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.

The recent advances in information technology for mobile devices have increased the work efficiency of users, the mobility of compact mobile devices, and the convenience of location independence. However, mobile devices have limited computing power and storage capacity, so mobile cloud computing is being researched to overcome these limitations in mobile devices. Mobile cloud computing is divided into two methods: the use of external cloud services and the use of mobile resource management without a cloud server (MRM), which integrates the computing and storage resources of nearby mobile devices. Because mobile devices can freely participate in MRM, it is critical to have authentication technology to determine the correctness of information regarding resources. Conventional technologies require strong authentication techniques because they have vulnerabilities that can easily be tampered with via man-in-the-middle (MITM) attacks. This paper proposes the Secure Authentication Management human-centric Scheme (SAMS) to authenticate mobile devices using blockchain for trusting resource information in the mobile devices that are participating in the MRM resource pool. The SAMS forms a blockchain based on the resource information of the subordinate client nodes around the master node in the MRM. Devices in the MRM that have not been authorized through the SAMS cannot access or falsify data. To verify the SAMS for application with MRM, it was tested for data falsification by a malicious user accessing the SAMS, and the results show that data falsification is impossible.

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.

Technological advances over the last decade in the field of wireless communications have resulted in the improvement of small and low cost sensor nodes outfitted with wireless communication abilities capable of establishing Wireless Sensor Network (WSN). Due to the expansion of Internet of Things (IoT), there are many areas in IoT application where WSN applications are found. These applications generally impose severe constraints on the lifetime of the WSN, which is expected to last several years. It is necessary to diminish the overall energy consumption of the sensor node and to find an additional source of energy for achieving this objective. On the other hand, due to the imminent crisis of the Radio Frequency (RF) spectrum, Light Fidelity (LiFi) offers many key benefits and effective solutions for these issues that have been postured in the most recent decade. In this paper, we propose a novel EH-HL model for future smart homes and industries based on the integration of Energy Harvesting Wireless Sensor Network (EH-WSNs) and hybrid LiFi/WiFi communication techniques. The proposed model is capable of efficiently transmitting data at high speed for bidirectional multi-device and by harvesting energy, we provide the power to the sensor nodes. To synchronize multi-device transmissions, transmit data and provide low-cost wireless communication, we used the color beams of the RGB LEDs. The result of the evaluation shows that the hybrid communication scheme is proposed in the EH-HL model. It also offers superior performance and achieves a data rate of 25 Mbps for multi-access/multi-users.