Myung-Mook Han

The study of selective encryption of motion vector based on the S-Box for the security improvement in the process of video

The Selective Encryption method encrypts the important and requisite parts of data. Since the method does not encrypt the whole of data, the amount of computation is small,which makes it faster and the resources can be used efficiently. The existing selective algorithms have vulnerabilities to the plain text attack and the image restoration attack using the motion vector. They are also vulnerable to the attack in storing and transmitting of the random table data using in encrypt and scramble. In this paper, we propose the selective encryption algorithm of motion vector based on S-Box to remove the vulnerabilities of the existing selective algorithms. The motion vectors generated by the end of motion estimation function of video encoding/decoding xored with S-Box table, are replaced to certain location by using mapping table. The S-Box and mapping table are generated by the secret key through the Rivest Cipher 4 (RC4) encryption algorithm. The proposed algorithm enhances the resistance against attacks through the reinforcement of video security, and thus, reduces the vulnerabilities of the existing algorithms such as I-Frame selective encryption and MVEA. Even though the level of security of the proposed algorithm is higher than the bit scrambling algorithms, it has much better security and higher processing rate than others selective algorithms.

Security License Distribution Mechanism for Rights Delegation Models of Group Distribution between Consumers

Modern DRM system not only emphasizes the protection of copyrights, but also aims to support specific business use models for the digital contents. Group distribution is a kind of newer business distribution model that would allow to purchase the digital contents by an group, such as a family or an organization etc. It makes convenient for consumer, but how to use the rights of group flexibly and securely has not been solved yet. In this paper, we propose two kinds of rights delegation models, m2m and m2c for group distribution and also give a formal definition for these models. We propose authenticate method for consumer and personalize content encryption key to detect illegal distribution. Finally, we describe security license distribution mechanism for these models.

The Model of Network Packet Analysis based on Big Data

Due to the development of IT technology and the information age, a dependency of the network over the most of our lives have grown to a greater extent. Although it provides us to get various useful information and service, it also has negative effectiveness that can provide network intruder with vulnerable roots. In other words, we need to urgently cope with theses serious security problem causing service disableness or system connected to network obstacle with exploiting various packet information. Many experts in a field of security are making an effort to develop the various security solutions to respond against these threats, but existing solutions have a lot of problems such as lack of storage capacity and performance degradation along with the massive increase of packet data volume. Therefore we propose the packet analysis model to apply issuing Big Data technology in the field of security. That is, we used NoSQL which is technology of massive data storage to collect the packet data growing massive and implemented the packet analysis model based on K-means clustering using MapReudce which is distributed programming framework, and then we have shown its high performance by experimenting.

The Efficient Method of Parallel Genetic Algorithm using MapReduce of Big Data

Big Data is data of big size which is not processed, collected, stored, searched, analyzed by the existing database management system. The parallel genetic algorithm using the Hadoop for BigData technology is easily realized by implementing GA(Genetic Algorithm) using MapReduce in the Hadoop Distribution System. The previous study that the genetic algorithm using MapReduce is proposed suitable transforming for the GA by MapReduce. However, they did not show good performance because of frequently occurring data input and output. In this paper, we proposed the MRPGA(MapReduce Parallel Genetic Algorithm) using improvement Map and Reduce process and the parallel processing characteristic of MapReduce. The optimal solution can be found by using the topology, migration of parallel genetic algorithm and local search algorithm. The convergence speed of the proposal method is 1.5 times faster than that of the existing MapReduce SGA, and is the optimal solution can be found quickly by the number of sub-generation iteration. In addition, the MRPGA is able to improve the processing and analysis performance of Big Data technology.

The Adaptive SPAM Mail Detection System using Clustering based on Text Mining

Spam mail is one of the most general mail dysfunctions, which may cause psychological damage to internet users. As internet usage increases, the amount of spam mail has also gradually increased. Indiscriminate sending, in particular, occurs when spam mail is sent using smart phones or tablets connected to wireless networks. Spam mail consists of approximately 68% of mail traffic; however, it is believed that the true percentage of spam mail is at a much more severe level. In order to analyze and detect spammail, we introduce a technique based on spam mail characteristics and text mining; in particular, spam mail is detected by extracting the linguistic analysis and language processing. Existing spam mail is analyzed, and hidden spam signatures are extracted using text clustering. Our proposed method utilizes a text mining system to improve the detection and error detection rates for existing spam mail and to respond to new spam mail types.

Improved WTA problem solving method using a parallel genetic algorithm which applied the RMI initialization method

The problem of Weapon Target Allocation (WTA) is to find an optimum solution, the type of vector that our weapons assign to targets, to minimize the damage of our assets from the target of an enemy offending us. we proposed the novel parallel genetic algorithm for solved to the WTA problem. The proposed. As the first step, our proposed algorithm is to expand the problem search space through the Random Mutation Inherit (RMI) population initialization method thereby improving convergence performance. We proposed an algorithm which obtains the WTA solution quickly and solves the WTA problem efficiently.

A structure-based approach for multimedia information filtering

While multimedia documents are sequentially presented to users, an information filtering (IF) system is useful to achieve a good retrieval performance in terms of both quality and efficiency. Conventional approaches for designing an IF system are based on the users evaluation on information relevance degree (IRD), but ignore other attributes in system design such as relative importance of the data in a collection of multimedia documents. In this paper, we aim at developing a framework of designing structure-based multimedia IF systems, which incorporates the characteristics of the importance and relevance of multimedia documents. A method of calculating the values of relative importance degree of multimedia documents is proposed. Furthermore, these values are combined into the IRD of multimedia documents to improve the representation of user profiles. An illustrative example is given to demonstrate the proposed techniques.

A Study of population Initialization Method to improve a Genetic Algorithm on the Weapon Target Allocation problem

The Weapon Target Allocation(WTA) problem is the NP-Complete problem. The WTA problem is that the threatful air targets are assigned by weapon of allies for killing the targets. A good solution of NP-complete problem is heuristic algorithms. Genetic algorithms are commonly used heuristic for global optimization, and it is good solution on the diverse problem domain. But there has been very little research done on the generation of their initial population. The initialization of population is one of the GA step, and it decide to initial value of individuals. In this paper, we propose to the population initialization method to improve a Genetic Algorithm. When it initializes population, the proposed algorithm reflects the characteristics of the WTA problem domain, and inherits the dominant gene. In addition, the search space widely spread in the problem space to find efficiently the good quality solution. In this paper, the proposed algorithm to verify performance examine that an analysis of various properties and the experimental results by analyzing the performance compare to other algorithms. The proposed algorithm compared to the other initialization methods and a general genetic algorithm. As a result, the proposed algorithm showed better performance in WTA problem than the other algorithms. In particular, the proposed algorithm is a good way to apply to the variety of situation WTA problem domain, because the proposed algorithm can be applied flexibly to WTA problem by the adjustment of RMI.

A dynamic neuro fuzzy knowledge based system in threat evaluation

With development of information technology, information system has been advanced in a battlefield situation, and it becomes key factors to obtain information about the enemy aircraft and analyze the situation in battlefield. Threat evaluation which is the key factor of analysis of a battlefield situation is a technology that evaluates threat values on the situation in accordance with air intelligence gotten through radar and provides information about weapon assignment. This stage requires the most accurate information than other stage in a battlefield situation. Most of data of threat evaluation is calculated by sensed sensor values and transmitted, but presentation of incorrect links of sensor data or data omission that could happen on the existing technique could cause confusion over decision making in a battlefield situation. Thus, the links must be defined accurately through knowledge of experts on various sensor data domain, and reliable threat consequences must be provided through adaptation and learning about unpredictable battlefields due to data omission. On this paper, a fusion system, a dynamic neuro fuzzy knowledge based inference system, which is favorable to adaptation and learning and presentation of expertise, is suggested and applied to threat evaluation.

Flexible License Management Mechanism for Group Distribution

With the requirement of consumer increased, many business use models have been proposed. Group Distribution as a new kind content distribution model has attracted increasing attention. It allows consumer to purchase digital content by a group and makes flexible use of rights among members possibly. However, how to use the rights belonged to group flexibly and securely has not been solved well. In this paper, we divide license into group license and member license and propose flexible license management mechanism for Group Distribution. Group license is requested by Group Manager and managed with DB at License Server. GroupID is used as the group licenses ID. Except for Identity Certificate, we construct Group Attribute Certificate based on the structure of Attribute Certificate to identify which group member belongs to. Based on group attribute (GroupID) included in Group Attribute Certificate, member license can be decided whether to create or not.