Similarity preserving is a key ingredient of cancellable biometric scheme design. The notion ensures the accuracy performance of the biometric systems can be preserved after the cancellable biometric technique is applied. Random Projection is among the most commonly adopted method in cancellable biometric schemes. However, it is reversible subject to certain conditions, which disrupts the template irreversibility criterion. This invites vulnerabilities for random projection-based schemes. In this paper, we propose a novel transform function, namely Absolute Value Equations Transform (AVET), which non-linearly projects feature vectors to another domain. The transformed templates hold two main merits ensuring the user’s privacy, and maintaining the system’s performance simultaneously. First, by relying on the hardness of the Absolute Value Equations problem, we guarantee that AVET satisfies irreversibility. Second, by using Johnson–Lindenstrauss lemma and the inverse triangle inequality, we prove that the proposed approach has the similarity preserving property. Notably, rigorous theoretical proofs and empirical experiments are provided. The efficacy of AVET is comprehensively evaluated on both physiological and behavioral biometrics including face, ear, fingerprint, and gait. With unimodal approach, we achieve competitive performances compared to related algorithms on eight public datasets. Regarding bimodal mode, the AVET surpasses the state-of-the-art technique on all three observed datasets. To the best of our knowledge, this is the first study that attempts to develop a secure transformation to augment the role of Random Projection in the existing cancellable biometric schemes.

With the wide development of network services and the increasing network quality of service (QoS) requirements, dynamic network provisioning, which offers a smart way to segment the network to support particular services, becomes an important problem. Past studies of dynamic network provisioning mostly consider dynamic bandwidth and delay assignment in order to maximize the system throughput and minimize communication overhead. These studies leveraged Software Defined Network (SDN) and Network Function Virtualization (NFV) to create dedicated end-to-end virtual networks which are called network slices. But, these works are lack of consideration of provisioning a dynamic network based on request locations where users would use the network to deploy their services. In this paper, a method for generating a network slice dynamically based on requested locations of users is proposed to support the location-aware dynamic network provisioning. The requested locations are obtained by users through a web interface, then the requested locations are encapsulated and sent to SDN controller which knows the geographical location of SDN switches. Then SDN controller sets up a location-aware network slice. To provide a viability of the proposed approach, we implemented the web interface and the protocol to share the requested locations.

The emerging research of SDN and NFV have been promising to provide flexibility in network provisioning based on service requirement. However, current network provision methods of SDN and NFV do not have a formalized model of describing dynamic requirement of the services. The usage of audio/video services may be the example of the services with dynamic requirement. The orchestrator of SDN and NFV needs to understand the service requirement in order to provision the network dynamically and systemically. Hence, innovative service abstraction is needed to model the service requirements. This paper describes our initial effort to design a service abstraction model for enhancing network provisioning in future network. We consider audio/video services as the target service of our initial service abstraction model and develop the XML based service abstraction model. Moreover, we present a communication mechanism to submit and deploy the XML-based service abstraction model to the orchestrator.

A Software Defined Network (SDN) is a new paradigm in network management that separates control plane and data plane. A control plane has an important role in managing the whole networks. Since SDN introduces control plane as the manager of the network, it also introduces the single point of failure. When SDN controller is unreachable by the network devices, the whole networks will collapse. One of the attack methods that can make SDN controller unreachable is DDoS attack. This paper reports our initial step of our research to develop the method for DDoS attack detection and mitigation for SDN controller. The method considers the time duration of DDoS attack detection and attacks time pattern of DDoS attack to prevent the future attack. In this paper, we present the potential vulnerabilities in SDN controller that can be exploited for DDoS attack and discuss the methods to detect and mitigate DDoS attack.

Advance in Software Defined Network is enabling more possibilities of network infrastructure utilization. For example, underutilized network resource in the production network can be shared to provide research and development network that does not mix with the production one. In our previous work, we already provide users a way to manage their defined network infrastructure through a web interface. In this paper, we presented our mobile application access design for users as a new way for them to manage their defined network infrastructure via their smartphone or tablet. We also update our previous work to reflect this change by modularizing the system and adding a REST API access module to our Virtual Network Management System.

Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security strength on mobile devices. However, the existing approaches are obtrusive since the user is required to perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is understood to be a reliable profile for effective implicit authentication, recent studies have been conducted in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the instability of sensor's orientation which mostly happens in the reality. A flexible solution taking advantages of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of the biometric gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45\% and accuracy rate of 99.14\% in terms of the verification and identification aspects being achieved, respectively.

Recently, OpenFlow and FlowVisor are the most promising architecture components of the software-defined networks (SDNs). The evolution of these components brought the revolution of utilizing network elements for the shared and virtual network infrastructure, however from the aspects of production networks, it still lacks of some key component. For example, policy-based services provisioning for user-defined requirement is an indispensable part. Hence, innovative aggregation management design for virtual network management system framework must be developed and tested. This paper describes an initial design to user-defined network infrastructure in order to make it capable of running policy-based engineering related experiments. We developed the framework regarding the real use cases upon the shared and virtual network in KREONET infrastructure. Moreover, we also presented perflow-user-defined specifications which include max rate limiter and dynamic priority assignment strategy to enhance the validation performance of our system for production networks.

Many achievements have been announced with real time running capability for activity recognition (AR) using mobile accelerometer. However, they also have weak points including low accuracies especially in multiple-subject activity recognition and lacking of evidences about power consumption. In this paper, we contribute a novel method for extracting features on time domain and frequency domain. These different features were then respectively applied to Support Vector Machine (SVM) classifier and Dynamic Time Warping (DTW) method in order to find out the most effective combinations. Our own data and SCUTT-NAA dataset were used in our experiment. Accuracy rates of 95% and 97% in multiple-subject AR were achieved by respectively using SVM and DTW from time domain features (TF). These approaches were then implemented on a mobile phone to measure the power consumptions. SVM using time feature method was found as the most effective method for balancing accuracy and energy consumption.

From secret knowledge like password up to physical traits as biometrics, current smartphone authentication systems are deemed inconvenience and difficult for users. Burdens on remembering password as well as privacy issues on stolen or forged biometrics have raised a futuristic idea of authentication systems. New system is hoped being transparent and with very minimum user involvement denoted as implicit authentication system. One of the ways to implicitly authenticate users is by authenticating them via image or video captured using smartphone camera during a call. During call interaction, we implicitly take ear image using front smartphone camera to recognize and authenticate users without them realizing. In this paper, we present a novel approach to ear recognition which considers both shape and texture information to represent ear image. Firstly, all Local Binary Pattern (LBP) are combined after extracted and concatenated into a single histogram. Second, in order to get geometric features, we use the idea of ear location center that is easily adjusted by smartphone user. Then, we combine previous steps to represent ear image as a descriptor. The recognition is performed using a nearest neighbor classifier computed feature space with Euclidean distance as a similarity/dissimilarity measure. Our proposed approach is very easy and simple thereby its simplicity allows very fast feature extraction. We foresee that this experiment is applicable directly on smartphone.

ABSTRACT The amount of Short Message Service (SMS) spam is increasing. Various solutions to filter SMS spam on mobile phones have been proposed. Most of these use Text Classification techniques that consist of training, filtering, and updating processes. However, they require a computer or a large amount of SMS data in advance to filter SMS spam, especially for the training. This increases hardware maintenance and communication costs. Thus, we propose to filter SMS spam on independent mobile phones using Text Classification techniques. The training, filtering, and updating processes are performed on an independent mobile phone. The mobile phone has storage, memory and CPU limitations compared with a computer. As such, we apply a probabilistic Naïve Bayes classifier using word occurrences for screening because of its simplicity and fast performance. Our experiment on an Android mobile phone shows that it can filter SMS spam with reasonable accuracy, minimum storage consumption, and acceptable processing time without support from a computer or using a large amount of SMS data for training. Thus, we conclude that filtering SMS spam can be performed on independent mobile phones. We can reduce the number of word attributes by almost 50% without reducing accuracy significantly, using our usability‐based approach. Copyright © 2012 John Wiley & Sons, Ltd.

Intrusion detection system (IDS) is one of the basic components of network security. IDS can use signatures or abnormal behaviors to detect attacks. When IDS uses signatures, in order to detect more attacks, we have to implement more attack signatures on IDS. However, in resource-constrained wireless sensor networks, it is not efficient to implement all of attack signatures. And it is hard to maintain attack signature database because it should be updated frequently when new kind of attacks appear. In this paper, we propose a distributed IDS mechanism which reduces the usage of memory and overhead. Analysis of our proposal shows that our proposal requires less memory.

Bandwidth allocation is a fundamental problem in communication networks. With current network moving towards the future Internet model, the problem is further intensified as network traffic demanding far from exceeds network bandwidth capability. Maintaining a certain user satisfaction degree therefore becomes a challenge research topic. In this paper, we deal with the problem by proposing BASMIN, a novel bandwidth allocation scheme that aims to maximize network userpsilas happiness. We also defined a new metric for evaluating network user satisfaction degree: network worth. A three-step evaluation process is then conducted to compare BASMIN efficiency with other three popular bandwidth allocation schemes. Throughout the tests, we experienced BASMIN's advantages over the others; we even found out that one of the most widely used bandwidth allocation schemes, in fact, is not effective at all.

In ubiquitous computing environments like smart home, context awareness is a very important component which aims at providing automatic services. This paper proposes chaining case based reasoning (CCBR) as the reasoning method which solves the vagueness of traditional case based reasoning (CBR) approach "In the certain case with more than one solution, we donpsilat know which solution or activity will be chosen to satisfy user's needs in smart homepsilas context". The context's contents in smart home are described in this paper. Also, we introduce the framework of context awareness based on CCBR, and discuss the case representation, case adaptation, and similarity computation in detail. Our proposed CCBR integrated into the virtual smart home environment acquires knowledge about user actions that are recorded to determine their preferences and then simultaneously activates the devices with predefined settings.

With research effort of Ubiquitous computing development, Wireless Sensor Networks have also been studied and deployed in many places. Recently various works for sharing the existing sensor data have been tried. In this paper we propose a P2P based system for sharing sensor data. It uses a structured P2P - Pastry to provide DHT based routing without server and has been deployed in KOREN. We classify the sensor data into real-time data and past data, and Scribe in Pastry is applied to multicast the real-time data and PAST in Pastry is used to store and provide the past data. Also as Pastry itself does not provide the search function, we propose to use PAST for providing search function using keywords.

Abstract The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two‐phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real‐time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real‐time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real‐time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real‐time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.