The establishment of shared cryptography keys is one of the challenging problems in the sensor networks. Key infection (R. Anderson et al., 2004) is a promising model to solve this problem without complex mechanism on the commodity sensor networks. This model, however, does not consider the mobility of sensor, so it can not support dynamic sensor network fields. Therefore, key infection model has to be extended to handle the mobility of sensor, and then an extended key infection model can be used on the dynamic sensor network. In this paper, we propose a scheme to extend the key infection model for supporting dynamic sensor networks and explain how the proposed scheme is operated in detail. Also we prove that the proposed scheme is secure.
In this paper, we propose multi-layer objectionable video classification system using local information and global information simultaneously. We also analyze the additional information of video files through the Internet for use in the objectionable video classification from a statistical point of view. The proposed system consists of 3 analyzers and uses MPEG-7 visual descriptors as features for content-based analyzer. To gain the local information of a video, we extract 200 representative frames from video file by uniform sampling and classify every frame using MPEG-7 visual descriptors and a SVM. To avoid the misclassification by a few frames, results of frame classification is used as the global information. Experiment results show that the proposed system has an excellent performance in classifying a video as the objectionable or as the unobjectionable.
The need for data encryption that protects sensitive data in a database has increased rapidly. However, encrypted data can no longer be efficiently queried because nearly all of the data should be decrypted. Several order-preserving encryption schemes that enable indexes to be built over encrypted data have been suggested to solve this problem. They allow any comparison operation to be directly applied to encrypted data. However, one of the main disadvantages of these schemes is that they expose sensitive data to inference attacks with order information, especially when the data are used together with unencrypted columns in the database. In this study, a new order-preserving encryption scheme that provides secure queries by hiding the order is introduced. Moreover, it provides efficient queries because any user who has the encryption key knows the order. The proposed scheme is designed to be efficient and secure in such an environment. Thus, it is possible to encrypt only sensitive data while leaving other data unencrypted. The encryption is not only robust against order exposure, but also shows high performance for any query over encrypted data. In addition, the proposed scheme provides strong updates without assumptions of the distribution of plaintext. This allows it to be integrated easily with the existing database system.
The establishment of shared cryptography keys is one of the challenging problems in the sensor networks. Key Infection is a promising model to solve this problem on the commodity sensor networks without complex mechanism. This model, however, does not consider the mobility of sensors, so if sensor nodes move out of initial communication range, they cannot rejoin the network since they require new pair-wise key. Therefore, key infection model has been restricted in the static sensor network To be applied on the dynamic sensor networks, therefore, key infection model has to be extended to handle incoming sensor node. In this paper, we propose secure protocol for incoming sensor node in the dynamic sensor networks and verify the proposed protocol formally. Furthermore, we use simulation to investigate the effect of our proposed protocol.
The openness of the Web allows any users to access almost any type of information. However, some information, such as adult content, is not appropriate for all users, notably children. Additionally for adults, some contents included in abnormal pornographic sites can do ordinary people's mental health harm. In this paper, we propose a new criterion and divide contents of Web documents into 4 grades. We use a hierarchical way of filtering texts. At first, we filter off 0-grade texts contain no adult contents using a pattern matching algorithm, and classify 1-grade, 2-grade and 3-grade texts using a machine learning algorithm
This paper presents the image feature extraction method and the image classification technique to select the harmful image flowed from the Internet by grade of image contents such as harmlessness, sex-appealing, harmfulness (nude), serious harmfulness (adult) by the characteristic of the image. In this paper, we suggest skin area detection technique to recognize whether an input image is harmful or not. We also propose the ROI detection algorithm that establishes region of interest to reduce some noise and extracts harmful degree effectively and defines the characteristics in the ROI area inside. And this paper suggests the multiple-SVM training method that creates the image classification model to select as 4 types of class defined above. This paper presents the multiple-SVM classification algorithm that categorizes harmful grade of input data with suggested classification model. We suggest the skin likelihood image made of the shape information of the skin area image and the color information of the skin ratio image specially. And we propose the image feature vector to use in the characteristic category at a course of traininB resizing the skin likelihood image. Finally, this paper presents the performance evaluation of experiment result, and proves the suitability of grading image using image feature classification algorithm.
Abstract — This paper proposes a specialized Web robot to automatically collect objectionable Web contents for use in an objectionable Web content classification system, which creates the URL database of objectionable Web contents. It aims at shortening the update period of the DB, increasing the number of URLs in the DB, and enhancing the accuracy of the information in the DB. Keywords — Web robot, objectionable Web content classification, URL database, URL rating I. I NTRODUCTION NTERNET users are easily exposed to hate literature, pornography, pedophiles, and other inappropriate information. This is becoming serious social issue and parents are much concerned about the access to this kind of information by children. Now, we see many parents installing objectionable Web content filtering software in PC. Objectionable Web content filtering solution can be built on URL database method or dynamic method, or both. Most of the filtering solutions block connection to URLs which are in URL database. This is known to be more effective and efficient. But, the generation and maintenance of the URL database by human is difficult and slow. As the result, we need a system for the generation and maintenance of the URL database by automatically collecting and classifying Web content. The collection of Web content in the system is the role of Web robot. However, it is inappropriate to employ a general-purpose Web robot for objectionable Web content classification, because general-purpose Web crawling quickly loses its way and starts to gather harmless pages, even though it starts from the URLs of porn sites. As the gathering process goes to wrong direction, objectionable Web content classification takes longer and the URL database refreshes much less frequently, which mean it is not likely to filter out new objectionable Web contents. In addition, general-purpose Web robot does not feed any information to the classification module except the
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