• The Journal of the Convergence on Culture Technology
• /
• v.9 no.5
• /
• pp.613-618
• /
• 2023

The advancement of Information and Communication Technology (ICT) is accelerating innovation across society, and the defense sector is no exception as it adopts technologies aligned with the Fourth Industrial Revolution. In particular, the Army is making efforts to establish an advanced Army TIGER 4.0 system, aiming to create highly intelligent and interconnected mobile units. To achieve this, the Army is integrating cutting-edge scientific and technological advancements from the Fourth Industrial Revolution to enhance mobility, networking, and intelligence. However, the existing addressing system, IPv4, has limitations in meeting the exponentially increasing demands for network IP addresses. Consequently, the military considers IPv6 address allocation as an essential process to ensure efficient network management and address space provisioning. This study proposes an approach for IPv6 address allocation for the future military, considering the Army TIGER system. The proposal outlines how the application networks of the Army can be differentiated, and IP addresses can be allocated to future unit structures of the Army, Navy, and Air Force, from the Ministry of National Defense and the Joint Chiefs of Staff. Through this approach, the Army's advanced ground combat system, Army TIGER 4.0, is expected to operate more efficiently in network environments, enhancing overall information exchange and mobility for the future military.

• Journal of Internet Computing and Services
• /
• v.25 no.1
• /
• pp.157-166
• /
• 2024

The Army TIGER System, which is being deployed to implement a future combat system, is expected to bring innovative changes to the army's combat methods and comabt execution capability such as mobility, networking and intelligence. To this end, the Army will introduce various systems using drones, robots, unmanned vehicles, AI(Artificial Intelligence), etc. and utilize them in combat. The use of various unmanned vehicles and AI is expected to result in the introduction of equipment with new technologies into the army and an increase in various types of transmitted information, i.e. data. However, currently in the military, there is an acceleration in research and combat experimentations on warfigthing options using Army TIGER forces system for specific functions. On the other hand, the current reality is that research on cyber threats measures targeting information systems related to the increasing number of unmanned systems, data production, and transmission from unmanned systems, as well as the establishment of cloud centers and AI command and control center driven by the new force systems, is not being pursued. Accordingly this paper analyzes the structure and characteristics of the Army TIGER force integration system and makes suggestions for necessity of building, available cyber defense solutions and Army TIGER integrated cyber protections system that can respond to cyber threats in the future.

• Information Systems Review
• /
• v.22 no.3
• /
• pp.99-118
• /
• 2020

At present, our society is developing into the intelligent information society in the wave of the 4th industrial revolution, and this change will have the positive effect of innovating all industry fields. However, due to the duality of technology, there will be positive and negative effects. With intelligence, threats to cyber dysfunction such as hacking, terrorism, privacy infringement, and illegal content distribution will become more serious. Until now, the security system of the Internet has been a proactive security system, but in recent years, a proposal for a trust network, a preventive security system, has been introduced. Therefore, this study aims to analyze the possibility of resolving cyber dysfunction of intelligent information society about Bright Internet, one of trust network technologies. This study defines the cyber dysfunction of the intelligent information society and analyzes the perceptions of changes in the cyber dysfunction of the intelligent information society on the introduction of the five principles of the Bright Internet. The change of cyber dysfunction severity of the intelligent information society due to the introduction of the trust network is analyzed to reflect the technical and social demands. This work will guide the structure of the trust network and the direction of practical technological introduction and its influence.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.19 no.1
• /
• pp.149-156
• /
• 2024

The increased intelligence and speed of vehicle infotainment, whose main purpose was emergency and external communication, is showing the potential for application to various services such as navigation and autonomous driving. In particular, functionality for linking external devices and infrastructure is being strengthened due to advances in communication and networks. Under this trend, it is necessary to consider the direction of linkage with the cooperative intelligent transportation system (C-ITS) for advanced vehicle services and driving. In addition, in the case of automobiles, future vehicle development concepts are being established based on the concept of software-defined vehicles (SDVs) in line with the trend of electrification beyond telematics and infotainment advancements, and such SDV linkage must be considered at the same time. In this paper, we consider the future direction of ITS and SDV linkage based on analysis of vehicle infotainment structure. First, for this purpose, we analyze the existing vehicle infotainment structure and architecture, and also present the structure of the SDV linked to it. Based on this, analysis and implications are drawn on the possibility of applying and linking standard-based C-ITS services with SDV devices.

• Smart Media Journal
• /
• v.13 no.4
• /
• pp.33-48
• /
• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Korean Security Journal
• /
• no.41
• /
• pp.293-326
• /
• 2014

Piracy is a worldwide issue, but the deteriorating security situation in the seas off Somalia, the Gulf of Aden and the wider Western Indian Ocean between 2005 and 2012 and in the increasing number of attacks in the Gulf of Guinea are a major problem. The depth of concern for the problem internationally is amply demonstrated by the levels of co-operation and coordination among naval and other forces from several countries that have assembled in the west Indian Ocean region and the Gulf of Aden to escort ships carrying humanitarian aid to Somalia and to protect vulnerable shipping. Notwithstanding this unprecedented effort, the vast sea area in which the pirates now operate makes it difficult to patrol and monitor effectively, particularly with the limited resources available. More resources, in the form of naval vessels and aircraft, are needed and at every opportunity the IMO encourages Member Governments to make greater efforts to provide the additional naval, aerial surveillance and other resources needed through every means possible. IMO provide interim guidance and recommendations to be taken into account when considering the use of PCASP(privately contracted armed security personnel) if and when a flag State determines that such a measure would be lawful and, following a full risk assessment, appropriate. The interim guidance and recommendations of IMO are not intended to endorse or institutionalize the use of armed guards. Therefore, they do not represent any fundamental change of policy by the Organization in this regard. It is for each flag State, individually, to decide whether or not PCASP should be authorized for use on board ships flying their flag. If a flag State decides to permit this practice, it is up to that State to determine the conditions under which authorization will be granted. Therefore, Korea should be introduced rationally PCASP for safe shipping. PCASP on board ships is much the same to special guard personnel of security services industry act. Act plan of Oceans and fisheries ministry on PCASP collides with special guard personnel system of National Police Agency. Rather than new law making, PCASP regukations have to be included in security services industry act. Management Agency of PCASP is to not Oceans and fisheries ministry, but Central Headquarters Korea Coast Guard of Public Safety and Security Ministry because of specialty and closely connection.

• Korean Security Journal
• /
• no.34
• /
• pp.89-113
• /
• 2013

Travel by airplane using airport in globalized modern society is familiar to our life but such airport can be a target of terrorists who would threaten our safety. However, aviation terrorism which is represented by 9.11 terror gives horror beyond our imagination to modern people. Since the first airplane hijacking in Peru in 1931 happened, security organization in each nation has taken various prevention measures to block aviation terrorism. The most realistic measures to prevent aviation terrorism would be security check activity to control approach of terrorist that passengers on airplane and cargos are checked to find out dangerous article like explosive etc. But security check activity in Korea shifted to security check activity focused on private security for efficiency of airport operation differently from security check activity in advanced countries where public interests is strengthened according to risk of terror after 9.11 system. That is, since Incheon airport opened in March 2001, security check system focused on the police was changed. Now Incheon international airport Corporation instructs and supervises security check job and private security personnel are in charge of actual security check activity. But this check system has limitation in blocking terror activity by terrorists which is systematized and intelligent as time passes due to deteriorated job satisfaction of private security personnel and confusing supervising system. Accordingly, it is suggested to introduce risk management skill which is one of management strategy of private corporation for security check activity to prevent terror activity. With this skill, risk factors of aviation terror are identified and analyzed regularly, and each process such as setting of priority, activity to reduce risk, and assessment of security is carried out. And it would be necessary to do efforts, for example, to properly change level of security check according to threat of terror such as to place policeman at airport security in case threat of terror is severe. On the other hand, it is necessary to establish national police organization for good communication of security check activity in field and supervising function, which can encourage systematization and specialization of aviation security job.

• The Korean Journal of Air & Space Law and Policy
• /
• v.32 no.2
• /
• pp.155-188
• /
• 2017

On the one hand, the 4th Industrial Revolution provides a positive opportunity to build a new civilization paradigm for mankind. However, on the other hand, due to the 4th Industrial Revolution, artificial intelligence such as 'Goggle Alpha Go' revolutionized and even the human ability was replaced with a 'Silicon Chip' as the opportunity to communicate decreases, the existence of human beings is weakened. And there is a growing concern that the number of violent crimes, such as psychopath, which hunts humans as games, will increase. Moreover, recent international terrorism is being developed in a form similar to 'Psychopathic Violent-Crime' that indiscriminately attacks innocent people. So, the probability that terrorist organizations abuse the positive effects provided by the Fourth Industrial Revolution as means of terrorism is increasing. Therefore, the paradigm of aviation terrorism is expected to change in a way that attacks airport facilities and users rather than aircraft. Because airport facilities are crowded, and psychopathic terrorists are easily accessible. From this point of view, our counter terrorism system of aviation has many weak points in various aspects such as: (1) limitations of counter-terrorism center (2) inefficient on-site command and control system (3) separated organization for aviation security consultation (4) dispersed information collection function in government (5) vulnerable to cyber attack (6) lack of international cooperation network for aviation terrorism. Consequently, it is necessary to improve the domestic counter terrorism system of aviation so as to preemptively respond to the international terrorism. This study propose the following measures to improve the aviation security system by (1) create 'Aviation Special Judicial Police' (2) revise the anti-terrorism law and aviation security law (3) Strengthening the ability respond to terrorism in cyberspace (4) building an international cooperation network for aviation terrorism.

• Journal of Intelligence and Information Systems
• /
• v.21 no.1
• /
• pp.161-177
• /
• 2015

With the rapid evolution of technology, the size, number, and the type of databases has increased concomitantly, so data mining approaches face many challenging applications from databases. One such application is discovery of fraud patterns from agricultural product wholesale transaction instances. The agricultural product wholesale market in Korea is huge, and vast numbers of transactions have been made every day. The demand for agricultural products continues to grow, and the use of electronic auction systems raises the efficiency of operations of wholesale market. Certainly, the number of unusual transactions is also assumed to be increased in proportion to the trading amount, where an unusual transaction is often the first sign of fraud. However, it is very difficult to identify and detect these transactions and the corresponding fraud occurred in agricultural product wholesale market because the types of fraud are more intelligent than ever before. The fraud can be detected by verifying the overall transaction records manually, but it requires significant amount of human resources, and ultimately is not a practical approach. Frauds also can be revealed by victim's report or complaint. But there are usually no victims in the agricultural product wholesale frauds because they are committed by collusion of an auction company and an intermediary wholesaler. Nevertheless, it is required to monitor transaction records continuously and to make an effort to prevent any fraud, because the fraud not only disturbs the fair trade order of the market but also reduces the credibility of the market rapidly. Applying data mining to such an environment is very useful since it can discover unknown fraud patterns or features from a large volume of transaction data properly. The objective of this research is to empirically investigate the factors necessary to detect fraud transactions in an agricultural product wholesale market by developing a data mining based fraud detection model. One of major frauds is the phantom transaction, which is a colluding transaction by the seller(auction company or forwarder) and buyer(intermediary wholesaler) to commit the fraud transaction. They pretend to fulfill the transaction by recording false data in the online transaction processing system without actually selling products, and the seller receives money from the buyer. This leads to the overstatement of sales performance and illegal money transfers, which reduces the credibility of market. This paper reviews the environment of wholesale market such as types of transactions, roles of participants of the market, and various types and characteristics of frauds, and introduces the whole process of developing the phantom transaction detection model. The process consists of the following 4 modules: (1) Data cleaning and standardization (2) Statistical data analysis such as distribution and correlation analysis, (3) Construction of classification model using decision-tree induction approach, (4) Verification of the model in terms of hit ratio. We collected real data from 6 associations of agricultural producers in metropolitan markets. Final model with a decision-tree induction approach revealed that monthly average trading price of item offered by forwarders is a key variable in detecting the phantom transaction. The verification procedure also confirmed the suitability of the results. However, even though the performance of the results of this research is satisfactory, sensitive issues are still remained for improving classification accuracy and conciseness of rules. One such issue is the robustness of data mining model. Data mining is very much data-oriented, so data mining models tend to be very sensitive to changes of data or situations. Thus, it is evident that this non-robustness of data mining model requires continuous remodeling as data or situation changes. We hope that this paper suggest valuable guideline to organizations and companies that consider introducing or constructing a fraud detection model in the future.

• Journal of Intelligence and Information Systems
• /
• v.18 no.1
• /
• pp.125-141
• /
• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.