• Journal of the Korea Society of Computer and Information
• /
• v.28 no.10
• /
• pp.37-53
• /
• 2023

The AI based intelligent command and control system can automatically analyzes the properties of intricate battlefield information and tactical data. In addition, commanders can receive situation analysis results and battlefield awareness through the system to support decision-making. It is necessary to build a battlefield situation analysis dataset similar to the actual battlefield situation for learning AI in order to provide decision-making support to commanders. In this paper, we explain the next step of the dataset construction method of the existing previous research, 'A Virtual Battlefield Situation Dataset Generation for Battlefield Analysis based on Artificial Intelligence'. We proposed a method to build the dataset required for the final battlefield situation analysis results to support the commander's decision-making and recognize the future battlefield. We developed 'Dataset Generator SW', a software tool to build a learning dataset for battlefield situation analysis, and used the SW tool to perform data labeling. The constructed dataset was input into the Siamese Network model. Then, the output results were inferred to verify the dataset construction method using a post-processing ranking algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.2
• /
• pp.749-766
• /
• 2021

Cyber powers around the world are conducting cyber information-gathering activities in cyberspace, a global domain within the Internet-based information environment. Accordingly, it is imperative to obtain the latest information through the cyber intelligence preparation of the battlefield (IPB) process to prepare for future cyber operations. Research utilizing the cyber battlefield visualization method for effective cyber IPB and situation awareness aims to minimize uncertainty in the cyber battlefield and enable command control and determination by commanders. This paper designed architecture by classifying cyberspace into a physical, logical network layer and cyber persona layer to visualize the cyber battlefield using BGP archive data, which is comprised of BGP connection information data of routers around the world. To implement the architecture, BGP archive data was analyzed and pre-processed, and cyberspace was implemented in the form of a Di-Graph. Information products that can be obtained through visualization were classified for each layer of the cyberspace, and a visualization method was proposed for performing cyber IPB. Through this, we analyzed actual North Korea's BGP and OSINT data to implement North Korea's cyber battlefield centered on the Internet network in the form of a prototype. In the future, we will implement a prototype architecture based on Elastic Stack.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.4
• /
• pp.11-17
• /
• 2020

In this paper, we propose a method to build a knowledge base based on natural language processing for intelligent battlefield awareness service. The current command and control system manages and utilizes the collected battlefield information and tactical data at a basic level such as registration, storage, and sharing, and information fusion and situation analysis by an analyst is performed. This is an analyst's temporal constraints and cognitive limitations, and generally only one interpretation is drawn, and biased thinking can be reflected. Therefore, it is essential to aware the battlefield situation of the command and control system and to establish the intellignet decision support system. To do this, it is necessary to build a knowledge base specialized in the command and control system and develop intelligent battlefield awareness services based on it. In this paper, among the entity names suggested in the exobrain corpus, which is the private data, the top 250 types of meaningful names were applied and the weapon system entity type was additionally identified to properly represent battlefield information. Based on this, we proposed a way to build a battlefield-aware knowledge base through mention extraction, cross-reference resolution, and relationship extraction.

• The Journal of Korea Robotics Society
• /
• v.18 no.4
• /
• pp.436-443
• /
• 2023

With the advancement of robot intelligence, the concept of man and unmanned teaming (MUM-T) has garnered considerable attention in military research. In this paper, we present a transformer-based architecture for predicting the health status of agents, with the help of multi-head attention mechanism to effectively capture the dynamic interaction between friendly and enemy forces. To this end, we first introduce a framework for generating a dataset of battlefield situations. These situations are simulated on a virtual simulator, allowing for a wide range of scenarios without any restrictions on the number of agents, their missions, or their actions. Then, we define the crucial elements for identifying the battlefield, with a specific emphasis on agents' status. The battlefield data is fed into the transformer architecture, with classification headers on top of the transformer encoding layers to categorize health status of agent. We conduct ablation tests to assess the significance of various factors in determining agents' health status in battlefield scenarios. We conduct 3-Fold corss validation and the experimental results demonstrate that our model achieves a prediction accuracy of over 98%. In addition, the performance of our model are compared with that of other models such as convolutional neural network (CNN) and multi layer perceptron (MLP), and the results establish the superiority of our model.

• Journal of the Korea Society of Computer and Information
• /
• v.27 no.6
• /
• pp.33-42
• /
• 2022

In the existing intelligent command control system study, the analysis results of the commander's battlefield situation questions are provided from knowledge-based situation data. Analysis reporters write these results in various expressions of natural language. However, it is important to analyze situations about information and intelligence according to context. Analyzing the battlefield situation using artificial intelligence is necessary. We propose a virtual dataset generation method based on battlefield simulation scenarios in order to provide a dataset necessary for the battlefield situation analysis based on artificial intelligence. Dataset is generated after identifying battlefield knowledge elements in scenarios. When a candidate hypothesis is created, a unit hypothesis is automatically created. By combining unit hypotheses, similar identification hypothesis combinations are generated. An aggregation hypothesis is generated by grouping candidate hypotheses. Dataset generator SW implementation demonstrates that the proposed method can be generated the virtual battlefield situation dataset.

• Journal of the Korea Society of Computer and Information
• /
• v.26 no.11
• /
• pp.33-40
• /
• 2021

Due to advanced complex strategies, the complexity of information that a commander must analyze is increasing. An intelligent service that can analyze battlefield is needed for the commander's timely judgment. This service consists of extracting knowledge from battlefield information, building a knowledge base, and analyzing the battlefield information from the knowledge base. This paper extract information similar to an input query by embedding the knowledge base built in the 2^nd step. The transformation model is needed to generate the embedded knowledge base and uses the random-walk algorithm. The transformed information is embedding using Word2Vec, and Similar information is extracted through cosine similarity. In this paper, 980 sentences are generated from the open knowledge base and embedded as a 100-dimensional vector and it was confirmed that similar entities were extracted through cosine similarity.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.1
• /
• pp.1-8
• /
• 2023

In this paper, we propose a training method of a recommendation learning model that analyzes the battlefield situation and recommends a suitable hypothesis for the current situation. The proposed learning model uses the preference determined by comparing the two hypotheses as a label data to learn which hypothesis best analyzes the current battlefield situation. Our model is based on Siamese neural network architecture which uses the same weights on two different input vectors. The model takes two hypotheses as an input, and learns the priority between two hypotheses while sharing the same weights in the twin network. In addition, a score is given to each hypothesis through the proposed post-processing ranking algorithm, and hypotheses with a high score can be recommended to the commander in charge.

• Convergence Security Journal
• /
• v.22 no.4
• /
• pp.65-73
• /
• 2022

Since cyber operations are performed in a virtual cyber battlefield, the measurement indicators that can evaluate and visualize the current state of the cyber environment in a consistent form are required for the commander to effectively support the decision-making of cyber operations. In this paper, we propose a method to define various evaluation indicators that can be collected on the cyber battlefield, normalized them, and evaluate the cyber status in a consistent form. The proposed cyber battlefield status element consists of cyber asset-related indicators, target network-related indicators, and cyber threat-related indicators. Each indicator has 6 sub-indicators and can be used by assigning weights according to the commander's interests. The overall status of the cyber battlefield can be easily recognized because the measured indicators are visualized in time series on a single screen. Therefore, the proposed method can be used for the situational awareness required to effectively conduct cyber warfare.

• Journal of IKEEE
• /
• v.15 no.1
• /
• pp.104-112
• /
• 2011

During the ground operation of Korean army, the voice message is mainly used for exchanging informations related to the surveillance and reconnaissance, command and control, and precision strike. However, in order to the battlefield visualization among fighting powers participating in the ground force operation, automatic situational awareness and variable message format (VMF) for command and control are required. For securing core technologies necessary for the battlefield visualization, message standard and message handler are established through several applied researches. Besides, the VMF for equipping a weapon system is in development. In this paper, a study on the Korean variable message format (KVMF), where interoperability of integrated battle management system (BMS) is guaranteed due to performing joint, ground, and combined operations so that the situation awareness and strike system can be automated in almost real time, is presented. From the modeling and simulation (M&S) results of the message processor, delay time is varied in accordance with the number of nodes in unit platoon network, message length, and generation interval of routine messages. Therefore, it is shown that the system performance can be optimized by establishing proper network protocol for each situation.

• Journal of Internet Computing and Services
• /
• v.24 no.6
• /
• pp.127-135
• /
• 2023

With the development of information and communication technology, the amount of information produced and shared in the battlefield and stored and managed in the system dramatically increased. This means that the amount of information which cansupport situational awareness and decision making of the commanders has increased, but on the other hand, it is also a factor that hinders rapid decision making by increasing the information overload on the commanders. To overcome this limitation, this study proposes a method to automatically search, select, and summarize documents that can help the commanders to understand the battlefield situation reports that he or she received. First, named entities are discovered from the battlefield situation report using a named entity recognition method. Second, the documents related to each named entity are discovered. Third, a language model and collaborative filtering are used to select the documents. At this time, the language model is used to calculate the similarity between the received report and the discovered documents, and collaborative filtering is used to reflect the commander's document reading history. Finally, sentences containing each named entity are selected from the documents and sorted. The experiment was carried out using academic papers since their characteristics are similar to military documents, and the validity of the proposed method was verified.