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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.

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.

As the firewall is a typical network security equipment, it is usually installed at most of internal/external networks and makes many packet data in/out. So analyzing the its logs stored in it can provide important and fundamental data on the network security research. However, along with development of c ommunications technology, the speed of internet network is improved and then the amount of log data is becoming 'Massive D ata' or 'BigData'. In this trend, there are limits to analyze log data using the traditional database model RDBMS. In this pa per, through our Method of Analyzing Firewall log data using MapReduce based on NoSQL, we have discovered that the introduci ng NoSQL data base model can more effectively analyze the massive log data than the traditional one. We have demonstr ated execellent performance of the NoSQL by comparing the performance of data processing with existing RDBMS. Also the pr oposed method is evaluated by experiments that detect the three attack patterns and shown that it is highly effective.Keywords: NoSQL, Firewall, Log Analysis, MapReduce, BigData 접수일(2013년 3월 29일), 수정일(1차: 2013년 6월 13일, 2차: 2013년 7월 1일), 게재확정일(2013년 7월 1일)* "이 논문은 2013년도 가천대학교 교내연구비 지원에 의한 결과임."(GCU-2013-R193)†주저자, cbm0728@gmail.com‡교신저자, mmhan@gachon.ac.kr (Corresponding author)

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.

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.