scholarly journals A Novel Dynamic Method in Distributed Network Attack-Defense Game

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Liu Xiaojian ◽  
Yuan Yuyu
Keyword(s):  
Information Acquisition ◽  
Dynamic Method ◽  
Dynamic Game ◽  
Evolutionary Process ◽  
Target System ◽  
Distributed Network ◽  
Network Attack

We analyze the distributed network attack-defense game scenarios, and we find that attackers and defenders have different information acquisition abilities since the ownership of the target system. Correspondingly, they will have different initiative and reaction in the game. Based on that, we propose a novel dynamic game method for distributed network attack-defense game. The method takes advantage of defenders’ information superiority and attackers’ imitation behaviors and induces attackers’ reaction evolutionary process in the game to gain more defense payoffs. Experiments show that our method can achieve relatively more average defense payoffs than previous work.

scholarly journals Research on Coordinated Development of a Railway Freight Collection and Distribution System Based on an “Entropy-TOPSIS Coupling Development Degree Model” Integrated with Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Yun Jing ◽  
Si-Ye Guo ◽  
Xuan Wang ◽  
Fang-Qiu Chen
Keyword(s):  
Machine Learning ◽  
Distribution System ◽  
Information Acquisition ◽  
High Speed ◽  
Distribution Systems ◽  
Target System ◽  
Railway Transportation ◽  
Railway Freight ◽  
Transportation Capacity ◽  
Heavy Haul

In recent years, with the gradual networking of high-speed railways in China, the existing railway transportation capacity has been released. In order to improve transportation capacity, railway freight transportation enterprises companies have gradually shifted the transportation of goods from dedicated freight lines to passenger-cargo lines. In terms of the organization form of collection and distribution, China has a complete research system for heavy-haul railway collection and distribution, but the research on the integration of collection and distribution of the ordinary-speed railway freight has not been completed. This paper combines the theories of the integration of collection and distribution theory, coordination theory, and coupling theory and incorporates the machine learning fuzzy mathematics to construct an “Entropy-TOPSIS Coupling Development Degree Model” for dynamic intelligent quantitative analysis of the synergy of railway freight collection and distribution systems. Finally, we take the Tongchuan Depot of “China Railway Xi’an Group Co., Ltd.” as a research object to construct a target system and use the intelligent information acquisition system to collect basic data. The analysis results show that through the coordinated control of the freight collection and distribution system, the coordination between the subsystems of the integrated freight collection and distribution system is increased by 5.94%, which verifies the feasibility of the model in the quantitative improvement of the integration of collection and distribution system. It provides a new method for the research of integrated development of railway freight collection and distribution.

scholarly journals A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guomin Wu ◽  
Guoping Tan ◽  
Defu Jiang
Keyword(s):  
User Experience ◽  
Learning Algorithm ◽  
Dynamic Game ◽  
Research Field ◽  
Distributed Network ◽  
Intelligent Algorithm ◽  
Network Slicing ◽  
Two Factors ◽  
Multiple Network ◽  
Dynamic Game Model

Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.

scholarly journals DDosTC: A Transformer-Based Network Attack Detection Hybrid Mechanism in SDN

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5047
Author(s):  
Haomin Wang ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Denial Of Service ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Target System ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
Optimal Model ◽  
Network Attack ◽  
Average Accuracy

Software-defined networking (SDN) has emerged in recent years as a form of Internet architecture. Its scalability, dynamics, and programmability simplify the traditional Internet structure. This architecture realizes centralized management by separating the control plane and the data-forwarding plane of the network. However, due to this feature, SDN is more vulnerable to attacks than traditional networks and can cause the entire network to collapse. DDoS attacks, also known as distributed denial-of-service attacks, are the most aggressive of all attacks. These attacks generate many packets (or requests) and ultimately overwhelm the target system, causing it to crash. In this article, we designed a hybrid neural network DDosTC structure, combining efficient and scalable transformers and a convolutional neural network (CNN) to detect distributed denial-of-service (DDoS) attacks on SDN, tested on the latest dataset, CICDDoS2019. For better verification, several experiments were conducted by dividing the dataset and comparisons were made with the latest deep learning detection algorithm applied in the field of DDoS intrusion detection. The experimental results show that the average AUC of DDosTC is 2.52% higher than the current optimal model and that DDosTC is more successful than the current optimal model in terms of average accuracy, average recall, and F1 score.

scholarly journals Hybridizing Network Attack Detection and Prevention Via Integrated DADCQ Protocol

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1393-1395
Keyword(s):  
Denial Of Service ◽  
Distribution Channel ◽  
Attack Detection ◽  
Distributed Network ◽  
Distributed Environment ◽  
Major Purpose ◽  
Network Attack ◽  
Channel Quality ◽  
Node Distribution

Distribution adaptive distance with channel quality or DADCQ is a protocol used for checking the fitness of a node for communication by utilizing node specific parameters in a distributed environment. The major purpose of the protocol is to check if the node should be given preference in re-broadcasting when a lot of nodes are trying the communicate in the network, DADCQ does this by accessing the node distribution, channel quality and distance from the target nodes. In this paper, we modify the DADCQ protocol in order to detect and remove abnormalities like distributed denial of service (DDOS) and eves-dropping attacks in a distributed network. The attacked network is evaluated for pre and post application of the modified protocol and the Quality of Service (QoS) parameters are evaluated. It is observed that the proposed protocol improves the QoS and is successful in removal of the aforementioned attacks from the network.

Towards Systematization

1977 ◽  
Vol 16 (03) ◽  
pp. 125-130 ◽  
Author(s):  
P. L. Reichertz
Keyword(s):  
Clinical Medicine ◽  
Medical Science ◽  
User Involvement ◽  
Empirical Science ◽  
Target System ◽  
General Strategy ◽  
System Construction ◽  
Formal Approach ◽  
Methodology Development ◽  
Planning Development

Data processing has become an important tool in theoretical and clinical medicine. The main categories of applications are : information analysis, (bio)signal processing and the field of information logistics (information systems).The problems encountered lie in the discrepancy of the basic methods of a formal approach to an empirical science, the complexity of the target system and the system ecology, i.e. the involvement of the user and the system environment during system construction and utilization.Possible solutions to these problems are the application of system techniques, inductive planning, development of medical methodology, development of methods and techniques for user involvement and assessment of motivation and education and educational planning.The necessary general strategy in the development in medical informatics is seen in the continuing systematization of the theoretical and practical approach. It is estimated that this will eventually contribute to the systematization of medical science and practice.

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