Program similarity analysis consists of substantial similarity and access. Substantial similarity is a judgment of how similarly the program source code is quantitatively. Access determines the degree of similarity by analyzing comments in the program or other contextual evidence. In the case of manuals, it may be the subject of legitimacy analysis. Manuals can be divided into three types as follows. First, a master manual is a document created during the development stage of a product. It is a user manual that contains all the functionality of the product and its derivatives. Second, the customer manual is a manual that is open only to the primary customer and orderer. Third, the user manual is a document that is applied to the final OEM production stage and is open to the end purchaser. In this paper, we compare the master manual seized from the suspect and the master manual provided by the suspect on the Internet. It then determines how similar this master manual is and includes the victim company's original and property values.
More than 25% of software copyright evaluations commissioned to the Korea Copyright Commission are software completeness-defective evaluations. Most of the existing software completeness-defective evaluation cases were made to ensure that the requirements contained in the contract or customer's desired requirements were implemented and able to run, limited to the functionality of the software.
This paper proposes a more systematic and reasonable maturity-correction technique to meet software completeness definitions. The ISO / IEC 9241.10 standard is a design standard for improving software quality. The ISO / IEC 9241.10 standard specifies seven items that must be complied with and requires functional integrity for work and operational integrity for work efficiency.
The software completeness-defective methodology presented in this paper complements the existing function’s implementation-functioning methodology with completeness evaluation on the quality of software.
Use without permission of font files is a social problem. In the meantime, our court recognized font files as computer programs. Is the font file a computer program? This recognition arises from the inability to distinguish between computer programs and data. Expert recognition, on the other hand, does not recognize font files as computer programs. In this regard, there was a case in 2014 that INI files were not computer programs, but only data files. So, the attitude of the Supreme Court in 2001 only makes it difficult to distinguish between computer programs and data. The Supreme Court's decision needs to be changed. In addition, a new legal system should be in place to protect font files.
In copyright dispute on software, appraisal procedure is needed, which is regarded as an implementation of the right to fast trial. Considering that, in practice, there has been a long discussion of attempting to operate the software appraisal as a summary procedure. This was done usually by reducing the resources devoted to the summary procedure, however, this way inevitably makes difference in a result obtained through the procedure. Therefore, this research reviewed the previous operation of summary procedure in software appraisal, focusing on intrinsic nature of the summary procedure and role of software appraisal in the trial process.
In this study, we reviewed the definition of completeness and payment in SW appraisal along to being complicated requirements of the appraisal, and we also presented their meaning and computation method.
The completeness in SW appraisals means the degree of functional completion of the final product, and the payment refers cost spent to develop the product. Therefore, the completeness is evaluated based on the functions or interfaces of the final product, while the payment is calculated on the outputs or expenses of development steps. Recently, SW appraisal is complicated and the requirements for completeness and payment are changing. In this paper, we review the meanings and objectives of completeness and payment and present the evaluation methods for those.
With the development of IT technology, computer-related crimes are rapidly increasing, and in recent years, the damage to ransomware infections is increasing rapidly at home and abroad. Conventional security solutions are not sufficient to prevent ransomware infections, and to prevent threats such as malware and ransomware that are evolving, a combination of deep learning technologies is needed to detect abnormal behavior and abnormal symptoms. In this paper, a method is proposed to detect user abnormal behavior using CNN-LSTM model and various deep learning models. Among the proposed models, CNN-LSTM model detects user abnormal behavior with 99% accuracy.
On increasing illegal software copyright, the need for similarity analysis is now rising. The reliability of object material are becoming important when it’s moving from developer to evaluation experts. Object material as a comparison data, is the important data to the evaluation expert which is delivered from agencies such as courts and police stations. The object material is submitted at first to the Copyright Commission and then delivered to the evaluation expert with safe. However, if the similarity result is not satisfied to the both side, they will claim to the reliability of the object material such as source code modification or revision etc. Software objects is produced in a file format and are recognized as being able to be modified. Therefore, the reliability to the object material is studied in various ways, and a forensic is proposed as one method. This study showed the suggestion to keep reliability of the object material through the actual evaluation cases.
Since Jeju is the absolute weight of agriculture and tourism, the analysis of precipitation is more important than other regions. Currently, some numerical models are used for analysis of precipitation of Jeju Island using observation data from meteorological satellites. However, since precipitation changes are more diverse than other regions, it is difficult to obtain satisfactory results using the existing numerical models. In this paper, we propose a Jeju precipitation pattern analysis method using the texture analysis method based on Convolution Neural Network (CNN). The proposed method converts the water vapor image and the temperature information of the area of ​​Jeju Island from the weather satellite into texture images. Then converted images are fed into the CNN to analyse the precipitation patterns of Jeju Island. We implement the proposed method and show the effectiveness of the proposed method through experiments.
The recent explosion of data traffic, including cloud services, coupled with the Internet penetration has led to a surge in the need for ultra-fast optical networks that can efficiently connect the data center's reconfiguable resources. In this paper, the algorithms for controlling switching cell operation in the optical switch connection structure are proposed, and the resulting performance is compared and analyzed through simulation. Performance analysis results showed that the algorithm proposed in this paper has improved the probability of successful multi-connection setup by about 3 to 7% compared to the existing algorithm.
Voice Recognition has recently been improved with AI(Artificial Intelligence) and has greatly improved the false recognition rate and provides an effective and efficient Human Machine Interface (HMI). This trend has also been applied in the defense industry, particularly in the aviation, F-35. However, for the quality evaluation of Voice Recognition, the defense industry, especially the aircraft, requires measurable quantitative models.
In this paper, the quantitative evaluation model is proposed for applying Voice Recognition to aircraft.
For the proposal, the evaluation items are identified from the Voice Recognition technology and ISO/IEC 25000(SQuaRE) quality attributes. Using these two perspectives, the quantitative evaluation model is proposed under aircraft operation condition and confirms the evaluation results.
In an open, heterogeneous environment based on machine-to-machine (M2M) interactions, service selection is a critical issue and the concept of social trust can be applied to service selection so that IoT devices can make the best choice for interaction. In this paper, we propose a method for evaluating the trust level of the service and for estimating the QoS of the composite service using a profile created based on social trust relationship in IoT environment. As the service selection is made through quantitative evaluation, it is expected that the result of a more reliable service combination can be obtained.
In this paper, we propose a simple hardware reporting method for errors in soft-RAID systems of Linux OS. Compared with other reporting methods, the proposed method displays error status intuitively without any additional access process such as log-in process or home-page access. In particular, the server actively displays the error status, so the administrator can take immediate action. In order to confirm the effectiveness of the proposed method, the experimental circuit was constructed and the experimental results showed that the error was actively displayed when an error occurred in the storage device. As such, a soft-RAID system can perform almost the same function as a hardware RAID system, thereby ensuring server data reliability at low cost.
Lasers for optical broadband communication systems should have excellent frequency selectivity and modal stability. DFB(Distributed Feedback) lasers have low lasing frequency shift during high speed current modulation.
In this paper, I have developed a simulation software and analysed threshold gain and lasing frequency of a lasing mode in longitudinal direction of an 1.55um DFB laser with two mirrors and without anti-reflection coatings, that have both an index- and gain-gratings. The grating phase on a left mirror face is fixed as π/2 and the grating phase on a right mirror face is varied. As the phases of the index and gain gratings on the right mirror facet are π and 0, should be in the range of 2∼6 in order to enhance the frequency stability. In order to reduce the threshold current of a lasing mode, should be greater than 8, regardless of the grating phases on the mirror faces.
The existing smart grid device authentication system is concentrated on DCU, meter reading FEP and MDMS, and the authentication system for smart meters is not established. Although some cryptographic chips have been developed at present, it is difficult to complete the PKI authentication scheme because it is at the low level of simple encryption. Unlike existing power grids, smart grids are based on open two-way communication, increasing the risk of accidents as information security vulnerabilities increase.
However, PKI is difficult to apply to smart meters, and there is a possibility of accidents such as system shutdown by sending manipulated packets and sending false information to the operating system. Issuing an existing PKI certificate to smart meters with high hardware constraints makes authentication and certificate renewal difficult, so an ultra-lightweight password authentication protocol that can operate even on the poor performance of smart meters (such as non-IP networks, processors, memory, and storage space) was designed and implemented. As a result of the experiment, lightweight cryptographic authentication protocol was able to be executed quickly in the Cortex-M3 environment, and it is expected that it will help to prepare a more secure authentication system in the smart grid industry.
Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses.
Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.
In the IoT environment, there is a huge amount of heterogeneous devices with limited capacity.
Existing trust evaluation methods are not adequate to accommodate this requirement due to the limited storage space and computational resources. In addition, since IoT devices are mainly human operated devices, the trust evaluation should reflect the social relations among device owners. There is also a need for a mechanism that reflects the tendency of the trustor and environmental factors. In this paper, we propose an adaptable trust evaluation method for SOA-based IoT system to deal with these issues. The proposed model is designed to minimize the confidence bias and to dynamically respond to environmental changes by combining direct evaluation and indirect evaluation. It is expected that it will be possible to secure trust through quantitative evaluation by providing feedback based on social relationships.