Abstract Indoor smartphone positioning is one of the key information and cummunication technology techniques enabling new opportunities for indoor navigation and mobile location‐based services to enrich our everyday lives. Generally, the development of an indoor positioning system heavily relies on wireless sensor network. Since wireless sensors can estimate the probable distance between radio source and the sensors themselves by evaluating the strengths of wireless signals received from radio sources, such as received strength signal indications of Wi‐Fi and Bluetooth. However, the radio signals could be influenced by indoor and outdoor objects, such as walls and furniture, and carrying mode of a user's smartphone, like in‐pocket or in‐backpack. But, according to the best of our knowledge, up to present, people do not know how carrying mode information (CMI) influences the positioning accuracy of a positioning system. Therefore, in this study, we propose an indoor positioning scheme, named LEarning‐based Indoor Positioning System (LEIPS), which identifies the carrying mode of a user's smartphone by using this smartphone's inertial sensors and deep learning algorithms, aiming to increase indoor positioning accuracy. Our experimental results demonstrate that this system reaches 96% of positioning accuracy. CMI is also validated, showing that it is able to improve indoor prediction accuracy.

In advanced data processing systems, one of the most important paradigms for distributed data analysis is innovative and transformative computing approaches. Such solutions allow not only data analytics tasks to be facilitated, but also intelligent and secure information analysis oriented especially for applications of new technologies and computational intelligence techniques. Nowadays there is a great demand to efficiently store and analyze a huge amount of information, originated from distributed sources or sensors, as well as an expectation to manage such information in a secure manner for applications in ubiquitous and mobile computing. The possibility of creation and development of such computation technologies will be connected with the introduction of new transformative computing procedures dedicated to security, big data analysis and cloud computing technologies. These subjects, as well as others, connected with innovative computational models for transformative computing technologies, data security, security protocols, and distributed data analysis will form the subject of this Transformative 2020 special issue. The main topics of transformative computing in security, big data analysis and cloud computing applications presented at Transformative 2020 are primarily oriented at new computational approaches for big data and cloud security, transformative computing applications, personalized cryptography and biometric security, ambient intelligence, innovative security and privacy protocols, security of cognitive information systems, cryptography and secret data management, computational intelligence in data and services management, security and privacy for mobile and distributed systems, visual and cognitive CAPTCHA, cognitive approaches for big data analytics, advanced cognitive steganography systems, behavioral features in data analysis and security solutions, and transformative approaches for big data analytics. This special issue features 12 papers, which present high quality scientific research, and interesting cutting-edge topics. The first paper entitled “Customizing intelligent recommendation study with multiple advisors based on hierarchy structured fuzzy-analytic hierarchy process” by Park et al.1 proposes a new system that integrates a multi advisory function. The proposed solution starts from problem definition and continues to define the required solving task. Such technology supports customized information on defining problems and enables the definition of the requirements to characterize user features. The second paper “Time-based legality of information flow in the capability-based access control model for the Internet of Things” by Nakamura et al.2 introduces a new idea of time based legality of information flow. In the capability-based access control (CBAC) model, each subject has a capability token with which users can effectively manage a device. The authors propose a time-based operation interruption protocol (TBOI) to prevent illegal information—at any given time and at a later date. The third paper entitled “Transformative and Cognitive Approaches to Information Retrieval and Security Procedures” by Ogiela3 describes cognitive approaches in data security and a transformative computing paradigm dedicated to creation of human-centered security protocols. Such transformative computing applications focus on data exploration in distributed systems. The fourth paper “A Machine Learning-Based Memory Forensics Methodology for TOR Browser Artifacts” by Pizzolante et al.4 presents a bottom-up formal investigation model for the memory forensics of the Tor Browser. This methodology was developed based on a bottom-up logical approach for collecting information from different abstraction levels. The fifth paper entitled “Healthcare Fraud Detection Using Primitive Sub Peer Group Analysis” by Settipalli and Gangadharan5 presents new algorithms for identifying suspicious behaviors in health insurance. In this paper, a primitive sub peer group analysis (PSPGA) based on peer group analysis (PGA) and pattern interpretation and analysis is proposed for identifying user behaviors. The PSPGA recognizes drifts and classifies them as correct or fraudulent. The sixth paper “New Cognitive Sharing Algorithms for Cloud Service Management” by Ogiela and Ogiela6 presents new algorithms based on the meaning description dedicated to data analysis and security processes. The authors describe the possibility of applying such algorithms, depending on the structure of the target system, especially in cloud computing. This paper also discusses examples of two-stage secret protection algorithms—the simple data protection and the information set with semantics. The seventh paper entitled “Protocol Fuzzing to Find Security Vulnerabilities of RabbitMQ” by Kwon et al.7 shows a new fuzzy protocol for systems and service communications. A message broker named RabbitMQ is developed to find unknown vulnerabilities inherent in software. Their simulations demonstrate that the proposed algorithm is able to solve tasks by using the RabbitMQ especially in data security processes. The eighth paper “A deep learning-based indoor-positioning approach using received strength signal indication and carrying mode information” by Lin et al.8 describes a new indoor positioning scheme—learning-based indoor positioning system (LEIPS) which is used for identification of smartphone users by using inertial sensors and deep learning algorithms. Their experimental results demonstrate that the LEIPS has reached 96% of positioning accuracy. The ninth paper entitled “Optimizing Resource Scheduling Based on Extended Particle Swarm Optimization in Fog Computing Environments” by Narayana et al.9 introduces the extended particle swarm optimization (EPSO) algorithm which was developed with additional gradient method for optimizing scheduling tasks in cloud-fog environments. It also improves the efficiency of data analysis and minimizes the time of their implementation. The tenth paper “Population Data Mobility Retrieval at Territory of Czechia in Pandemic Covid-19 Period” by Platos et al.10 addresses the selection and collection steps of data analysis on mobile phones at the Czech Republic during the Covid pandemic. A data collection architecture is then proposed for spatial temporal mobility analysis. The analysis precision including the pandemic and non-pandemic periods is also shown. The eleventh paper entitled “Design and Analysis of Efficient Neural Intrusion Detection for Wireless Sensor Networks” by Batiha and Krömer11 analyses the acceleration of a neural intrusion detection model. The model was developed to detect intrusion/malicious behaviors for wireless sensor networks. The authors present their computational experiments with classification accuracy and training efficiency on different devices. In the last paper “Improved Publicly Verifiable Auditing Protocol for Cloud Storage” by Zhang et al.,12 the authors describe an outsourcing data protocol for cloud systems which is one of the new full integrity cloud auditing protocols. In addition to identifying its weaknesses, authors also analyze this protocol and present its security issues. This special issue introduces new solutions of transformative computing paradigms on different important topics. A part of them focuses on security and system protection. Some solve urgent network problems. All these techniques are oriented from information flow processes and machine learning in transformative computing, cognitive and semantic description of data management and security, fuzzy protocols and deep learning positioning, intrusion detection, fog-cloud applications, and big data collection and analysis processes. The wide range and impacts of the presented papers indicate an extraordinary variety of transformative computing paradigms, applications, and approaches. The authors also raise important open topics and their solutions. Following that, authors also show the possibility of further development on the discussed aspects. We would like to specially thank the Editors in Chief—Professor Geoffrey Fox, who first gave the opportunity of publishing this issue, and Professor David W. Walker who led this work at all times to the end. We are especially grateful for the opportunity to present this Special Issue and for their great kindness and help, as well as the unique opportunity to present new and interesting scientific works in Concurrency and Computation: Practice and Experience. We would also like to thank all the authors who have submitted their papers to this Special Issue. We congratulate all authors whose works have been accepted and positively evaluated. These works bring a great contribution to the development of the computer science, show new directions for research, as well as an innovative view of the previously developed experience and science.

Summary Currently, wireless sensor networks (WSNs) have been widely developed in different domains. Generally, sensors work independently, so it is relatively easier for a WSN to add new nodes. Often, a WSN is constrained to low energy consumption. However, data encryption/decryption not only takes a long time but also consumes many network resources. Due to limited battery power and CPU capability, a sensor's encryption algorithm should not be complicated. However, a simple security algorithm will downgrade the system's security level. Hence, designing a secure wireless transmission system with high encryption/decryption performance, particularly for 5G networks, is an urgent task. Therefore, in this study, we propose an efficient and secure approach, named the Symmetric Key Matrix and Rectangular Frame Scheme (SKeMRF, for short), which involves security mechanisms to speed up encryption/decryption operations for a WSN. Further, the SKeMRF integrates global Symmetric Key Matrix (SKM, for short) and other security mechanisms to ensure the security of transmitted data and enable the system to effectively resist the forgery KDS attack, thus greatly enhancing security of the whole system. Theoretical analysis and simulation show that the SKeMRF is an efficient and highly secure WSN compared with those state‐of‐the‐art schemes.

In this paper, we design an enhanced access network discovery and selection function (ANDSF), named E-ANDSF, which provides previous mobile access gateway (PMAG) with information for base station selection, and propose an improved IP-based handover/offload scheme, calledIP-basedHandover/offload system assisted byE-ANDSF (IHEAD), which invokes multilayer perceptron, a machine learning technique, to select a base station for user equipment to handover or offload network traffic to wireless local area network (WLAN). The purpose is improving network service quality with the assistance of stream control transmission protocol and E-ANDSF. Experimental results show that the IHEAD outperforms those tested schemes.

Over the past twenty years, the self-report scales have been used widely to measure learners' cognitive perceptions and behaviors in the contexts of learning. As the rapid development of analytical techniques, there is a need to incorporate appropriate computer algorithms to better analyze and comprehend learners' characteristics and their learning. This study therefore uses questionnaires data but applies machine learning (ML) techniques to discover which significant attributes that a successful learner often demonstrates in a computer course. Five ML algorithms include decision trees, naïve Bayes, support vector machines, multilayer perceptron and logistic regression are conducted to compare the performance of their accuracies, precisions and sensitivities. The results suggest that naïve Bayes is the most appropriate one for predicting students' final performance. The accuracy and sensitivity measure of this classifier achieve 77.53% and 88.68%, respectively. The classification model is presented in this paper and an attempt for improving classification accuracy is suggested.

Summary In recent years, by merit of convenient and unique features, bio‐authentication techniques have been applied to identify and authenticate a person based on his/her spoken words and/or sentences. Among these techniques, speaker recognition/identification is the most convenient one, providing a secure and strong authentication solution viable for a wide range of applications. In this paper, to safeguard real‐world objects, like buildings, we develop a speaker identification system named mel frequency cepstral coefficients (MFCC)‐based speaker identification system for access control (MSIAC for short), which identifies a speaker U by first collecting U's voice signals and converting the signals to frequency domain. An MFCC‐based human auditory filtering model is utilized to adjust the energy levels of different frequencies as U's voice quantified features. Next, a Gaussian mixture model is employed to represent the distribution of the logarithmic features as U's specific acoustic model. When a person, eg, x, would like to access a real‐world object protected by the MSIAC, x's acoustic model is compared with known‐people's acoustic models. Based on the identification result, the MSIAC will determine whether the access will be accepted or denied.

Nowadays, many speech recognition applications have been used by people in the world. Typical examples are the SIRI of iPhone, Google speech recognition system, and mobile phones operated by voice, etc. On the contrary, speaker identification in its current stage is relatively immature. Therefore, in this paper, we study a speaker identification technique which first takes the original voice signals of a person, e.g., Bob, and then normalizes the audio energies of the signals. After that, the audio signals is converted from time domain to frequency domain by employing Fourier transformation approach. Next, a MFCC-based human auditory filtering model is utilized to identify the energy levels of different frequencies as the quantified characteristics of Bob's voice. Further, the probability density function of Gaussian mixture model is utilized to indicate the distribution of the quantified characteristics as Bob's specific acoustic model. When receiving an unknown person, e.g., x's voice, the system processes the voice with the same procedure, and compares the processing result, which is x's acoustic model, with known-people's acoustic models collected in an acoustic-model database beforehand to identify who the most possible speaker is.

Internet of Things (IoTs) is an enabler for the intelligence appended to many central features of the modern world, such as hospitals, cities, grids, organizations, and buildings. The security and privacy are some of the major issues that prevent the wide adoption of IoTs. Its storage and computing power of these devices is often limited and their designs currently focus on ensuring functionality and largely ignore other requirements, including security and privacy concerns. Role-based Access Control in a hierarchy is an interesting research topic in computer security. Many investigations have been published in the literature with solutions involving assigning cryptographic keys to users for secure scheme in IoTs environments. The new security requirements are required by secure grouping IoTs, which are required for new applications of grouping IoTs. However, the existing secure schemes require a large number of costly arithmetic operations with large integers. The traditional scheme is difficult to implement among the IoTs devices groups with lower computation ability. In this paper, we present a solution, an implement of agent with Role-based Hierarchy Access Control for secure grouping IoTs, suitable for role-based access control in IoTs groups. The proposed scheme has promising characteristics such as high computational efficiency, little required memory and low cost implementation in each IoT device organized to be an grouping IoTs with a secure hierarchy key management.

Internet of Things (IoTs) is an enabler for the intelligence appended to many central features of the modern world, such as hospitals, cities, grids, organizations, and buildings. The security and privacy are some of the major issues that prevent the wide adoption of IoTs. Its storage and computing power of these devices is often limited and their designs currently focus on ensuring functionality and largely ignore other requirements, including security and privacy concerns. Role-based Access Control in a hierarchy is an interesting research topic in computer security. Many investigations have been published in the literature with solutions involving assigning cryptographic keys to users for secure scheme in IoTs environments. The new security requirements are required by secure grouping IoTs, which are required for new applications of grouping IoTs. However, the existing secure schemes require a large number of costly arithmetic operations with large integers. The traditional scheme is difficult to implement among the IoTs devices groups with lower computation ability. In this paper, we present a solution, an implement of agent with Role-based Hierarchy Access Control for secure grouping IoTs, suitable for role-based access control in IoTs groups. The proposed scheme has promising characteristics such as high computational efficiency, little required memory and low cost implementation in each IoT device organized to be an grouping IoTs with a secure hierarchy key management.

Today, due to the popularity and convenience of wireless communications, people communicate with others by using mobile phones almost every day, as one of their important daily activities. Currently, LTE-4G is the mainstream of mobile communication. Recent studies on the upcoming LTE-5G wireless environments employ AES to protect delivered messages. However, using this cryptography standard to encrypt/decrypt messages will cause the performance bottlenecks. Also, AES will be insecure in the near future since it has been partially solved. In other words, it will not meet the security and performance needs of LTE-5G, meaning that a new generation of safer and higher performance encryption method is indeed required. Therefore, in this paper, we present a new generation of encryption methods, namely "3D Encryption with Shifting Mapping Substitution Mechanism" (3DESMS), to effectively enhance the safety for LTE-5G and improve its performance. The 3DESMS effectively integrates two encryption schemes, i.e., three-dimensional encryption method (3D encryption for short) and Shifting Mapping Substitution Mechanism (SMSM), where the 3D Encryption uses three fundamental operators, i.e., Exclusive-OR operator (⊕), Binary- Addition operator (+2) and Rotate-Equivalence operator (Θ), to encrypt/decrypt operands. The SMSM is a mapping substitution with constant shifting displacement for data encryption. Our theoretical analyses show that the 3DESMS has achieved the practical secure in a wireless communication environment, and according to the results of our implementation, the performance ranking of the 3DESMS, SeFEM and AES is 3DESMS > SeFEM > AES, indicating that the 3DESMS is more suitable for LTE-5G wireless environments than AES.

The theory of self-regulation, which includes cognitive, motivational, and behavioral dimensions, is useful for understanding the relationships between motivations, learning strategies, and the learner in the context of learning. The critical attributes, such as planning, metacognitive monitoring, and self-reflection, provide valuable information to clarify why some students perform better than others. In order to extract significant attributes for successful learners, the supervised learning method is applied to 131 students of Tunghai University in Taiwan. The mining results indicate that successful students normally spend time on the unclear concepts and put more emphasis on difficult learning materials during the course of learning. The findings provide further information for understanding how students deploy their self-regulatory behaviors in the learning contexts.

MapReduce is a popular distributed programming framework for large-scale data processing. To prevent MapReduce jobs from being interrupted by node failures that occur frequently in a MapReduce cluster consisting of a set of commodity machines/nodes, the most well-known MapReduce implementation, i.e. Hadoop, adopts a task re-execution policy (TR policy). When a map/reduce task of a job crashes, the TR policy assigns another node to reperform the task. However, the impact of the TR policy on MapReduce jobs in terms of reliability, job turnaround time (JTT) and energy consumption are not clear, particularly when jobs have different features, e.g. different filtering percentages, different input-data sizes, and different numbers of reduce tasks. In this paper, we formally analyze the job completion reliability (JCR) of a job based on Poisson distributions, and then derive the expected JTT and job energy consumption (JEC) based on the universal generation function. Extensive analyses are further conducted to explore the impact of the TR policy on JCR, JTT and JEC of jobs with different features. The results show that employing the TR policy can dramatically improve JCR for a large MapReduce job. Moreover, if the JCR of a job is highly improved by the TR policy, the expected JTT and JEC will not be significantly prolonged and increased, respectively.

In this paper, we propose an improved IP-based handover scheme, named the Heterogeneous Network Handover by using 3GPP ANDSF (HNH3A for short) for S-PMIPv6 where S-PMIPv6 is a MIPv6 family protocol developed in one of our previous studies, and ANDSF (standing for Access Network Discovery and Selection Function) is an entity within an evolved packet core (EPC) of the system architecture evolution (SAE) for 3GPP compliant mobile networks. The purpose of using the ANDSF is to assist user equipment (UE) to discover non-3GPP access networks, such as WiFi or WIMAX. The HNH3A can help the handover among mobile WiMAX (i.e., Worldwide Interoperability for Microwave Access), 3GPP (i.e., 3rd Generation Partnership Project) family (including 3GPP systems, 3G LTE and 4G LTE-A) and WiFi based on existing handover techniques among the three heterogeneous networks. The analytical results demonstrate that the HNH3A can effectively mitigate handover delays and handover signaling costs.

In this paper we propose a novel co-allocation scheme, called a Reliable Multicast Brute-Force co-allocation scheme (ReMBF for short), which employs a reliable multicast (RM for short) technique with the Brute-Force (BF for short) scheme to accelerate data retrieval and delivery, and reliably transmit data to its users for data grids. Several types of data access patterns, including Zipf-like, geometric, and uniform distributions, are utilized to model user access behaviors and evaluate the performance of ReMBF. The simulation results demonstrate that ReMBF can efficiently deliver a bulk of data in a shorter time period compared with two state-of-the-art schemes.

Recently, MapReduce has been widely employed by many companies/organizations to tackle data-intensive problems over a large-scale MapReduce cluster. To solve machine/node failure which is inevitable in a MapReduce cluster, MapReduce employs several policies, such as input-data replication and intermediate-data replication policies. To speed up job execution, MapReduce allows reduce tasks to early fetch their required intermediate data. However, the impact of these policy combinations on the job completion reliability (JCR for short) and job energy consumption (JEC for short) of a MapReduce cluster was not clear, where JCR is the reliability with which a MapReduce job can be completed by the cluster, whereas JEC is the energy consumed by the cluster to complete the job. Therefore, in this study, we analyze the JCR and JEC of a MapReduce cluster on four policy combinations (POCs for short) derived from two typical intermediate-data replication policies and two typical reduce-task assignment policies. The four POCs are further compared in extensive scenarios, which not only consider jobs at different scales with various parameters, but also give a MapReduce cluster two extreme parallel execution capabilities and diverse bandwidths. The analytical results enable MapReduce managers to comprehend how these POCs impact the JCR and JEC of a cluster and then select an appropriate POC based on the characteristics of their own MapReduce jobs and clusters.

In recent years, many social network systems like, YouTube, Facebook, Twitter, etc. have been a part of our everyday life. Among these systems, YouTube which plays video programs of different interesting themes for users has been one of the most attractive ones. Basically, when the space of Memcached in YouTube is full, the Least Recently Used algorithm (LRU for short) is employed to evict a least recently watched video. However, the LRU, due to its own property, may cause more miss counts of Memcached for YouTube, consequently increasing the network bandwidth and energy consumptions. To solve these problems, in this paper we proposed two cache replacement algorithms, the Pareto Least Recently Used algorithm (PLRU for short) and Pareto Least Frequently Used algorithm (PLFU for short), in which videos are classified into different popularity categories, and those videos in the top 10% and 20% of the top two popular categories of YouTube, based on Pareto principle, are then chosen to serve users' requests without removing them from Memcached. The simulation results show that the PLFU algorithm can significantly reduce miss counts of Memcached compared with those when the LRU and Least Frequently Used algorithm (LFU for short) are employed, thus achieving great performance improvements for the top two popular categories of videos. Also, when a large space of Memcached is available, the miss counts of the PLRU are higher than those of the LRU. But when comparing them in a small available Memcached space, the miss counts of the LRU on the contrary is higher than those of the PLRU.

MapReduce has been a worldwide accepted framework for solving data-intensive applications. To prevent MapReduce jobs from being interrupted by node failures which occur frequently in a large-scale MapReduce cluster, current MapReduce implementations, e.g., Hadoop, employ a task re-execution policy (TR policy for short) for MapReduce jobs, i.e., when a map/reduce task of a job fails due to node failure, this policy reperforms the task on another node. However, the impact of the TR policy on job completion reliability and job completion time have not been studied from a theoretical viewpoint, especially when the job is given different characteristics, e.g., different input data sizes, different numbers of reduce tasks, and different intermediate data sizes. In this study, we derive the job completion reliability (JCR for short) of a MapReduce job based on Poisson distributions and analyze the expected job completion time (JCT for short) based on the universal generation function. We use nine settings of task re-execution factor (TR factor for short) to explore the impact of the TR policy on the JCR and JCT of jobs. The results show that the TR policy can effectively improve JCR without significantly prolonging JCT. But there is no single TR factor with which all jobs can achieve a high JCR.

Today the quick development of computers and networks not only enriches our everyday life, but also provides us with a convenient living environment. However, hackers are everywhere in the world. Hence, our sensitive data need to be securely protected. Currently, AES and DES as the most widely used block ciphering mechanisms are popularly employed to encrypt important data. But DES was publicly cracked several years ago. On the other hand, due to the fast computer processing speed and processing power, currently no one dare to say that AES is still secure. It may be cracked someday. Therefore, in this paper, we propose a secure encryption method, called the Random Wrapping Encryption Method (RWEM for short), which is developed by employing information of current time as a part of the parameters to wrap up ciphertext in its encryption process. If a file is encrypted at different time points, the content and the length of the wrapped ciphertext file will not be the same. Thus, it is hard for hackers to analyze and crack the ciphertext. We also analyze the security of the DES, AES and RWEM, and explain why the RWEM can effectively protect some specific attacks, and why it is more secure than the DES and AES.

The improvement of sanitary conditions and changes of life style making people's health awareness began to rise. The progress of medical and the promotion of medicine and health, the concept of preventive healthcare is widespread. People begin to concern about the quality of the environment where they stayed. The environmental comfort index include temperature, related humidity, illumination, noise and carbon dioxide. Among these, we monitor and record the values of temperature, related humidity and carbon dioxide. In this paper, we built an environment quality monitoring system, which can adjust the air quality in door and monitor the concentration of harmful gases like formaldehyde, volatile organic compounds and carbon monoxide. If the environment comfort value is not up to standard, the related equipment will be turned on. The system will alert if the content of harmful gases exceed the standard. We hope that based on these real-time data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.

Along with the improvement of sanitary conditions and changes of life style, people begin to pay attention to the modern concept of health promotion and preventive medicine. Therefore, we built an intelligent environment monitoring feedback system to collect data of physical conditions of employees and air conditions of the working environment, displayed the collected data with a real time interface, and sent out warming messages to prevent accidents. We hope that based on these real-time data, the proposed system can help people make right and timely decisions, and act on time to maintain a beneficial environment in the monitored area.

Abstract Grid computing employs heterogeneous resources which may be installed on different platforms, hardware/software, computer architectures, and perhaps using different computer languages to solve large‐scale computational problems. As many more Grids are being developed worldwide, the number of multi‐institutional collaborations is growing rapidly. However, to realize Grid computing's full potential, it is expected that Grid participants must be able to share one another's resources. This paper presents a resource broker that employs the multi‐site resource allocation (MSRA) strategy and the dynamic domain‐based network information model that we propose to allocate Grid resources to submitted jobs, where the Grid resources may be dispersed at different sites, and owned and governed by different organizations or institutes. The jobs and resources may also belong to different clusters/sites. Resource statuses collected by the Ganglia, and network bandwidths gathered by the Network Weather Service, are both considered in the proposed scheduling approach. A dynamic domain‐based model for network information measurement is also proposed to choose the most appropriate resources that meet the jobs' execution requirements. Experimental results show that MSRA outperformed the other tested strategies. Copyright © 2010 John Wiley & Sons, Ltd.

Recently, wireless communication has been popularly used in our everyday life. However, its security is a crucial challenge since wireless messages are broadcasted in the air through which hackers can intercept the massages and realize what the contents of the messages are. That is why security has been one of the most important issues in wireless communication studies. Encrypting messages to ciphertexts is one of the methods to solve this problem. But, it also causes another problem, i.e., how to safely and confidentially encrypt and deliver security keys so that hackers have to spend a very long time before they can decrypt the ciphertexts. In this paper, we propose a novel Diffie-Hellman-based Public Key Distribution System (DH-PKDS for short) which adopts a stream cipher technique to encode plaintext with a pseudo random number sequence (PRNS for short). We have verified the proposed method, and experimental results show that the proposed system is truly suitable in wireless environments.

In this paper, we propose an authentication key management approach, called Diffie-Hellman-PKDS-based authentication method (DiHam for short) to improve current security level of facility authentication between IEEE802.16e's BS and SS by using a secret door asymmetric one-way function, PKDS. With the Initialization Vector transmitted between SS and BS, messages including those conveying user data and authentication parameters, such as the AKs and TEKs, can then be more safely delivered.

In this article, we proposed an intrusion prevention system, named cumulative-sum-based intrusion prevention system (CSIPS) which detects malicious behaviors, attacks and distributed attacks launched to remote clients and local hosts based on the cumulative sum (CUSUM) algorithm. Experimental results show that CSIPSs in a united defense environment can carry out a higher security level for the environment.

In this paper, we propose an agent_based distributed intrusion detection architecture, which detects DoS/DDoS attacks by comparing source IP addresses' normal and current connection frequencies. First, we collect source IPs' packet statistics to obtain their normal packet distribution. When current statistics suddenly increase, very often it is an attack. Experimental results show that this approach can effectively detect DoS/DDoS attacks.

In this paper, we propose a resource broker, which providing a friendly interface for accessing available and appropriate resources via user credentials, is developed on a platform constructed by employing the Globus toolkit. This broker not only deploys a domain-based network information model and its dynamic version to measure network status by invoking Network Weather Service (NWS) on grid computing environments, but also uses the Ganglia and NWS tools to monitor resource status and network-related information. Based on the actual value of network bandwidth and preserving the advantages of our previous model, a network bandwidth-award job scheduling algorithm for grid resource brokerage is then developed for communication-intensive job execution. Furthermore, the network information and resource status collected are all up-to-date so that the resource broker can effectively match appropriate grid resources and users’ requests to improve execution.

Recently, Grid computing infrastructures have been developed, but most of them connect nodes with hardwired links. Seldom adopt wireless technology and mobile devices. However, if we can integrate a Grid system with wireless network devices, techniques and resources to form a wireless Grid infrastructure to provide different kinds of terminal devices, Grid applications will soon be popularly and widely deployed in different domains to serve many more people in the world. In this article, we propose a service platform, named Wireless Grid-based mobile agent system (WGMAS) which integrates mobile agent techniques and wireless environment with a Grid system, to provide a wireless accessible mobile agent system to users.

In recent years, distributed denial-of-service (DDoS) and denial-of-service (DoS) are the most dreadful network threats. Single-node IDS often suffers from losing its detection effectiveness and capability when processing enormous network traffic. To solve the drawbacks, we propose grid-based IDS, called grid intrusion detection system (GIDS), which uses grid computing resources to detect intrusion packets. For balancing detection load, score subtraction approach (SSA) and score addition approach (SAA) are deployed. Furthermore, to effectively detect intrusions, a two-phase packet detection process is proposed. The first phase detects logical and momentary attacks. Chronic attacks are detected in the second phase. Experiments are also performed and the results show that GIDS is truly an outstanding system in detecting attacks.

Distributed denial-of-service (DDoS) and denial-of-service (DoS) are the most dreadful network threats in recent years. In this paper, we propose a grid-based IDS, called performance-based grid intrusion detection system (PGIDS), which exploits grid's abundant computing resources to detect enormous intrusion packets and improve the drawbacks of traditional IDSs which suffer from losing their detection effectiveness and capability when processing massive network traffic. For balancing detection load and accelerating the performance of allocating detection node (DN), we use exponential average to predict network traffic and then assign the collected actual traffic to the most suitable DN. In addition, score subtraction algorithm (SSA) and score addition algorithm (SAA) are deployed to update and reflect the current performance of a DN. PGIDS detects not only DoS/DDoS attacks but also logical attacks. Experimental results show that PGIDS is truly an outstanding system in detecting attacks.

In this article we propose a method that can compress a small-world-like massive digraph (directed graph) into at most half size of the original representation represented by using adjacency list. This method also provides a fast decompression algorithm that works as quickly as adjacency list does. In this paper we deal with the problem of finding a compact representation of a graph from which the vertices adjacent to any specified vertex can be easily determined.

The authors describe an effective rule-base inspection method that checks rule-base consistency and completeness by using an algorithmic approach. A knowledge base in this method is represented by a finite state diagram called a context transition diagram. Through this diagram, some errors and anomalies of the rule base can be clearly illustrated and identified. The characteristics of states representing such errors and anomalies in the state diagram are investigated, and the algorithms for effectively identifying those defects are introduced.>