The rapid expansion of online content distribution has led to a significant increase in copyright infringement, where unauthorized works are illegally shared through various web-based platforms. To fundamentally block these copyright-infringing websites, it is essential to accurately identify the IP address or physical location of the original server. However, most illegal content distribution sites utilize advanced security mechanisms, such as DNS resolvers, reverse proxies, and anonymization techniques, to conceal their true IP addresses, making direct tracking increasingly difficult. These evasive tactics allow illegal sites to continue operating while avoiding enforcement measures. To address this challenge, this paper proposes a keyword-based IP tracking method for identifying illegal web content distribution sites by leveraging port scanning on HTTP and HTTPS (ports 80 and 443). The proposed approach systematically detects and analyzes servers that provide unauthorized content by scanning network ports commonly used for web services. By correlating detected IP addresses with keyword-based filtering techniques, this method enables efficient tracking of illegal sites that actively hide their original server’s IP address. Through experimental validation, the proposed method successfully pinpoints the IP addresses of illegal content distribution servers, even when they employ obfuscation techniques to mask their identity. This study contributes to enhancing copyright protection by introducing a web-based detection approach that integrates network security techniques, web engineering principles, and automated keyword analysis. Furthermore, the findings provide a practical solution for law enforcement agencies, copyright holders, and regulatory bodies to combat illegal web content distribution more effectively.

In the field of book search, research on a web service-based user-customized book recommendation system is being conducted to respond to increasingly diverse user requirements. The collaborative filtering algorithm, which is mainly used for book recommendation, has a problem in that it is difficult to reflect the user’s recent interest without considering the changes in preference over time, and the user’s satisfaction decreases because it repeatedly recommends only similar items. In this paper, we propose a book recommendation method using category similarity based on deep learning. The proposed method is to predict books to be used next time by inputting users’ past and current book usage history through BERT, a natural language processing model, and to recommend popular books in other categories with high similarity to the predicted book category in the BERT model to reflect serendipity. This method reflects serendipity, which can lead to users’ recent interests and practical preferences, so that recommendation accuracy and user satisfaction can be satisfied at the same time.

With the proliferation of web-based content platforms, the distribution of illegally streamed videos poses a serious threat to the reliability of web applications and the integrity of content copyright protection systems. Traditional video identification methods typically require the processing of large-scale feature data, which hinders the real-time performance, lightweight nature, and scalability demanded by web environments. In this paper, we propose a method for identifying illegally streamed videos that is optimized for efficient operation within web systems. The proposed approach utilizes only the low-frequency components of the fast Fourier transform (FFT). By transforming video frames into the frequency domain and extracting the structurally significant low-frequency components, the method replaces high-dimensional feature data with more compact representations. This allows the system to maintain low computational complexity and fast response times, even in web application environments. Experimental results demonstrate that, compared to existing methods, the proposed technique achieves up to 93 times reduction in feature data size, a recognition rate of 98%, and an average response time of 1745 ms. From the perspective of web engineering, the proposed method holds strong potential as a real-time identification module in web-based copyright protection systems. It offers a balanced approach that satisfies both lightweight processing requirements and high accuracy.

With the full-fledged service of mobile carrier 5G networks, it is possible to use large-capacity, immersive content at high speed anytime, anywhere. It can be illegally distributed in web-hard and torrents through DRM dismantling and various transformation attacks; however, evaluation indicators that can objectively evaluate the filtering performance for copyright protection are required. Since applying existing 2D filtering techniques to immersive content directly is not possible, in this paper we propose a set of robustness indicators for immersive content. The proposed indicators modify and enlarge the existing 2D video robustness indicators to consider the projection and reproduction method, which are the characteristics of immersive content. A performance evaluation experiment has been carried out for a sample filtering system and it is verified that an excellent recognition rate of 95% or more is achieved in about 3 s of execution time.

The theme, background, and signal music usage history information consists of general information such as music information, platform information, and music usage information, and sensitive information such as rights management information, music usage permission range, and contract information. If sensitive information among these is disclosed, disputes such as trade secrets and infringement of personal information protection between companies or between companies and individuals may arise. We propose a selective encryption/decryption method to secure the confidentiality, integrity, reliability and non-repudiation of sensitive music usage history information used in the theme, background, and signal music blockchain environment. In the proposed method, a monitoring company encrypts sensitive information using a secret key for usage history information, which is combined with general information, and digitally signs it using a private key to register it in a block. A trust group can view and access the information at the time of inquiry by verifying the digital signature with the public key of the monitoring company and then can decrypt the sensitive information using the private key.