2023, Vol.9, No.1

Journal of Internet of Things and Convergence 2023 KCI Impact Factor : 0.89

pISSN : 2466-0078
https://journal.kci.go.kr/kiots

Cases in which personal terminals or servers are infected by ransomware are rapidly increasing. Ransomware uses a self-developed encryption module or combines existing symmetric key/public key encryption modules to illegally encrypt files stored in the victim system using a key known only to the attacker. Therefore, in order to decrypt it, it is necessary to know the value of the key used, and since the process of finding the decryption key takes a lot of time, financial costs are eventually paid. At this time, most of the ransomware malware is included in a hidden form in binary files, so when the program is executed, the user is infected with the malicious code without even knowing it. Therefore, in order to respond to ransomware attacks in the form of binary files, it is necessary to identify the encryption module used. Therefore, in this study, we developed a mechanism that can detect and identify by reverse analyzing the encryption module applied to the malicious code hidden in the binary file.

Deep learning with large amount of computations is difficult to implement on micro-sized IoT devices or moblie devices. Recently, lightweight deep learning technologies have been introduced to make sure that deep learning can be implemented even on small devices by reducing the amount of computation of the model. Quantization is one of lightweight techniques that can be efficiently used to reduce the memory and size of the model by expressing parameter values with continuous distribution as discrete values of fixed bits. However, the accuracy of the model is reduced due to discrete value representation in quantization. In this paper, we introduce various quantization techniques to correct the accuracy. We selected APoT and EWGS from existing quantization techniques, and comparatively analyzed the results through experimentations The selected techniques were trained and tested with CIFAR-10 or CIFAR-100 datasets in the ResNet model. We found out problems with them through experimental results analysis and presented directions for future research.

Recently, with the recent rapid development of memory technology, various types of memory are developed and are used to improve processing speed in data management systems. In particular, NAND flash memory is used as a main media for storing data in memory-based storage devices because it has a nonvolatile characteristic that it can maintain data even at the power off state. However, since the recently studied memory-based storage device consists of various types of memory such as MRAM and PRAM as well as NAND flash memory, research on memory management technology is needed to improve data processing performance and efficiency of media in a storage system composed of different types of memories. In this paper, we propose a memory mapping scheme thought technique for efficiently managing data in the storage device composed of various memories for data management. The proposed idea is a method of managing different memories using a single mapping table. This method can unify the address scheme of data and reduce the search cost of data stored in different memories for data tiering.