of parallel and distributed computing; (7) social network analysis facilitated through cloud environments; (8) ad hoc and mesh networks via cloud systems; and (9) managing streaming content with cloud environments. In these several topics of cloud computing, some articles proposed the following: Park et al. 13 proposed and experimented the group classification for a dynamic environment, where information of mobile devices is changing. The existing studies, which provide arbitrary cutoff points, are not appropriate in a dynamic environment. The proposed method provides dynamic grouping by setting cutoff points and reflecting the changes of mobile devices after calculating the entropy values. We also proposed a fault tolerance technique for a dynamic environment based on our grouping algorithm. In the proposed algorithm, different fault tolerance techniques are applied to every group according to the different information of mobile devices of each group. Choi and Lim 14, in order to maximize the parallelism within a basic block, we exploit a novel register allocation approach, called as alternative register allocation. The alternative register allocation enables the target processors to improve the parallelism by eliminating the instruction dependency within a translated block. Alternative register allocation divides the register set of the target processor into two separate groups that are used to alternately handle translated blocks. This paper achieved performance improvement by using the two sets of register allocation orders. Alternative register allocation is to maximize the pipelining performance, which is a very popular technique for all modern microprocessors, eliminating the false data dependency. Thus, this paper resolves the false dependency problem and outperforms conventional approach by up to 26.3% for some cases. Sood 15 provided an efficient prediction model for adaptive resource provisioning in the cloud for dynamic resource management. The strategy used in this paper helps the cloud service provider for optimizing resource allocation as well as making the cloud cost-effective. Load-balancing algorithm is used to divide the resource requirements of application in different clouds and, at the same time, to minimize the cost to the best possible extent. The approach used in this paper is best suitable for making the decision to allocate or release a Virtual Machine (VM) in time. This paper had also evaluated the metrics described in manuscript in terms of cost and functionality by performing various experiments. These experiments validate the accuracy of the proposed method, and the results show that the prediction accuracy of the proposed technique is considerably effective. Thus, the proposed approach is highly effective in terms of both cost and functionality. Wang et al. 16 provided a dynamic power model based on division of computation and memory and a static power model based on real-time temperature perception. The evaluation of nine typical applications showed that with the method proposed in this research, about 35% of dynamic energy (within 10% error bounds) subject to performance constraint conditions can be saved, while about 21.6% of static energy could be saved with a performance loss of less than 2.8%. De and De 17 proposed a new mobile cloud architecture, where the interactions between mobile applications and mobile cloud services are decoupled during service invocation and delivery phases. The tuple space model is used in this architecture to decouple such interactions, thereby improving adaptability and robustness of mobile cloud computing. This paper also suggested an approach of modeling and formally verifying the correctness of the proposed architecture, which also validated its adaptability and robustness during interactions between mobile applications and mobile cloud services. The modeling and verification are carried out using the mobile UNITY model. Our special thanks go to Professor Geoffrey C. Fox and Professor David W. Walker who are editors-in-chief of Concurrency and Computation: Practical and Experience and all editorial staffs for their valuable supports throughout the preparation and publication of this special issue. We would like to thank all authors for their contributions to this special issue. We also extend our thanks to the external reviewers for their excellent help in reviewing the manuscripts.

Abstract In order to secure the content of simple object access protocol (SOAP) messages in Web services, several security standards of Web service security, such as XML digital signature, are used. However, the content of a SOAP message, protected with XML digital signature, can be altered without invalidating the signature. Existing methods for detecting XML rewriting attacks are inefficient because the cost of performing detection operation is linear to the height of the SOAP message tree. Thus, each element of SOAP message needs to be accessed and checked. In this paper, we propose an efficient method for detecting XML rewriting attacks on SOAP messages using a histogram. With our method, once the source of attacks is identified, we save it in the form of a histogram, which enables us to maintain a statistical information about the location of the attack in the SOAP message. We can use this information to detect attacks in the future and thus avoid unnecessary check of all elements in the SOAP message. Experiments show that our methods outperform existing methods by several times in many cases. Copyright © 2014 John Wiley & Sons, Ltd.

Recently wireless sensor networks have been used as the technology that is actively grafted onto industries and daily living. Sensors should have built-in routing functions and basic sensing functions for the self-configuration of topologies. The number of sensors necessary for using them in an actual observation ranges from tens to hundreds or thousands. When theses sensors are wrongly placed in an observation region, they can quickly run out of batteries or be disconnected. These incidents may result in huge losses in terms of sensing data from numerous sensors and their costs. Therefore a number of simulators have been developed as tools for effective design and verification before the actual arrangement of sensors. While a number of simulators have been developed, simulation results can be fairly limited and the execution speed can be markedly slow depending on the function of each simulator. To improve the performance of existing simulators, this paper aimed to develop a parallel processing simulator for separate sensor (P2S3) that enables users to selectively use the GPU mode. It enables parallel and independent operations by matching GPU with many cores in order to resolve the slowdown of the execution speed when numerous sensor nodes are used for simulations. Also, P2S3 include the analyzed of sensor nodes with log data and visualization. The P2S3 supports the GPU mode in an environment that allows the operation of compute unified device architecture (CUDA), and performs the parallel simulation processing of multiple sensors using the mode within a short period of time.

In the past few years, cloud computing has emerged as a new reliable, scalable and flexible virtual computing environment (VCE). In this new VCE, users can use the available resources as a service by paying for that service according to the time for which these resources are used. It remains a significant challenge to achieve quality of service (QoS) in a VCE with the available resources. The main goal is to schedule the available resources so that the overall QoS delivered by the VCE can be improved. Resources are assumed to be located both at local and global sites. We propose a three-step scheme: resource selection, scheduling of users requests with shared resources and a new Resource Allocation and Adaptive Job Scheduling algorithm, which improves the QoS delivered by the cloud. For job scheduling, we define a new weight metric that is used to efficiently schedule jobs competing for available resources. Our proposed strategy increases the reliability of resource availability for a job and reduces the job completion time, which in turn increases the QoS delivered to end-users. We evaluate our proposed scheme using well-known heuristics. The results obtained show that our proposed scheme considerably reduces the job execution time, and increases the reliability of resource availability for job execution and throughput.

ABSTRACT This paper proposes the trusted platform board monitoring (TPBM) system that enabled effectively detecting and managing abnormal phenomenon based on the trusted platform board not only for security but also for multiple clients connected on the web. In other words, TPBM strengthens weak points of the existing security and provides the function to monitor with human‐centric method not only system resources and process status for remote multi‐client systems that are operated based on hardware security but also behavior of multiple clients in the cloud computing environment. In addition, measuring instruction level behavior is potentially a more effective protection than a pure software approach. From program's run‐time behavior, system state safety can also be analyzed. However, dynamic characteristic of program trace also introduces challenges on what to measure and how to measure. To solve such problems, branch instruction trace (BiT) profiling tool is introduced to measure processor's instruction level run‐time behavior. However, the BiT has provided only the text results of logical error information but not the visual monitoring function by tracing abnormal branch at the level of instruction. In order to resolve this problem, this paper includes the human‐centric visual monitoring function for information on abnormal branch trace to TPBM. Copyright © 2012 John Wiley & Sons, Ltd.

The objectives of this special issue are advanced security technologies and services for future computing environments, including, but not limited to, security primitives, protocols and security applications and services. Recent advances in security technologies and services for future computing environments have created a new class of the following: (i) Wireless sensor networks and radio-frequency identification security and privacy; (ii) security architectures for distributed network systems, P2P systems, cloud and grid systems; and (iii) security in e-commerce, mobile and wireless networks, and finally, security standards and assurance methods. All papers are expected to focus on novel approaches for advanced security technologies and services for future computing environments and to present high-quality results for tackling problems arising from the ever-growing advanced security technologies and services for future computing environments.

Recent development of high-speed wireless networks and embedded systems has enabled the recording and delivery of high-performance multimedia to heterogeneous mobile users. To support heterogeneous mobile users with high-quality multimedia services, scalable video coding was introduced. In the scalable video coding (SVC), through multidimensional scalability, all types of these scalability can be exploited at the same time. However, the generated video sequences of scalable video coding are not adequate for mobile multimedia service systems since its flexibility makes non contiguous storing and retrieval of partial stream data. In this article, we propose efficient scalable video data reorganization for video servicing systems, which consist of video server and mobile clients. For video server, we reorganize scalable video streams taking into account both of decoding dependency and location in disk array storage, where disk array is widely used for storage systems of video server. In the mobile devices, we place substreams with the consideration of NAND flash memory page and block boundaries, which is storage for mobile devices. The experimental results show that the proposed reorganization of scalable video can improve the performance of mobile multimedia service systems.

One of most critical issues in power-constrained wireless sensor networks is to maximize the network lifetime. Recently, studies have been made on scheduling sensor activity to prolong network lifetime. In this paper, we address the maximum set covers (MSC) problem to maximize the network lifetime and propose an energy-efficient sensor scheduling algorithm for the MSC problem based on the branch and bound approach. Simulation results show that the proposed algorithms can contribute to extending the network lifetime and its performance is better than the existing greedy-based algorithm.

This paper implements U-LES system that supports automotive emergency call service. U-LES system contains E-mobile device (emergency mobile device) that is feasible even to the handicapped, home-alone elders, or young children, and E-server (emergency server) that could monitor and manage such E-mobile device in real-time. In any emergent cases, E-mobile device automatically requests help through wired/wireless network from any emergency center or proper guardian. E-mobile device is light- weighted, cheap, and designed in such way that even the elders or children could easily use. E-server is a comprehensive system for E-mobile device management that contains inclusive signal management feature to support other heterogeneous types of networks such as CDMA, TRS, or USN. For superior emergency system and efficient E-mobile device, E-server also provides real-time monitoring feature.

The study designed cooperative learning objects, with which users can exchange information with others in the cooperative learning space on the Web, and developed a Web platform that supports the object. The cooperative learning object was designed by extending API and data model provided by RTE of SCORM, and the Web platform was developed in three layers - service base layer, service management layer and service support layer. In addition, this study suggested a plan to utilize them.

Recently, hierarchical architecture for location databases has been proposed in order to accommodate the growing number of PCSs user. With the 3-level hierarchical database architecture, which is compatible with the current cellular mobile systems, newly developed additional databases, including the regional location database (RLR), are positioned between the HLR and the VLRs. We propose an efficient cache scheme, the Double T-thresholds Location Cache Scheme, could reduce the network and database costs to lookup a portable using the 3-level architecture. The idea behind our proposed scheme is to use two pieces of cache information, VLR and RLR, which are serving the called portables. These two pieces are required in order to exploit not only the locality of a registration area (RA) but also the locality of a regional registration area (RRA), a wide area covered by the RLR. We also use two threshold values in order to determine whether the two pieces are obsolete. The cost analysis presented in this paper shows that the double T-thresholds location cache scheme significantly reduces network and database costs for most patterns of portables.

This paper proposes a restoration scheme based on mobility learning and prediction in the presence of the failure of mobility databases in personal communication systems (PCSs). In PCSs, mobility databases must maintain the current location information of users to provide a fast connection for them. However, the malfunction of mobility databases may cause some location information to be lost. As a result, without an explicit restoration procedure, incoming calls to users may be rejected. Therefore, an explicit restoration scheme against the malfunction of mobility databases is needed to guarantee continuous service availability to users. Introducing mobility learning and prediction into the restoration process allows systems to locate users after a failure of mobility databases. In failure-free operations, the movement patterns of users are learned by a neuro-fuzzy inference system (NFIS). After a failure, an inference process of the NFIS is initiated and the users' future location is predicted. This is used to locate lost users after a failure. This proposal differs from previous approaches using a checkpoint because it does not need a backup process nor additional storage space to store checkpoint information. In addition, simulations show that our proposal can reduce the cost needed to restore the location records of lost users after a failure when compared to the checkpointing scheme.