2023, Vol.9, No.4

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

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

As cyber attacks become more intelligent and advanced, cyber attacks targeting heterogeneous systems such as Internet of Things (IoT) devices are increasing. There is a need for a technique to share detailed threat information about the incident attack. In the event of an infringement incident, a technique that can express digital forensic artifacts collected from heterogeneous IoT devices as indicators of compromise (IoC) and share them must be established. In particular, when malicious code is executed targeting various IoT devices, an efficient IoC generation method to express cyber threat information and share it among CTI systems must be presented. Therefore, in this study, the existing IoC creation method and expression method were analyzed. A classification system for generating IoC for malware and an efficient and standardized expression method were presented. Based on the proposed IoC expression and standardization method, it is expected that it will be able to actively respond to intelligent attacks when establishing an accident management framework

Recently, resource optimization research has been actively conducted in enterprises and data centers to manage the rapid growth of big data. In particular, thin provisioning, which allocates a large number of resources compared to fixedly allocated storage resources, has the effect of reducing initial costs, but as the number of resources actually used increases, the cost effectiveness decreases and the management cost for allocating resources increases. In this paper, we propose a technique that divides the physical blocks of flash memory into single-bit cells and multi-bit cells, formats them with a hybrid technique, and manages them by dividing frequently used hot data and infrequently used cold data. The proposed technique has the advantage that the physical and allocated resources are the same, such as thick provisioning, and can be used without additional cost increase, and the underutilized resources can be managed in multi-bit cell blocks, such as thin provisioning, which can allocate more resources than typical storage devices. Finally, we estimated the resource optimization effectiveness of the proposed technique through experiments based on simulations.

In this paper, we conducted research on a spatial crowdedness measurement system using the Internet of Things(IoT) and Amazon Web Services(AWS). Current spatial congestion measurement systems require expensive servers and entail significant investment in algorithm development and updates to enhance spatial recognition performance. The proposed system can be implemented at low cost by capturing the screen of a web camera(or CCTV) through OpenCV on a small/low-cost single board computer, Raspberry Pi, and transmitting and processing the information to AWS. Within AWS, the received spatial image information is stored in Amazon S3(Amazon Simple Storage Service). Subsequently, Amazon Lambda transfers the images to Amazon Rekognition for congestion analysis based on the images. The Amazon Rekognition service offers the capability to utilize artificial intelligence(AI) technology for image processing at a low cost of 0.001 dollars per image. Through human object recognition, it enables congestion analysis. The analyzed congestion level is stored in DB(Database), and the result is displayed on the screen. The proposed system is expected to be used for checking crowdedness at low cost in public place