Journal of Computer Science and Cybernetics最新文献_第3页

SAFE: EFFICIENT DDOS ATTACK DEFENSE WITH ELASTIC TRAFFIC FLOW INSPECTION IN SDN-BASED DATA CENTERS 安全:基于sdn的数据中心，通过弹性流量检测，有效防范ddos攻击

Journal of Computer Science and Cybernetics Pub Date : 2023-03-03 DOI: 10.15625/1813-9663/16629

Tri Gia Nguyen, Hai Hoang Nguyen, Trung V. Phan

{"title":"SAFE: EFFICIENT DDOS ATTACK DEFENSE WITH ELASTIC TRAFFIC FLOW INSPECTION IN SDN-BASED DATA CENTERS","authors":"Tri Gia Nguyen, Hai Hoang Nguyen, Trung V. Phan","doi":"10.15625/1813-9663/16629","DOIUrl":"https://doi.org/10.15625/1813-9663/16629","url":null,"abstract":"In this paper, we propose an efficient distributed denial-of-Service (DDoS) Attack deFEnse solution, namely SAFE, which utilizes an elastic traffic flow inspection mechanism, for Software-Defined Networking (SDN) based data centers. In particular, we first examine a leaf-spine SDN-based data center network, which is highly vulnerable to volumetric DDoS attacks. Next, we develop a rank-based anomaly detection algorithm to recognize anomalies in the amount of incoming traffic. Then, for the traffic flow inspection, we introduce a component called DFI (Deep Flow Inspection) running an Open vSwitch (OvS) that can be dynamically initiated (as a virtual machine) on-demand to collect traffic flow statistics. By utilizing deep reinforcement learning-based traffic monitoring from our previous study, the DFIs can be protected from the flow-table overflow problem while providing more detailed traffic flow information. Afterward, a machine learning-based attack detector analyzes the gathered flow rule statistics to identify the attack, and appropriate policies are implemented if an attack is recognized. The experiment results show that the SAFE can effectively defend against volumetric DDoS attacks while assuring a reliable Quality-of-Service level for benign traffic flows in SDN-based data center networks. Specifically, for TCP SYN and UDP floods, the SAFE attack detection performance is improved by approximately 40% and 30%, respectively, compared to the existing SATA solution. Furthermore, the attack mitigation performance, the ratio of dropped malicious packets obtained by the SAFE is superior by approximately 48% (for TCP SYN flood) and 52% (for UDP flood) to the SATA.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88914757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PARALLEL FUZZY FREQUENT ITEMSET MINING USING CELLULAR AUTOMATA 基于元胞自动机的并行模糊频繁项集挖掘

Journal of Computer Science and Cybernetics Pub Date : 2023-01-13 DOI: 10.15625/1813-9663/38/4/17462

T. T. Tran, T. T. Nguyen, Giang Nguyen, Chau N. Truong

引用次数: 0

TWO-PHASE COMBINED MODEL TO IMPROVE THE ACCURACY OF INDOOR LOCATION FINGERPRINTING 两相组合模型提高室内位置指纹识别精度

Journal of Computer Science and Cybernetics Pub Date : 2022-12-26 DOI: 10.15625/1813-9663/38/4/17592

Van-Hieu Vu, Binh Ngo-Van, Tung Hoang Do Thanh

{"title":"TWO-PHASE COMBINED MODEL TO IMPROVE THE ACCURACY OF INDOOR LOCATION FINGERPRINTING","authors":"Van-Hieu Vu, Binh Ngo-Van, Tung Hoang Do Thanh","doi":"10.15625/1813-9663/38/4/17592","DOIUrl":"https://doi.org/10.15625/1813-9663/38/4/17592","url":null,"abstract":"Wi-Fi Fingerprinting based Indoor Positioning System (IPS) aims to help locate and navigate users inside buildings. It has become a popular research topic in recent years. For the most parts, authors use the traditional machine learning algorithms to enhance the accuracy of locationing. Their methods involve using a standalone algorithm or a combination of different algorithms in only one phase, producing results with an acceptable accuracy. In this paper, we present a different approach applying a machine learning model that combines many algorithms in two phases, and propose a feature reduction method. Specifically, our research is focused on the combination of different regression and classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machines (SVM), Random Forest (RF), Extra Tree Regressor (extraTree), Light Gradient Boosting Machine (LGBM), Logistic Regression (LR) and Linear Regression (LiR) to create a new data set and models that can be used in the training phase. These proposed models are tested on the UJIIndoorLoc 1 dataset. Our experimental results show a prediction accuracy of 98.73% by floor, and an estimated accuracy of 99.62% and 99.52% respectively by longitude and latitude. When compared with the results of the models in which we use independent algorithms, and of other researches that have different models using the same algorithms and on the same dataset, most of our results are better.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75431956","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AN IN-DEPTH EVALUATION OF FREQUENCY-AWARE SCHEDULER FOR IMPROVING USER EXPERIENCE ON MOBILE DEVICES 深入评估频率感知调度器，以改善移动设备上的用户体验

Journal of Computer Science and Cybernetics Pub Date : 2022-12-22 DOI: 10.15625/1813-9663/38/4/16873

Giang Son Tran, A. Carlier, D. Hagimont

{"title":"AN IN-DEPTH EVALUATION OF FREQUENCY-AWARE SCHEDULER FOR IMPROVING USER EXPERIENCE ON MOBILE DEVICES","authors":"Giang Son Tran, A. Carlier, D. Hagimont","doi":"10.15625/1813-9663/38/4/16873","DOIUrl":"https://doi.org/10.15625/1813-9663/38/4/16873","url":null,"abstract":"Mobile devices are more and more invading our daily life. Users of such devices expect to have a good experience, which is mainly linked with performance. However, higher performance also means a reduction in battery life, negatively contributing to the overall user experience. A common way to balance this performance-battery trade-off is to reduce CPU frequency when underload with Dynamic Voltage and Frequency Scaling. In our previous work, we introduced a Frequency-Aware Completely Fair Scheduler (called FA-CFS), which helps in reducing battery consumption and increasing the smoothness of mobile interface browsing. However, the current evaluation of FA-CFS model is only at quantitative results of power consumption rather than user experience on using their mobile device. In this paper, we perform an in-depth evaluation of the FA-CFS model, both quantitative results for system performance evaluation and qualitative results for user experience on mobile device usage. The experiments show that FA-CFS can reduce the rate of interface frame time peaks by up to 40% in terms of quantitative results and obtains a quantifiable impact on the quality of user experience with a quicker, more responsive interface.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90174323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FAST COMPUTATION OF DIRECT EXPONENTIATION TO SPEED UP IMPLEMENTATION OF DYNAMIC BLOCK CIPHERS 直接幂的快速计算，加快动态分组密码的实现

Journal of Computer Science and Cybernetics Pub Date : 2022-12-22 DOI: 10.15625/1813-9663/38/4/17226

Luong Tran Thi

引用次数: 0

EVOLUTIONARY ALGORITHM FOR TASK OFFLOADING IN VEHICULAR FOG COMPUTING 车辆雾计算中任务卸载的进化算法

Journal of Computer Science and Cybernetics Pub Date : 2022-12-22 DOI: 10.15625/1813-9663/38/3/17012

D. Son, Vu Tri An, H. Vo, Pham Vu Minh, Nguyễn Quang Phúc, Nguyen Phi Le, B. Nguyen, Huynh Thi Thanh Binh

{"title":"EVOLUTIONARY ALGORITHM FOR TASK OFFLOADING IN VEHICULAR FOG COMPUTING","authors":"D. Son, Vu Tri An, H. Vo, Pham Vu Minh, Nguyễn Quang Phúc, Nguyen Phi Le, B. Nguyen, Huynh Thi Thanh Binh","doi":"10.15625/1813-9663/38/3/17012","DOIUrl":"https://doi.org/10.15625/1813-9663/38/3/17012","url":null,"abstract":"Internet of Things technology was introduced to allow many physical devices to connect over the Internet. The data and tasks generated by these devices put pressure on the traditional cloud due to high resource and latency demand. Vehicular Fog Computing (VFC) is a concept that utilizes the computational resources integrated into the vehicles to support the processing of end-user-generated tasks. This research first proposes a bag of tasks offloading framework that allows vehicles to handle multiple tasks and any given time step. We then implement an evolution-based algorithm called Time-Cost-aware Task-Node Mapping (TCaTNM) to optimize completion time and operating costs simultaneously. The proposed algorithm is evaluated on datasets of different tasks and computing node sizes. The results show that our scheduling algorithm can save more than $60%$ of monetary cost than the Particle Swarm Optimization (PSO) algorithm with competitive computation time. Further evaluations also show that our algorithm has a much faster learning rate and can scale its performance as the number of tasks and computing nodes increases.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81314401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EMPIRICAL STUDY OF FEATURE EXTRACTION APPROACHES FOR IMAGE CAPTIONING IN VIETNAMESE 越南语图像字幕特征提取方法的实证研究

Journal of Computer Science and Cybernetics Pub Date : 2022-12-22 DOI: 10.15625/1813-9663/38/4/17548

Khang Nguyen

引用次数: 0

A HYBRID PSO-SA SCHEME FOR IMPROVING ACCURACY OF FUZZY TIME SERIES FORECASTING MODELS 一种提高模糊时间序列预测模型精度的混合pso-sa方案

Journal of Computer Science and Cybernetics Pub Date : 2022-09-22 DOI: 10.15625/1813-9663/38/3/17424

Phạm Đình Phong, Nguyen Duc Du, Phạm Hoàng Hiệp, Trần Xuân Thành

引用次数: 1

AN EFFECTIVE ALGORITHM FOR COMPUTING REDUCTS IN DECISION TABLES 一种计算决策表约简的有效算法

Journal of Computer Science and Cybernetics Pub Date : 2022-09-22 DOI: 10.15625/1813-9663/38/3/17450

Do Si Truong, Lam Thanh Hien, N. Thanh Tung

{"title":"AN EFFECTIVE ALGORITHM FOR COMPUTING REDUCTS IN DECISION TABLES","authors":"Do Si Truong, Lam Thanh Hien, N. Thanh Tung","doi":"10.15625/1813-9663/38/3/17450","DOIUrl":"https://doi.org/10.15625/1813-9663/38/3/17450","url":null,"abstract":"Attribute reduction is one important part researched in rough set theory. A reduct from a decision table is a minimal subset of the conditional attributes which provide the same information for classification purposes as the entire set of available attributes. The classification task for the high dimensional decision table could be solved faster if a reduct, instead of the original whole set of attributes, is used. In this paper, we propose a reduct computing algorithm using attribute clustering. The proposed algorithm works in three main stages. In the first stage, irrelevant attributes are eliminated. In the second stage relevant attributes are divided into appropriately selected number of clusters by Partitioning Around Medoids (PAM) clustering method integrated with a special metric in attribute space which is the normalized variation of information. In the third stage, the representative attribute from each cluster is selected that is the most class-related. The selected attributes form the approximate reduct. The proposed algorithm is implemented and experimented. The experimental results show that the proposed algorithm is capable of computing approximate reduct with small size and high classification accuracy, when the number of clusters used to group the attributes is appropriately selected.","PeriodicalId":15444,"journal":{"name":"Journal of Computer Science and Cybernetics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88396764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

REVISITING SOME FUZZY ALGEBRAIC STRUCTURES 重访一些模糊代数结构

Journal of Computer Science and Cybernetics Pub Date : 2022-09-22 DOI: 10.15625/1813-9663/38/3/17039

R. Kellil

引用次数: 0