Search

Abstract With online work environments and other distributed computing systems—such as cloud technologies or Internet of Things systems—becoming increasingly popular today due to the COVID-19 pandemic and general technological advances, the question of how to keep them secure has also become a pertinent concern. With this increased dependence on online systems for companies, cyberattacks have also been on the rise. To protect terminal devices, many companies have resorted to implementing a single boundary-defense model. This method has yielded positive results in securing the network from external threats, but it does not effectively protect network from internal security threats. With the vulnerabilities in the internal network security in mind, a dynamic access control method used with a zero-trust software-defined perimeter security model could be a viable solution. This study proposes a dynamic access control method using an engine with a new reward and penalty point-based system (RP Engine) and a dynamic task engine (DT Engine) for a zero-trust SDP security model.

Abstract With the advent of the Internet, the activities of individuals and businesses have expanded into the online realm. As a result, vulnerabilities that result in actual breaches can lead to data loss and program failure. The number of breaches is increasing every year, as is the number of vulnerabilities. To address this problem, current research focuses on the detection of vulnerabilities using static analysis techniques. To prevent the propagation of vulnerabilities, a new paradigm is needed to quickly detect vulnerabilities, analyze them, and take actions such as blocking or removing them. Recently, artificial intelligence algorithms such as deep learning have been introduced for vulnerability detection. In this paper, we propose a vulnerability detection model, V-CNN, which aims to detect CWE/CVE (Common Weakness Enumeration/Common Vulnerabilities and Exposures) using CNN (convolutional neural network). We trained CWE for deep learning and redefined vulnerabilities based on CWE. We propose an experimental algorithm to improve vulnerability detection. The accuracy of the proposed V-CNN model is 98%, which exceeds the 95% of the random forest model. Therefore, our V-CNN has excellent correctness detection performance in the field of vulnerability detection. The V-CNN vulnerability detection algorithm can be used instead of static analysis to detect various security vulnerabilities.

Abstract Hybrid access point (HAP) is a node in wireless powered communication networks (WPCN) that can distribute energy to each wireless device and also can receive information from these devices. Recently, mobile HAPs have emerged for efficient network use, and the throughput of the network depends on their location. There are two kinds of metrics for throughput, that is, sum throughput and common throughput; each is the sum and minimum value of throughput between a HAP and each wireless device, respectively. Likewise, two types of throughput maximization problems can be considered, sum throughput maximization and common throughput maximization. In this paper, we focus on the latter to propose a deep learning-based methodology for common throughput maximization by optimally placing a mobile HAP for WPCN. Our study implies that deep learning can be applied to optimize a complex function of common throughput maximization, which is a convex function or a combination of a few convex functions. The experimental results show that our approach provides better performance than mathematical methods for smaller maps.

Abstract As the 4th industrial revolution using information becomes an issue, wireless communication technologies such as the Internet of Things have been spotlighted. Therefore, much research is needed to satisfy the technological demands for the future society. A LPWA (low power wide area) in the wireless communication environment enables low-power, long-distance communication to meet various application requirements that conventional wireless communications have been difficult to meet. We propose a method to consume the minimum transmission power relative to the maximum data rate with the target of LoRaWAN among LPWA networks. Reinforcement learning is adopted to find the appropriate parameter values for the minimum transmission power. With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. The simulation results show that the proposed method is about 15% better than the existing ADR (adaptive data rate) MAX of LoRaWAN in terms of throughput relative to energy transmission.

Abstract This paper proposes a large-scale wireless powered communication network (WPCN), which consists of a hybrid access point (H-AP) and numerous nodes. The H-AP broadcasts energy to all nodes, and the nodes harvest the energy and then use the harvested energy to transmit information. For the large-scale WPCN, we also propose a novel hybrid multiple access protocol, termed hybrid multiple access-based dual harvest-then-transmit. The proposed protocol utilizes both time division multiple access (TDMA) and carrier-sense multiple access (CSMA), in order to deal with various traffic patterns and transmission reliability of different applications in large-scale networks. We consider a dual wireless energy transfer (WET) at the H-AP. The main WET is performed in TDMA and the other WET is performed at space holes in CSMA, to increase the channel utilization and harvested energy. For the considerations, we study the sum-throughput maximization in the large-scale WPCN based on hybrid multiple access-based dual harvest-then-transmit. Simulation results show that the proposed protocol outperforms the conventional protocol in large-scale WPCN.