Software-defined networking (SDN) is becoming the standard for the management of networks due to its scalability and flexibility to program the network. SDN provides many advantages but it also involves some specific security problems; for example, the controller can be taken down using cyber attacks, which can result in the whole network shutting down, creating a single point of failure. In this paper, DDoS attacks in SDN are detected using AI-enabled machine and deep learning models with some specific features for a dataset under normal DDoS traffic. In our approach, the initial dataset is collected from 84 features on Kaggle and then the 20 top features are selected using a permutation importance algorithm. The dataset is learned and tested with five AI-enabled models. Our experimental results show that the use of a machine learning-based random forest model achieves the highest accuracy rate of 99.97% in DDoS attack detection in SDN. Our contributions through this study are, firstly, that we found the top 20 features that contributed to DDoS attacks. Secondly, we reduce the time and cost of comparing various learning models and their performance in determining a learning model suitable for DDoS detection. Finally, various experimental methods to evaluate the performance of the learning model are presented so that related researchers can utilize them.

This study explores the impact of digital transformation on Korean society by analyzing Korean social media data, focusing on the societal and economic effects triggered by advancements in digital technology. Utilizing text mining techniques and semantic network analysis, we extracted key terms and their relationships from online news and blogs, identifying major themes related to digital transformation. Our analysis, based on data collected from major Korean portals using various related search terms, provides deep insights into how digital evolution influences individuals, businesses, and government sectors. The findings offer a comprehensive view of the technological and social trends emerging from digital transformation, including its policy, economic, and educational implications. This research not only sheds light on the understanding and strategic approaches to digital transformation in Korea but also demonstrates the potential of social media data in analyzing the societal impact of technological advancements, offering valuable resources for future research in effectively navigating the era of digital change.

Software Defined Networking (SDN) is positioning the standard for the management of networks due to its scalability and flexibility to program the network. The SDN provides many advantages but it also involves some specific security problems take down the controller using cyber attack and in result the whole network will shut down which makes it a single point of failure. In this paper, the DDoS attacks in SDN were detected using AI-enabled, machine and deep learning, models with some specific features for data-set under normal and DDoS traffic. In our approach, initial data-set is collected from 84 features on kaggle and then the 20 top most features are selected using permutation importance algorithm. The data-set were learned and tested with AI-enabled 5 models. Our experimental results showed that the use of machine learning based random forest model has achieved the highest accuracy rate of 99.97%, in DDoS attack detection in SDN. Our contributions through this study are, first, we found highest 20 attacks that absolutely contributed to DDoS attacks. Secondly, it can reduce the time and cost of comparing various learning models and performance required for determining a learning model suitable for DDoS detection. Finally, various experimental methods for evaluating the performance of the learning model are presented so that related researchers can utilize them.

정규표현식 패턴 매칭은 네트워크 침입 탐지 시스템(NIDS) 등의 응용 프로그램에서 널리 사용되며 시간 제약이 있어 고성능 처리가 필요한 경우에 하드웨어 기반 패턴 매칭이 사용된다. 패턴이 업데이트될 때마다 재합성 해야 하는 하드웨어 기반 방식의 문제점을 해결하여 프로세서 기반 정규표현식 매칭 프로세서인 ReCPU, SMPU, REMP가 제안되었다. 그러나 이 프로세서 기반 정규표현식 매칭 프로세서들은 정규표현식의 반복 연산을 비효율적으로 처리한다. 본 논문에서는 ReCPU, SMPU의 비효율적인 반복 연산을 개선하기 위해 새로운 명령어 세트를 제시한다. REMP의 명령어 세트를 기반으로 하는 효율적인 반복 연산이 가능한 정규표현식 매칭 프로세서 REMPr을 제안한다. REMPr은 특히 짧은 서브 패턴을 OR의 반복 연산으로 처리하는 비효율적인 방법을 개선하고, 단일 명령어로 처리할 수 있도록 하였다. 또한 다운 카운터와 카운터 스택을 사용하여 제약 및 중첩 반복 연산도 효율적으로 처리하도록 하였다. REMPr은 Verilog로 설계하고 Intel Stratix IV FPGA로 합성하여 동작을 검증하였다.