scholarly journals Fuzzy Theory Based Security Service Chaining for Sustainable Mobile-Edge Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Guanwen Li ◽  
Huachun Zhou ◽  
Bohao Feng ◽  
Guanglei Li ◽  
Taixin Li ◽  
...  
Keyword(s):  
Network Architecture ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Edge Computing ◽  
Optimal Order ◽  
Mobile Edge Computing ◽  
Inference System ◽  
Mobile Cellular ◽  
Security Service ◽  
Improved Performance

Mobile-Edge Computing (MEC) is a novel and sustainable network architecture that enables energy conservation with cloud computing and network services offloading at the edge of mobile cellular networks. However, how to efficiently manage various real-time changing security functions is an essential issue which hinders the future MEC development. To address this problem, we propose a fuzzy security service chaining approach for MEC. In particular, a new architecture is designed to decouple the required security functions with the physical resources. Based on this, we present a security proxy to support compatibility to traditional security functions. Furthermore, to find the optimal order of the required security functions, we establish a fuzzy inference system (FIS) based mechanism to achieve multiple optimal objectives. Much work has been done to implement a prototype, which is used to analyze the performance by comparing with a widely used method. The results prove that the proposed FIS mechanism achieves an improved performance in terms of Inverted Generational Distance (IGD) values and execution time with respect to the compared solution.

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

Improved ANFIS based MRAC observer for sensorless control of PMSM

2021 ◽  
pp. 1-13
Author(s):  
Suryakant ◽  
Mini Sreejeth ◽  
Madhusudan Singh
Keyword(s):  
Fuzzy Inference ◽  
Permanent Magnet Synchronous Motor ◽  
Torque Control ◽  
Control Algorithms ◽  
Motor Speed ◽  
Inference System ◽  
Rotor Position ◽  
Improved Performance ◽  
Incremental Encoder ◽  
Model Reference Adaptive

Detection of the rotor position is an important prerequisite for controlling the speed and developed torque in permanent magnet synchronous motor (PMSM). Even though use of incremental encoder and resolver is one of the popular schemes for sensing the rotor position in a PMSM drive, it increases the size and weight of the drive and reduces its reliability. Dynamic modeling of the motor and control algorithms are often used in sensor-less control of PMSM to estimate rotor position and motor speed. Most sensor-less control algorithms use machine parameters like torque constant, stator inductances and stator resistance for estimating the rotor position and speed. However, with accuracy of such estimation and the performance of the motor degrades with variation in motor parameters. Model reference adaptive control (MRAC) provides a simple solution to this issue. An improved Adaptive neuro-fuzzy inference system (ANFIS) based MRAC observer for speed control of PMSM drive is presented in this paper. In the proposed method adaptive model and adaptive mechanism are replaced by an improved ANFIS controller, which neutralize the effect of parametric variation and results in improved performance of the drive. The modeling equations of PMSM are used to estimate the rotor position for speed and torque control of the drive. Simulation studies have been carried out under various operating condition using MATLAB/Simulink. In addition, a comparative analysis of the conventional MRAC based observer and improved ANFIS based MRAC observer is carried out. It is observed that the proposed method results in better performance of the PMSM drive.

scholarly journals M-CFIS-R: Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 707 ◽  
Author(s):  
Tran Manh Tuan ◽  
Luong Thi Hong Lan ◽  
Shuo-Yan Chou ◽  
Tran Thi Ngan ◽  
Le Hoang Son ◽  
...  
Keyword(s):  
Decision Making ◽  
Similarity Measure ◽  
Fuzzy Inference System ◽  
Granular Computing ◽  
Fuzzy Inference ◽  
Fuzzy Theory ◽  
Rule Base ◽  
Inference System ◽  
Fuzzy Measures ◽  
Fuzzy Similarity

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

scholarly journals Review of the D2D Trusted Cooperative Mechanism in Mobile Edge Computing

Information ◽  
2019 ◽  
Vol 10 (8) ◽  
pp. 259 ◽  
Author(s):  
Jie Yuan ◽  
Erxia Li ◽  
Chaoqun Kang ◽  
Fangyuan Chang ◽  
Xiaoyong Li
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Network Architecture ◽  
Social Trust ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Internet Technologies ◽  
Architecture Model ◽  
Security Issues ◽  
Cooperative Relationship

Mobile edge computing (MEC) effectively integrates wireless network and Internet technologies and adds computing, storage, and processing functions to the edge of cellular networks. This new network architecture model can deliver services directly from the cloud to the very edge of the network while providing the best efficiency in mobile networks. However, due to the dynamic, open, and collaborative nature of MEC network environments, network security issues have become increasingly complex. Devices cannot easily ensure obtaining satisfactory and safe services because of the numerous, dynamic, and collaborative character of MEC devices and the lack of trust between devices. The trusted cooperative mechanism can help solve this problem. In this paper, we analyze the MEC network structure and device-to-device (D2D) trusted cooperative mechanism and their challenging issues and then discuss and compare different ways to establish the D2D trusted cooperative relationship in MEC, such as social trust, reputation, authentication techniques, and intrusion detection. All these ways focus on enhancing the efficiency, stability, and security of MEC services in presenting trustworthy services.

scholarly journals Risk Control Management of New Rural Cooperative Financial Organizations Based on Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Fanrong Kong ◽  
Hongxia Lu
Keyword(s):  
Cloud Computing ◽  
Mutual Fund ◽  
Network Architecture ◽  
Financial Institution ◽  
Risk Control ◽  
Edge Computing ◽  
Experimental Simulation ◽  
Mobile Edge Computing ◽  
Financial Organization ◽  
Rural Enterprises

Rural cooperative financial organization is a new type of cooperative financial organization in recent years. It is a community financial institution created by farmers and small rural enterprises to voluntarily invest in shares in order to meet the growing demand for rural financing. However, this financial organization has many flaws in the design of the system; it has not promoted the better development of rural mutual fund assistance. In addition, mobile edge computing (MEC) can be used as an effective supplement to mobile cloud computing and has been proposed. However, most of the current literature studies on cloud computing provide computing offload just to propose a network architecture, without modeling and solving to achieve. In this context, this paper focuses on the practical application of MEC in the risk control of new rural cooperative financial organizations. This paper proposes a collaborative LECC mechanism based on machine learning under the MEC architecture. The experimental simulation shows that the HR under the LECC mechanism is about 17%–23%, 46%–69%, and 93%–177% higher than that of LENC, LRU, and RR, respectively. It is unrealistic to want to rely on meager loan interest for long-term development. The most practical way is to increase the income level of the organization itself.

scholarly journals Methodology for constructing a neural fuzzy network in the field of information security

2021 ◽  
pp. 43-56
Author(s):  
Anastasiya Arkhipova ◽  
◽  
Pavel Polyakov ◽  
Keyword(s):  
Network Architecture ◽  
Fuzzy Inference ◽  
Activation Function ◽  
Fuzzy Rules ◽  
Sql Injection ◽  
Inference System ◽  
Component Structure ◽  
Training Models ◽  
Injection Attacks ◽  
Sql Injection Attacks

This paper proposes the use of hybrid models based on neural networks and fuzzy systems to build intelligent intrusion detection systems based on the theory of fuzzy rules. The presented system will be able to generate rules based on the results using fuzzy logic neurons. To avoid oversaturation and assist in determining the necessary network topology, training models based on extreme learning machine and regularization theory will be used to find the most significant neurons. In this paper, a type of SQL injection cyberattack is considered, which actively exploits errors in systems that communicate with the database via SQL commands, and for this reason is considered a kind of straightforward attack. The fuzzy neural network architecture used in detecting SQL injection attacks is a multi-component structure. The first two layers of the model are considered as a fuzzy inference system capable of extracting knowledge from data and transforming it into fuzzy rules. These rules help build automated systems for detecting SQL injection attacks. The third layer consists of a simple neuron that has an activation function called a leaky ReLU. The first layer consists of fuzzy neurons, the activation functions of which are Gaussian membership functions of fuzzy sets, defined in accordance with the partitioning of the input variables. The technique uses the concept of a simple linear regression model to solve the problem of choosing the best subsets of neurons. To perform model selection, the paper used the widely used least angular regression (LARS) algorithm.

scholarly journals Distributed Edge Computing with Blockchain Technology to Enable Ultra -Reliable Low-Latency v2x Communications

2021 ◽  
Author(s):  
Andrei Vladyko ◽  
Vasiliy Elagin ◽  
Anastasia Spirkina ◽  
Ammar Muthanna ◽  
Abdelhamied A. Ateya
Keyword(s):  
Energy Efficient ◽  
Information Exchange ◽  
Network Architecture ◽  
Edge Computing ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Advantages And Disadvantages ◽  
Blockchain Technology ◽  
Road Users ◽  
Secure Information

As V2X technology develops, acute problems related to reliable and secure information exchange between network objects in real time appear. The article aims to solve the scientific problem of building a network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The authors present a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels, and an energy-efficient algorithm of traffic offloading to the MEC server. The paper presents the results of application of blockchain technologies and mobile edge computing in the developed system, their description, evaluation of advantages and disadvantages of the implementation.

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