Yunsik Son

Hybrid Deep Neural Network based Performance Estimation Method for Efficient Offloading on IoT-Cloud Environments

The IoT-Cloud virtual machine system is a cloud-based execution solution for IoT devices with offloading techniques that delegate tasks requiring high computing power from low-performance IoT devices to a high-performance cloud environment as a service. The IoT devices with the IoT-Cloud virtual machine system can perform complex tasks using the computing power of high-performance cloud. The offloading technique can reduce the execution performance depending on the workload of the IoT devices and the clouds. Therefore, it is necessary to decide offloading execution considering the workload of the IoT devices and the clouds. In this paper, CPU utilization trend, which is one of the workload indices, is predicted through deep learning in order to decide offloading execution considering the workload of the IoT devices and clouds. In this paper, we present four CPU usage models and introduce a technique for predicting server load based on hybrid deep neural network. The predicted CPU utilization trend is indicative of future CPU utilization information and is therefore an indicator for offloading execution decisions. Through experiments, we confirmed that the proposed method estimates the load of the model very similar, and it can apply the offloading adaptively according to the load of the server.

The x86/64 Binary Code to Smart Intermediate Language Translation for Software Weakness

Today, the proportion of software in society as a whole is steadily increasing. In addition to size of software increasing, the number of cases dealing with personal information is also increasing. This shows the importance of weekly software security verification. However, software security is very difficult in cases where libraries do not have source code. To solve this problem, it is necessary to develop a technique for checking existing binary security weaknesses. To this end, techniques for analyzing security weaknesses using intermediate languages are actively being discussed. In this paper, we propose a system that translate binary code to intermediate language to effectively analyze existing security weaknesses within binary code.