Intelligent Transport Systems (ITS) are rapidly transforming modern transportation through real-time data exchange between vehicles and infrastructure. As these systems increasingly rely on seamless communication, ensuring both security and low latency is paramount. However, traditional encryption algorithms, such as RSA and ECC, often impose computational burdens that are incompatible with the stringent time and energy constraints of ITS environments. To address this challenge, we propose a novel security framework that integrates the Residue Number System (RNS) with Montgomery modular multiplication. This combination enables e cient modular arithmetic through parallel processing, reducing the overall computational complexity. The framework was implemented and evaluated within a simulated Vehicle-toEverything (V2X) environment using OMNeT++ and SUMO. The results demonstrate that the RNS-Montgomery approach reduces encryption time by approximately 34% and energy consumption by nearly 29% compared to traditional methods, while maintaining equivalent cryptographic strength. The proposed framework o ers a viable and scalable solution for secure ITS communications. Its efficiency and resistance to side channel attacks position it as a promising direction for future deployment in latency-sensitive and resource-constrained vehicular networks.

Determining the timing of buying and selling in stock investment is one of the most important factors to increase the return on stock investment. Buying low and selling high makes a profit, but buying high and selling low makes a loss. The price is determined by the quantity of buying and selling, which determines the price of a stock, and buying and selling is also related to corporate performance and economic indicators. The fear and greed index provided by CNN uses seven factors, and by assigning weights to each element, the weighted average defined as greed and fear is calculated on a scale between 0 and 100 and published every day. When the index is close to 0, the stock market sentiment is fearful, and when the index is close to 100, it is greedy. Therefore, we analyze the trading criteria that generate the maximum return when buying and selling the US S&P 500 index according to CNN fear and greed index, suggesting the optimal buying and selling timing to suggest a way to increase the return on stock investment.

Background: Information and communication technology development provides services to various fields. In particular, the development of mobile technology has made ubiquitous services possible. However, as technology advances, security is being emphasized more and more. The existing method of user authentication by entering an ID and password is likely to leak sensitive information if the server is attacked or keylogged. Therefore, multi-level authentication is needed to prevent server attacks or keylogging. Methods: Biometric authentication technology has been utilized by smartphones, but it is challenging to apply in the case of a lost device because the central server does not manage biometric data. Encrypting transactions through blockchain technology makes data management more secure because blockchain technology is distributed, and there is no primary target for hackers. Results: The method proposed in this study can become a basis for an authentication method that guarantees both security and integrity due to the synergetic use of biometric and blockchain technologies. Conclusions: The information stored in the service provider does not include sensitive information such as the user’s biometric data but is only a means of verifying the user’s information in the blockchain transaction with guaranteed anonymity. Therefore, users can receive services with confidence and safety.

Recently, research on predictive systems using deep learning and machine learning of artificial intelligence is being actively conducted. Due to the development of artificial intelligence, the role of the investment manager is being replaced by artificial intelligence, and due to the higher rate of return than the investment manager, algorithmic trading using artificial intelligence is becoming more common. Algorithmic trading excludes human emotions and trades mechanically according to conditions, so it comes out higher than human trading yields when approached in the long term. The deep learning technique of artificial intelligence learns past time series data and predicts the future, so it learns like a human and can respond to changing strategies. In particular, the LSTM technique is used to predict the future by increasing the weight of recent data by remembering or forgetting part of past data. fbprophet, an artificial intelligence algorithm recently developed by Facebook, boasts high prediction accuracy and is used to predict stock prices and cryptocurrency prices. Therefore, this study intends to establish a sound investment culture by providing a new algorithm for automatic cryptocurrency trading by analyzing the actual value and difference using fbprophet and presenting conditions for accurate prediction.

As part of the digital era, a digital twin that simulates the weak part of a product by performing a stress test that reduces the lifespan of some expensive equipment that cannot be done in reality by accurately moving the real world to virtual reality is being actively used in the manufacturing industry. Due to the development of IoT, the digital twin, which accurately collects data collected from the real world and makes it the same in the virtual space, is mutually beneficial through accurate prediction of urban life problems such as traffic, disaster, housing, quarantine, energy, environment, and aging. Based on its action, it is positioned as a necessary tool for smart city construction. Although digital twin is widely applied to the manufacturing field, this study proposes a smart city model suitable for the 4th industrial revolution era by using it to smart cities and increasing citizens' safety, welfare, and convenience through the proposed model. In addition, when a digital twin is applied to a smart city, it is expected that more accurate prediction and analysis will be possible by real-time synchronization between the real and virtual by maintaining realism and immediacy through real-time interaction.

Background/Objectives: The wireless internet service, which has positioned as an important element to support all industries, can be connected to notebook computers and smartphones everywhere. Using wireless internet access increases, the risk of hacking also increases. Methods/Statistical analysis: There is information leakage accident by modulating DNS address of home router and hacking threats by using wireless router always exists Findings: In this paper, we search hacking techniques using vulnerabilities in wireless LAN, and analyze the need for security for wireless LANs through WEP encryption algorithms and improved encryption algorithms. Improvements/Applications: We also suggest countermeasures against hacking techniques such as DoS attacks, WEP Crack, and DNS Spoofing.

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

The macroeconomic concept represents the movement of a country's economy, and it affects the overall economic activities of business, government, and households. In the macroeconomy, by looking at changes in national income, inflation, unemployment, currency, interest rates, and raw materials, it is possible to understand the effects of economic actors' actions and interactions on the prices of products and services. The US Federal Reserve System (FED) is leading the world economy by offering various stimulus measures to overcome the corona economic recession. Although the stock price continued to decline on March 20, 2020 due to the current economic recession caused by the corona, the US S&P 500 index began rebounding after March 23 and to 3,694.62 as of December 15 due to quantitative easing, a powerful stimulus for the FED. Therefore, the FED's economic stimulus measures based on macroeconomic indicators are more influencing, rather than judging the stock price forecast from the corporate financial statements. Therefore, this study was conducted to reduce losses in stock investment and establish sound investment by analyzing the FED's economic stimulus measures and its effect on stock prices.

Since the stock price is a measure of the future value of the company, when analyzing the stock price, the company's growth potential, such as sales and profits, is considered and invested in stocks. In order to set the criteria for selecting stocks, institutional investors look at current industry trends and macroeconomic indicators, first select relevant fields that can grow, then select related companies, analyze them, set a target price, then buy, and sell when the target price is reached. Stock trading is carried out in the same way. However, general individual investors do not have any knowledge of investment, and invest in items recommended by experts or acquaintances without analysis of financial statements or growth potential of the company, which is lower in terms of return than institutional investors and foreign investors. Therefore, in this study, we propose a research method to select undervalued stocks by analyzing ROE, an indicator that considers the growth potential of a company, such as sales and profits, and predict the stock price flow of the selected stock through deep learning algorithms. This study is conducted to help with investment.

Currently, many researchers are working on stock price prediction system by using deep learning algorithms. Stock market is completely random, and there is no pattern. Even though, a pattern in stock market could be found, it will not be last for a long time because the stock market will adopt a new situation and the strategy is no longer available on already changed stock market. There are many auto trading programs such as a trading bot on stock market. However, they are literally trade stocks based on human’s direction or rules. It will not affect any changes, and it keeps working as what rules are set up from the initial status on the stock market. Stock price depends on volume of total sales, stock news, revenue, total asset, big buyer’s position and so on. There are many aspects for affecting stock price, and it changes all the time. Therefore, it keeps monitoring stock market and makes a decision whether buy or sell at the right time for earning profits. This research uses Bidirectional Long Short-Term Memory (BLSTM) to predict stock price in the near future. BLSTM is more accurate than LSTM which is one directional. In addition, stock market is like a living creature. Data to manipulate stock price must be inputted and analyzed consistently. Therefore, stock price can be predicted by consistent monitoring with BLSTM.

While blockchain platforms for various purposes have been developed and the blockchain ecosystem is being developed, interoperability problems are emerging in which each blockchain is isolated and operated. In this study, we introduce interchain and sidechain technologies, which are blockchain that connect blockchain, and explain examples of using heterogeneous blockchain transactions and functions by applying them. In addition, blockchain, artificial intelligence, and IoT technologies, which are drawing attention in the fourth industrial revolution, are going through a process of converging and developing beyond their own development. In this regard, we present processes for combining artificial intelligence or IoT in blockchain, and propose a model that can operate without intervention by applying the combination of blockchain and artificial intelligence IoT to processes for trading and exchange between heterogeneous blockchain.

Background/Objectives: As urbanization increases worldwide, so do the need for smart cities that enable more efficient city management.Based on smart city operational data, it explains the digital twin technologies required to predict outcomes and control according to circumstances through virtual simulations and presents related metaverse technologies.Methods/Statistical analysis: Introduction and explanation of smart city services using blockchain.The data infrastructure underlies each area, such as city operations, essential in a smart city.Findings: Therefore, blockchain is used to avoid hacking, data errors, and system failures of the existing centralized management and ensure data integrity and system stability by building a more secure server.In addition, blockchain enables the establishment of governance and free access to information through individual information sovereignty.Improvements/Applications: Most smart city models do not have a high level of authentication.Therefore, this study investigated a new smart city model that provides security and convenience to citizens by mutually complementing the virtual and real worlds by predicting urban situations through digital twins that can simulate impossible conditions in reality based on blockchain-based authentication.

Artificial intelligence technology, which is the core of the 4th industrial revolution, is making intelligent judgments through deep learning techniques and machine learning that it is impossible to predict if it is applied to stock prediction beyond human capabilities. In US fund management companies, artificial intelligence is replacing the role of stock market analyst, and research in this field is actively underway. In this study, we use BLSTM to reduce errors that occur in unidirectional prediction of the existing LSTM method, reduce errors in predictions by predicting in both directions, and macroscopic indicators that affect stock prices, namely, economic growth rate, economic indicators, interest rate, analyze the trade balance, exchange rate, and volume of currency. To help stock investment by accurately predicting the target price of stocks by analyzing the PBR, BPS, and ROE of individual stocks after analyzing macro-indicators, and by analyzing the purchase and sale quantities of foreigners, institutions, pension funds, etc., which have the most influence on stock prices.

Background/Objectives: Drones were originally developed for military use, and as their technology evolves, they can be said to be the new industrial sector of the 4th Industrial Revolution, which has unlimited possibilities.Methods/Statistical analysis: The first was a hot air balloon, which was actually used in the Battle of Austria in 1849 and subsequently as an important combat weapon in World Wars I and II.On the top of that, drone is unmanned, remote-controlled flight systems.Findings: It's widely used as an entertainment tool, but actually it's not only used for military purposes such as reconnaissance and surveillance, but also for transportation like Amazon Prime Air.Drones are vulnerable to hacking like GPS spoofing, control extortion, and jamming because they're wirelessly controlled.There are these various anti-drones technologies and there are grave risks to drone security.Improvements/Applications: Therefore, it is necessary to protect privacy from unwanted drone to use anti-drones technology.

Macroeconomics are one of the indicators that are preceded and analyzed when analyzing stocks because it shows the movement of a country's economy as a whole. The overall economic situation at the national level, such as national income, inflation, unemployment, exchange rates, currency, interest rates, and balance of payments, has a great affect on the stock market, and economic indicators are actually correlated with stock prices. It is the main source of data for analysts to watch with interest and to determine buy and sell considering the impact on individual stock prices. Therefore, economic indicators that impact on the stock price are analyzed as leading indicators, and the stock price prediction is predicted through deep learning-based prediction, after that the actual stock price is compared. If you decide to buy or sell stocks by analysis of stock prediction, then stocks can be investments, not gambling. Therefore, this research was conducted to enable automated stock trading by using macro-indicators and deep learning algorithms in artificial intelligence.

The stock price reflects people's psychology, and factors affecting the entire stock market include economic growth rate, economic rate, interest rate, trade balance, exchange rate, and currency. The domestic stock market is heavily influenced by the stock index of the United States and neighboring countries on the previous day, and the representative stock indexes are the Dow index, NASDAQ, and S & P500. Recently, research on stock price analysis using stock news has been actively conducted, and research is underway to predict the future based on past time series data through artificial intelligence-based analysis. However, even if the stock market is hit for a short period of time by the forecasting system, the market will no longer move according to the short-term strategy, and it will have to change anew. Therefore, this model monitored Samsung Electronics' stock data and news information through text mining, and presented a predictable model by showing the analyzed results.

Sensor nodes play a major role in IoT environment, and each sensor is a peer to peer networking. Due to limited physical size, IoT sensor nodes must have light-weight authentication protocol. The Internet of Things (IoT) is a collection of various technical elements. It is expected that interworking between heterogeneous terminals, networks, and applications. They will accelerate through the liberalization of the IoT platform. As a result, many technical and administrative security threats will arise in the IoT environment. Sensor node protocols must be light-weight and secure. As IoT devices are used for various purposes, for some devices that require performance, the OS with a high-performance chipset that works, most passwords protocol. However, to turn on / off the lights IoT devices that perform simple tasks such as based on a low-performance chipset with no OS running. If it does not support encryption protocol or certificate, then it is vulnerable, and it does not have enough performance to handle. Therefore, in this paper, Block-chain-based IoT device is proposed to get a more secure authentication scheme.

At present, it is indispensable to utilize data as an information society. Therefore, the database is used to manage large amounts of data. In real life, most of the data in a database is the personal information of a group of members. Because personal information is sensitive data, the role of the database administrator who manages personal information is important. However, there is a growing number of attacks on databases to use this personal information in a malicious way. SQL Injection is one of the most known and old hacking techniques. SQL Injection attacks are known as an easy technique, but countermeasures are easy, but a lot of efforts are made to avoid SQL attacks on web pages that require a lot of logins, but some sites are still vulnerable to SQL attacks. Therefore, this study suggests effective defense measures through analysis of SQL hacking technology cases and contributes to preventing web hacking and providing a secure information communication environment.