Earticle

There are some definitions of cloud computing but it can be defined as utilization of personal computers, servers and softwares in one cluster for approaches to the use of shared computing resources. Nowadays the applications of cloud computing are rapidly increasing because of its merits on economic aspect, connectivity convenience, storage space and so on. The main objective of this paper is to find an effective methodology as an initial stage for applications of cloud computing in a real life. Therefore this paper addresses worldwide reviews on cloud computing.

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

This paper introduces a Deep Reinforcement Learning (DRL)-based framework to enhance Network Function Parallelism (NFP) in Mobile Edge Computing (MEC). Leveraging Network Function Virtualization (NFV), the proposed framework optimizes service delay by solving a fairness-aware throughput maximization problem for service function chain placement. It aims to maximize the long-term cumulative reward while satisfying Quality of Service (QoS) requirements. The framework also preserves resources for future requests by efficiently managing the initialized network functions distribution. Simulation results demonstrate the superior performance of the proposed framework across various metrics. Specifically, our framework improves the average delay and deployment rate by 1.2% and 2.4% compared to the existing best method.

Intrusion detection acts as a vital function in providing information security, and additionally the key technology is to precisely classify diverse attacks. Intrusion detection system (IDS) is identified as an important security issue within the cloud network environment. In this paper, IDS is given based on an innovative optimized custom RC-NN (Recurrent Convolutional Neural Network) which is proposed for intrusion detection along with the Ant Lion optimization algorithm. By this method, CNN (Convolutional Neural Network) is made hybrid with LSTM (Long Short Term Memory). Thus, all the attacks identified with the network layer of cloud are classified efficiently. The experimental results shown below describe the presentation of the IDS classification model with high accuracy, thus improving the detection rate or error rate. The optimized custom RC-NN-IDS model thus achieved an improved classification accuracy of 94% and also a decreased error rate of 0.0012. Additionally true positive rate, true negative rate and precision are considered as performance metrics. The proposed approach is evaluated using the DARPA IDS evaluation Data Sets and CSE-CIC-IDS2018 dataset and is compared with some existing approaches.

In a cloud computing environment, frequent data interactions require high information security. This paper briefly overviews encrypted data storage in the cloud computing environment. A dual encryption scheme that combined identity attributes and security devices was proposed. The scheme was evaluated through simulation experiments conducted on a laboratory server. The results indicated that an increase in the size of the storage file led to an increase in the time overhead of the encryption scheme. Furthermore, the dual encryption scheme exhibited slightly higher time overhead than a single security device encryption scheme, but was similar to a single identity attribute-based encryption scheme. For cloud storage data, the successful queries of encrypted data and effective resistance against third-party attacks were only achieved when the number of overlapping identity attributes was 5, which was the same as the storage user.

This paper investigates QoS-aware computation offloading issues for mobile edge computing in the 6G network. To minimize the end-to-end delay, we harness the Information-Centric Network (ICN) to ensure resource-constrained mobile user offloading computation-sensitive tasks in a distributed manner. Then, a two-stage approach based on a Multi-Agent Reinforcement Learning (MARL) algorithm entwined with optimization-embedding offloading ratio is proposed to enhance server selection for load balancing. Numeral results demonstrate that, with reference to a workshop-scale scenario, the proposed method can achieve outperformed performance in reducing delay and balancing loads on edge servers than the other four baseline schemes.

To address the issue of class imbalance in network traffic data, which affects the network intrusion detection performance, a combined framework using transformers is proposed. First, Tomek Links, SMOTE, and WGAN are used to preprocess the data to solve the class-imbalance problem. Second, the transformer is used to encode traffic data to extract the correlation between network traffic. Finally, a hybrid deep learning network model combining a bidirectional gated current unit and deep neural network is proposed, which is used to extract long-dependence features. A DNN is used to extract deep level features, and softmax is used to complete classification. Experiments were conducted on the NSLKDD, UNSWNB15, and CICIDS2017 datasets, and the detection accuracy rates of the proposed model were 99.72%, 84.86%, and 99.89% on three datasets, respectively. Compared with other relatively new deep-learning network models, it effectively improved the intrusion detection performance, thereby improving the communication security of network data.

This paper proposes high-speed computing decoders for polar codes based on a neural network. To compensate for the performance gap with the successive cancellation decoder, we propose applying the recurrent neural network to the BP decoder with a modified factor graph to reduce the computational complexity of the decoder without any performance degradation. The results of the performance simulation conducted in this paper reveal that the proposed decoder requires substantially less computational complexity than the conventional decoders to achieve the same bit error rate performance.

Conventional split computing approaches for AI models that generate large outputs suffer from long transmission and inference times. Due to the limited resources of the edge server and selfish MDs, some MDs cannot offload their tasks and sacrifice their performance. To address these issues, we formulate an optimization problem to determine one or two split points that minimize inference latency while ensuring fair offloading among MDs. Additionally, we devise a low-complexity heuristic algorithm called fast and fair split computing (F2SC). Evaluation results demonstrate that F2SC reduces inference time by 3.8%~20.1% compared to the conventional approaches while maintaining fairness.

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

One of the latest emerging technologies is artificial intelligence, which makes the machine mimic human behaviour. The most important component used to detect cyber attacks or malicious activities is the intrusion detection system (IDS). Artificial intelligence plays a vital role in detecting intrusions and widely considered as the better way in adapting and building IDS. In modern days, neural network algorithms are emerging as a new artificial intelligence technique that can be applied to real-time problems. The proposed system is to detect a classification of botnet attack which poses a serious threat to financial sectors and banking services. The proposed system is created by applying artificial intelligence on a realistic cyber defence dataset (CSE-CIC-IDS2018), the latest IDS Dataset in 2018 by Canadian Institute for Cybersecurity (CIC) on AWS (Amazon Web Services). The proposed system of Artificial Neural Networks provides an outstanding performance of Accuracy score is 99.97% and an average area under ROC(Receiver Operator Characteristic) curve is 0.999 and an average False Positive rate is a mere value of 0.03. The proposed system of Artificial Intelligence-based Intrusion detection of botnet attack classification is powerful, more accurate and precise. The novel proposed system can be applied to conventional network traffic analysis, cyber-physical system traffic analysis and also can be applied to the real-time network traffic data analysis.

Our paramount task is to examine and detect network attacks, is one of the daunting tasks because the variety of attacks are day by day existing in colossal number. The program proposed detects botnet attacks using the newest CSE-CIC-IDS2018 cyber dataset published by the Canadian Cybersecurity Establishment (CIC). The cyber dataset can be accessed on AWS (Amazon Web Services). The realistic network dataset consists of all the modern and existing attacks such as Brute-force attacks and password cracking, Heartbleed, Botnet, DoS (Denial of Service), DDoS also known as Distributed Denial of Service, Web attacks i.e. vulnerable web app attacks, and infiltration of the network from inside. The objective of the proposed research is to identify a classification of Botnet attacks. Botnet attack is a Trojan Horse malware attack that poses a serious security threat to the banking and financial sectors. Since a specific classifier could possibly work for such datasets it is crucial to finish a comparative examination of classifiers in order to achieve the most noteworthy execution in such basic detection of network attacks. The proposed framework is to incorporate different classifier methods such as KNearset Neighbor classifier, Naive Bayes, Adaboost with Decision Tree, Support Vector Machine classifier, Random Forest classifier, and Artificial Intelligence to distinguish a portrayal of botnet attacks on the recent and realistic cyber dataset CSE-CIC-IDS2018. The results of the classification are given as precise precision for the specific classifiers. And furthermore, the proposed framework uses the Calibration curve as a standard approach in analytical methods which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not. Finally, the displayed graph proves how well the artificial intelligence technique outperforms all other classifiers which generates reliability diagrams to check the predicted probabilities of various classifiers are well-calibrated or not.

소셜 네트워크는 이용자들의 공통적 관심 분야를 기반으로 서로 연결된 개별적 온라인 서비스 구조이다. 소셜 네트워킹 웹사이트의 사용은 온라인 커뮤니케이션과 데이터 공유 및 상호작용 방법을 혁신적으로 바꿔놓았다. 소셜 네트워킹 사이트의 사용량이 증가함에 따라 막대한 양의 사용자 상호작용 데이터가 매일 축적되고 있어 다양한 분야에서의 적용을 위한 대량의 소셜 네트워크 데이터 마이닝 및 분석 방안이 갈수록 주목받고 있다. OLAP데이터 분석은 효과적으로 소셜 데이터를 분석하기 위하여 사용될 수 있는 유용한 데이터 분석방법이지만 계속적으로 가변하는 대량 데이터와 OLAP쿼리의 복잡성 때문에 작업 시간이 오래걸리는 문제점이 있다. 본 논문은 대량의 소셜 네트워크 데이터 분석에 적합한 클라우드 컴퓨팅의 발전이 이루어지면서 효율적이고 빠른 데이터 분석을 위하여 클라우드 컴퓨팅 플랫폼을 사용하는 OLAP기반 소셜 데이터 분석도구에 대해 설명한다.

A social network is an online structure of individuals which are related to each other based on some common relationship of interest. The usage of social networking websites has literally revolutionized the methods of online communication, data sharing and interaction. As a result of the increased usage of social networking sites a massive amount of user interaction data is been generated daily. The mining and analysis of this large amount of social network data for a variety of applications is getting popularity. OLAP analysis is a famous data analysis method which can be used to effectively analyze social data. However, because of the huge amount of continuously changing data and the complexity of OLAP queries, this data analysis in a time consuming task. The advances in cloud computing motivated us to use the cost effective cloud computing infrastructure for the task of analyzing large amount of social network data. This paper presents an OLAP based social data analysis tool which uses cloud computing platform for efficient and timely analysis of data. The usage of cloud computing infrastructure results in reduction of computational cost, device and location independence, and an increase in peak-load capacity.

Existing simulators for performance analysis of resource management techniques in edge computing have a limitation: they lack horizontal management features such as inter-server clustering and container registry placement. To address this issue, this paper proposes EdgeNet, a new simulator specialized for modeling of network overhead and server clustering algorithm in edge computing environments. EdgeNet provides a Python library that can be used to develop leader election algorithms for clustered server groups, facilitating research into horizontal resource management approaches that were previously difficult to study.

유비쿼터스 분야에서는 다양한 형태의 P2P 시스템을 분산환경을 위하여 연구하고 있다. 분산해쉬테이블(DHT)기반의 P2P 시스템은 부하조절을 통한 효율적 기법으로 제시되고 있는 반면 이동성과 근거리 기반의 자원 활용을 보장하지는 못하는 문제점을 가지고 있다. 본 연구에서는 이를 극복하기 위하여 이동상황에서의 근거리 기반 P2P 시스템 (MLH-Net)을 제안한다. 이는 이동성에 기반하여 두 개의 계층으로 이루어져 있다. 상위 계층의 경우 super node를 통한 전체적인 관리를 담당하며, 하위 계층의 경우 일반 노드의 망으로 구성되어 있다. 제시하는 방법을 종래의 JXTA 및 Chord 와 비교 실험 한 결과 node의 발견 시 메시지 이동 hop은 JXTA 대비 13% 및 Chord 대비 69% 감소되었으며, 네트워크 거리의 경우도 각각 17% 및 83% 감소되는 효과를 확인 하였다.

Many peer-to-peer (p2p) systems have been studied in distributed, ubiquitous computing environments. Distributed hash table (DHT)-based p2p systems can improve load-balancing even though locality utilization and user mobility are not guaranteed. We propose a mobile locality-based hierarchical p2p overlay network (MLH-Net) to address locality problems without any other services. MLH-Net utilizes mobility features in a mobile environment. MLH-Net is constructed as two layers, an upper layer formed with super-nodes and a lower layer formed with normal-nodes. The simulation results demonstrate that MLH-Net can decrease discovery routing hops by 13% compared with JXTA and 69% compared with Chord. It can decrease the discovery routing distance by 17% compared with JXTA and 83% compared with Chord depending on the environment.

Purpose: The purpose of this study was to develop a home care nursing network system for operating home care effectively and efficiently by utilizing a wire-wireless network and mobile computing in order to record and send patients' data in real time, and by combining the headquarter office and the local offices with home care nurses over the Internet. It complements the preceding research from 1999 by adding home care nursing standard guidelines and upgrading the PDA program. Method: Method/l and Prototyping were adopted to develop the main network system. Result: The detailed research process is as follows: 1 )home care nursing standard guidelines for Diabetes, cancer and peritoneal-dialysis were added in 12 domains of nursing problem fields with nursing assessment/intervention algorithms. 2) complementing the PDA program was done by omitting and integrating the home care nursing algorhythm path which is unnecessary and duplicated. Also, upgrading the PDA system was done by utilizing the machinery and tools where the PDA and the data transmission modem are integrated, CDMX-1X base construction, in order to reduce a transmission error or transmission failure.

본 연구는 유비쿼터스 환경에서 네트워크 중심전(NCW)을 원활하게 실시하기 위해 필요한 정 보보호 대책을 제시하기 위한 것이다. 대책을 도출하기 위해 우선 유비쿼터스 환경에서 NCW가 구현 되었을 때 예상되는 전투양상을 전장기능별로 조명해보고, 유비쿼터스 환경에서 일어날 수 있는 정보보호 위협의 특징을 도출하였다. 이러한 특징을 바탕으로 위협을 해결하기 위한 국방 정보보호 대응 방안을 제시하였다. 본 연구에서 제시한 대응방안을 바탕으로 추후 세부 실행과 제를 도출한다면 보다 완벽한 NCW 개념 실현을 위한 국방정보보호 대책이 마련될 수 있을 것이다.

Information security is a critical issue for network centric warfare(NCW). This paper provides defense information security guidelines for NCW, especially for ubiquitous network computing environment. For this purpose, this paper identified changes of battle aspect of tactical level and characteristics of information threats, and finally, the research suggested several information security guidelines for NCW. This paper is to intended to help military organization’s planners determine practical and implemental plans in the near future.

사람이 늘 입고 다니는 웨어러블 컴퓨터에서 착용 시의 이물감과 활동 시의 불편함은 해결되어야 하는 중요한 문제이다. 웨어러블 컴퓨터의 또 다른 중요한 조건인 저전력 소모를 만족시키면서 이물감과 불편함을 최대한 감소시키기 위하여, 본 논문에서는 전도성 섬유와 Woven Inductor로 구성된 유무선 통합 네트워크를 제안하였다. 제안된 채널은 겉옷과 속옷의 통신에만 적은 에너지를 소모하는 유도 결합을 기반으로 한 근거리 무선 통신을 사용하고, 겉옷 내에서는 의복 내에 완벽한 일체가 가능한 전도성 섬유를 통한 유선 통신을 사용한다. 제안된 채널을 이용하여 체온 측정이 가능한 센서 노드와 센서 컨트롤러를 연결하는 웨어러블 컴퓨팅 네트워크를 구현하였으며, 네트워크의 성공적인 동작을 검증하였다.

In the wearable computer that people are always wearing on their body, the feeling of heterogeneity for wearing and the inconvenience for acting are important problems that must be solved. In this paper, to reduce the feeling of heterogeneity and the inconvenience and to consume low power, which is the other important condition for wearable computer, at the same time, the combined network of wireless and wire-line communication is proposed which consists of conductive yarn and a pair of Woven Inductor. In the proposed channel, the wireless communication is used only between inner and outer wear, and this wireless communication is based on the inductive coupling which consumes low energy. Also the wire-line communication is used in the outer wear through the conductive yarn which can be integrated into the clothes perfectly. By using the proposed channel, the wearable computing network of temperature sensor nodes and the sensor controller is implemented, and the overall network is successfully demonstrated.

5G 네트워크의 핵심 기술로 모바일 에지 컴퓨팅(Mobile Edge Computing, MEC)이 주목받음에 따라, 모바일 사용자의 데이터를 기반으로 한 5G 네트워크 기반 에지 AI 기술이 최근 다양한 분야에서 이용되고 있다. 하지만, 전통적인 인공지능 보안에서와 마찬가지로, 에지 AI 핵심 기능을 담당하는 코어망 내 표준 5G 네트워크 기능들에 대한 적대적 교란이 발생할 가능성이 존재한다. 더불어, 3GPP에서 정의한 5G 표준 내 Standalone 모드의MEC 환경에서 발생할 수 있는 데이터 오염 공격은 기존 LTE망 대비 현재 연구가 미비한 실정이다. 본연구에서는 5G에서 에지 AI의 핵심 기능을 담당하는 네트워크 기능인 NWDAF를 활용하는 MEC 환경에 대한 위협 모델을 탐구하고, 일부 개념 증명으로써 Leaf NWDAF에 대한 데이터 오염 공격 탐지 성능을 향상시키기 위한 특징 선택 방법을 제안한다. 제안한 방법론을 통해, NWDAF에서의 Slowloris 공격 기반 데이터 오염 공격에 대해 최대 94.9%의 탐지율을 달성하였다.

As mobile edge computing (MEC) is gaining attention as a core technology of 5G networks, edge AI technology of 5G network environment based on mobile user data is recently being used in various fields. However, as in traditional AI security, there is a possibility of adversarial interference of standard 5G network functions within the core network responsible for edge AI core functions. In addition, research on data poisoning attacks that can occur in the MEC environment of standalone mode defined in 5G standards by 3GPP is currently insufficient compared to existing LTE networks. In this study, we explore the threat model for the MEC environment using NWDAF, a network function that is responsible for the core function of edge AI in 5G, and propose a feature selection method to improve the performance of detecting data poisoning attacks for Leaf NWDAF as some proof of concept. Through the proposed methodology, we achieved a maximum detection rate of 94.9% for Slowloris attack-based data poisoning attacks in NWDAF.