Internet of Things (IoT) technology provides the basic infrastructure for a hyper connected society where all things are connected and exchange information through the Internet. IoT technology is fused with 5G and artificial intelligence (AI) technologies for use various fields such as the smart city and smart factory. As the demand for IoT technology increases, security threats against IoT infrastructure, applications, and devices have also increased. A variety of studies have been conducted on the detection of IoT malware to avoid the threats posed by malicious code. While existing models may accurately detect malicious IoT code identified through static analysis, detecting the new and variant IoT malware quickly being generated may become challenging. This paper proposes a dynamic analysis for IoT malware detection (DAIMD) to reduce damage to IoT devices by detecting both well-known IoT malware and new and variant IoT malware evolved intelligently. The DAIMD scheme learns IoT malware using the convolution neural network (CNN) model and analyzes IoT malware dynamically in nested cloud environment. DAIMD performs dynamic analysis on IoT malware in a nested cloud environment to extract behaviors related to memory, network, virtual file system, process, and system call. By converting the extracted and analyzed behavior data into images, the behavior images of IoT malware are classified and trained in the Convolution Neural Network (CNN). DAIMD can minimize the infection damage of IoT devices from malware by visualizing and learning the vast amount of behavior data generated through dynamic analysis.

In this article, we propose the hybrid malware detection scheme, HyMalD, with bidirectional long short-term memory (Bi-LSTM) and the spatial pyramid pooling network (SPP-Net). Its purpose is to protect Internet of Things (IoT) devices and minimize the damage caused by infection through obfuscated malware. HyMalD performs the static and dynamic analyses logically simultaneously to detect obfuscated malware, which is impossible to do using static analysis alone. First, it extracts static features of the opcode sequence using a reconstructed dataset according to the obfuscation and extracts the application programming interface (API) call sequence dynamically. The extracted features are trained through the Bi-LSTM and SPP-Net models, which HyMalD uses to detect and classify IoT malware. The performance of HyMalD was evaluated, and its detection accuracy was 92.5%. The false-negative rate (FNR) of HyMalD was 7.67%. Thus, HyMalD detects IoT malware more accurately and with a lower FNR than in the static analysis, which had 92.09% detection accuracy and 9.97% FNR.

With the steady increase in the demand for Internet of Things (IoT) devices in diverse industries, such as manufacturing, medical care, and transportation infrastructure, the production of malware tailored for Smart IoT environments is also increasing. Accordingly, various malware detection studies are being conducted to detect not only known malware but also variant malware. However, it is difficult to detect malware transformed in a way that hides malicious behavior by changing and deleting bytes or modifying the assembly code. Therefore, in this study, we propose a malware detection for static security service (Mal3S) scheme that provides a secure Smart IoT environment by accurately detecting various types of malware. Mal3S extracts bytes, opcodes, API calls, strings, and dynamic link libraries (DLLs) through static analysis and then generates five types of images. Images of various sizes are trained on a multi spatial pyramid pooling network (SPP-net) model to detect malware. When evaluating the performance of Mal3S using three malware datasets, the average detection accuracy was 98.02% and the classification accuracy was 98.43%, showing better performance than existing malware detection techniques. In addition, Mal3S has demonstrated effective generalization capabilities for various types of malware.

The container resource autoscaling technique provides scalability to cloud services composed of microservice architecture in a cloud-native computing environment. However, the service efficiency is reduced as the scaling is delayed because dynamic loads occur with various workload patterns. Furthermore, estimating the efficient resource size for the workload is difficult, resulting in resource waste and overload. Therefore, this study proposes high-performance resource management (HiPerRM), which stably and elastically manages container resources to ensure service scalability and efficiency even under rapidly changing dynamic loads. HiPerRM forecasts future workloads using a sample convolutional and interaction network (SCINet) model applied with the reversible instance normalization (RevIN) method. HiPerRM generates a resource request with an elastic size based on the forecasted CPU and memory usage, and then efficiently adjusts the pod's resource request and the number of replicas via HiPerRM's VPA (Hi-VPA) and HiPerRM's HPA (Hi-HPA). As a result of evaluating the performance of HiPerRM, the average resource utilization was improved by approximately 3.96–34.06% compared to conventional autoscaling techniques, even when the resource size was incorrectly estimated for various workloads, and there were relatively fewer overloads.

With the advent of Industry 4.0, high-quality consumer electronics are being efficiently produced by simulating a virtual model connected to a physical object using the digital twin (DT) technology. Furthermore, high-performance cloud computing technology is being used to simulate resource-intensive consumer electronics DT. Virtual machine (VM)-based DT simulation can simulate various DTs in parallel, but VM is heavy due to the hypervisor and has a slow startup time. In contrast, container-based DT simulation is lightweight and fast-driving and can elastically utilize computing resources. However, it causes a scaling delay problem and affords a degraded DT simulation performance. Therefore, this study proposes intelligence resource scaling (IReS) for efficient consumer electronics DT simulation in a high-performance cloud computing environment. IReS continuously monitors the container’s workload and predicts computing resource requirements using a DLinear model to respond to future workloads. Through predicted computing resource requirements, IReS calculates the optimal number of replicas that can handle future workloads and then performs horizontal autoscaling. The evaluation of the IReS performance shows that it elastically provided computing resources according to the workload changes regardless of the execution time of the consumer electronics DT simulation and optimized the resource cost for DT simulation.