NFHP-RN:AMethod of Few-Shot Network Attack Detection Based on the Network Flow Holographic Picture-ResNet

Due to the rapid evolution of Advanced Persistent Threats(APTs)attacks,the emergence of new and rare attack samples,and even those never seen before,make it challenging for traditional rule-based detection methods to extract universal rules for effective detection.With the progress in techniques such as transfer learning and meta-learning,few-shot network attack detection has progressed.However,challenges in few-shot network attack detection arise from the inability of time sequence flow features to adapt to the fixed length input requirement of deep learning,difficulties in capturing rich information from original flow in the case of insufficient samples,and the challenge of high-level abstract representation.To address these challenges,a few-shot network attack detection based on NFHP(Network Flow Holographic Picture)-RN(ResNet)is proposed.Specifically,leveraging inherent properties of images such as translation invariance,rotation invariance,scale invariance,and illumination invariance,network attack traffic features and contextual relationships are intuitively represented in NFHP.In addition,an improved RN network model is employed for high-level abstract feature extraction,ensuring that the extracted high-level abstract features maintain the detailed characteristics of the original traffic behavior,regardless of changes in background traffic.Finally,a meta-learning model based on the self-attention mechanism is constructed,achieving the detection of novel APT few-shot network attacks through the empirical generalization of high-level abstract feature representations of known-class network attack behaviors.Experimental results demonstrate that the proposed method can learn high-level abstract features of network attacks across different traffic detail granularities.Comparedwith state-of-the-artmethods,it achieves favorable accuracy,precision,recall,and F1 scores for the identification of unknown-class network attacks through cross-validation onmultiple datasets.

Classified VPN Network Traffic Flow Using Time Related to Artificial Neural Network

VPNs are vital for safeguarding communication routes in the continually changing cybersecurity world.However,increasing network attack complexity and variety require increasingly advanced algorithms to recognize and categorizeVPNnetwork data.We present a novelVPNnetwork traffic flowclassificationmethod utilizing Artificial Neural Networks(ANN).This paper aims to provide a reliable system that can identify a virtual private network(VPN)traffic fromintrusion attempts,data exfiltration,and denial-of-service assaults.We compile a broad dataset of labeled VPN traffic flows from various apps and usage patterns.Next,we create an ANN architecture that can handle encrypted communication and distinguish benign from dangerous actions.To effectively process and categorize encrypted packets,the neural network model has input,hidden,and output layers.We use advanced feature extraction approaches to improve the ANN’s classification accuracy by leveraging network traffic’s statistical and behavioral properties.We also use cutting-edge optimizationmethods to optimize network characteristics and performance.The suggested ANN-based categorization method is extensively tested and analyzed.Results show the model effectively classifies VPN traffic types.We also show that our ANN-based technique outperforms other approaches in precision,recall,and F1-score with 98.79%accuracy.This study improves VPN security and protects against new cyberthreats.Classifying VPNtraffic flows effectively helps enterprises protect sensitive data,maintain network integrity,and respond quickly to security problems.This study advances network security and lays the groundwork for ANN-based cybersecurity solutions.

A new evolutional model for institutional field knowledge flow network

Purpose:This paper aims to address the limitations in existing research on the evolution of knowledge flow networks by proposing a meso-level institutional field knowledge flow network evolution model(IKM).The purpose is to simulate the construction process of a knowledge flow network using knowledge organizations as units and to investigate its effectiveness in replicating institutional field knowledge flow networks.Design/Methodology/Approach:The IKM model enhances the preferential attachment and growth observed in scale-free BA networks,while incorporating three adjustment parameters to simulate the selection of connection targets and the types of nodes involved in the network evolution process Using the PageRank algorithm to calculate the significance of nodes within the knowledge flow network.To compare its performance,the BA and DMS models are also employed for simulating the network.Pearson coefficient analysis is conducted on the simulated networks generated by the IKM,BA and DMS models,as well as on the actual network.Findings:The research findings demonstrate that the IKM model outperforms the BA and DMS models in replicating the institutional field knowledge flow network.It provides comprehensive insights into the evolution mechanism of knowledge flow networks in the scientific research realm.The model also exhibits potential applicability to other knowledge networks that involve knowledge organizations as node units.Research Limitations:This study has some limitations.Firstly,it primarily focuses on the evolution of knowledge flow networks within the field of physics,neglecting other fields.Additionally,the analysis is based on a specific set of data,which may limit the generalizability of the findings.Future research could address these limitations by exploring knowledge flow networks in diverse fields and utilizing broader datasets.Practical Implications:The proposed IKM model offers practical implications for the construction and analysis of knowledge flow networks within institutions.It provides a valuable tool for understanding and managing knowledge exchange between knowledge organizations.The model can aid in optimizing knowledge flow and enhancing collaboration within organizations.Originality/value:This research highlights the significance of meso-level studies in understanding knowledge organization and its impact on knowledge flow networks.The IKM model demonstrates its effectiveness in replicating institutional field knowledge flow networks and offers practical implications for knowledge management in institutions.Moreover,the model has the potential to be applied to other knowledge networks,which are formed by knowledge organizations as node units.

Algorithmic approach to discrete fracture network flow modeling in consideration of realistic connections in large-scale fracture networks

Analyzing rock mass seepage using the discrete fracture network(DFN)flow model poses challenges when dealing with complex fracture networks.This paper presents a novel DFN flow model that incorporates the actual connections of large-scale fractures.Notably,this model efficiently manages over 20,000 fractures without necessitating adjustments to the DFN geometry.All geometric analyses,such as identifying connected fractures,dividing the two-dimensional domain into closed loops,triangulating arbitrary loops,and refining triangular elements,are fully automated.The analysis processes are comprehensively introduced,and core algorithms,along with their pseudo-codes,are outlined and explained to assist readers in their programming endeavors.The accuracy of geometric analyses is validated through topological graphs representing the connection relationships between fractures.In practical application,the proposed model is employed to assess the water-sealing effectiveness of an underground storage cavern project.The analysis results indicate that the existing design scheme can effectively prevent the stored oil from leaking in the presence of both dense and sparse fractures.Furthermore,following extensive modification and optimization,the scale and precision of model computation suggest that the proposed model and developed codes can meet the requirements of engineering applications.

FlowTrust： trust inference with network flows

Web-based social networking is increasingly gaining popularity due to the rapid development of computer networking technologies. However, social networking applications still cannot obtain a wider acceptance by many users due to some unresolved issues, such as trust, security, and privacy. In social networks, trust is mainly studied whether a remote user behaves as expected by an interested user via other users, who are respectively named trustee, trustor, and recommenders. A trust graph consists of a trustor, a trustee, some recommenders, and the trust relationships between them. In this paper, we propose a novel FlowTrust approach to model a trust graph with network flows, and evaluate the maximum amount of trust that can flow through a trust graph using network flow theory. FlowTrust supports multi-dimensional trust. We use trust value and confidence level as two trust factors. We deduce four trust metrics from these two trust factors, which are maximum flow of trust value, maximum flow of confidence level, minimum cost of uncertainty with maximum flow of trust, and minimum cost of mistrust with maximum flow of confidence. We also propose three FlowTrust algorithms to normalize these four trust metrics. We compare our proposed FlowTrust approach with the existing RelTrust and CircuitTrust approaches. We show that all three approaches are comparable in terms of the inferred trust values. Therefore, FlowTrust is the best of the three since it also supports multi-dimensional trust.

Structure of Chinese City Network as Driven by Technological Knowledge Flows

Based on patent cooperation data,this study used a range of city network analysis approaches in order to explore the structure of the Chinese city network which is driven by technological knowledge flows.The results revealed the spatial structure,composition structure,hierarchical structure,group structure,and control structure of Chinese city network,as well as its dynamic factors.The major findings are:1) the spatial pattern presents a diamond structure,in which Wuhan is the central city;2) although the invention patent knowledge network is the main part of the broader inter-city innovative cooperation network,it is weaker than the utility model patent;3) as the senior level cities,Beijing,Shanghai and the cities in the Zhujiang(Pearl) River Delta Region show a strong capability of both spreading and controlling technological knowledge;4) whilst a national technology alliance has preliminarily formed,regional alliances have not been adequately established;5) even though the cooperation level amongst weak connection cities is not high,such cities still play an important role in the network as a result of their location within ′structural holes′ in the network;and 6) the major driving forces facilitating inter-city technological cooperation are geographical proximity,hierarchical proximity and technological proximity.

THE APPLICATION OF THE BRANCH AND BOUND METHOD FOR DETERMINING THE MINIMUM FLOW OF A TRANSPORT NETWORK

Blockage is a kind of phenomenon frequently occurred in a transport network, in which the human beings are the moving subjects. The minimum flow of a network defined in this paper means the maximum flow quantity through the network in the seriously blocked situation. It is an important parameter in designing and operating a transport network, especially in an emergency evacuation network. A branch and bound method is presented to solve the minimum flow problem on the basis of the blocking flow theory and the algorithm and its application are illustrated by examples.

Roles and Functions of Tourism Destinations in Tourism Region of South Anhui:A Tourist Flow Network Perspective

Theoretic and practical significance has been highlighted in the research of the roles and functions of destinations,as destinations are restricted by the spatial structure based on tourist flow network from the perspective of relationship.This article conducted an empirical analysis for Tourism Region of South Anhui(TRSA) and revealed the necessity and feasibility of studying the roles and functions of destinations from tourist flow network's perspective.The automorphic equivalence analysis and centrality analysis were used to classify 16 destinations in TRSA into six role types:tourist flow distribution center,hub of tourist flows,passageway destination,common touring destination,attached touring destination,and nearly isolated destination.Some suggestions were given on suitable infrastructure construction and destinations service designs according to their functions in network.This destination role positioning was based on tourist flow network structure in integral and macroscopic way.It provided an important reference for the balanced and harmonious development of all the destinations of TRSA.In addition,this article verified the applicability of social network analysis on tourist flow research in local scale,and expanded this method to destination role and function positioning.

A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network

Computer networks and power transmission networks are treated as capacitated flow networks.A capacitated flow network may partially fail due to maintenance.Therefore,the capacity of each edge should be optimally assigned to face critical situations-i.e.,to keep the network functioning normally in the case of failure at one or more edges.The robust design problem(RDP)in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure.The RDP is known as NP-hard.Thus,capacity assignment problem subject to system reliability and total capacity constraints is studied in this paper.The problem is formulated mathematically,and a genetic algorithm is proposed to determine the optimal solution.The optimal solution found by the proposed algorithm is characterized by maximum reliability and minimum total capacity.Some numerical examples are presented to illustrate the efficiency of the proposed approach.

Multi-layer Tectonic Model for Intraplate Deformation and Plastic-Flow Network in the Asian Continental Lithosphere

In a large area of the east—central Asian continent there is a unified seismic network system composed of two families of large—seismic belts that intersect conjugately. Such a seismic network in the middle—upper crust is actually a response to the plastic flow network in the lower lithosphere including the lower crust and lithospheric mantle. The existence of the unified plastic flow system confirms that the driving force for intraplate tectonic deformation results mainly from the compression of the India plate, while the long-range transmission of the force is carried out chiefly by means of plastic flow. The plastic flow network has a control over the intraplate tectonic deformation.

DDoS Attack Detection via Multi-Scale Convolutional Neural Network

Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.In this paper,we propose a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network(CNN).According to the different characteristics of the attack flow and the normal flow in the IP protocol,the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary.Based on the network flow grayscale matrix feature(GMF),the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation,global features and local features of the network flow are extracted.A DDoS attack classifier based on multi-scale convolution neural network is constructed.Experiments show that compared with correlation methods,this method can improve the robustness of the classifier,reduce the false alarm rate and the missing alarm rate.

A RECOGNITION PROBLEM IN CONVERTING LINEAR PROGRAMMING TO NETWORK FLOW MODELS

The main goal of this paper is to study the following combinatorial problem : given a finite set E = (e1, e2, ...,em} and a subset family a - [S1,S2, ... ,Sk} of E , does there exist a tree T with the edge set E such that each induced subgraph T[Si] of Si is precisely a path (1≤i≤k) ?

Cycle Flow Formulation of Optimal Network Flow Problems and Respective Distributed Solutions

In this paper, we use the cycle basis from graph theory to reduce the size of the decision variable space of optimal network flow problems by eliminating the aggregated flow conservation constraint. We use a minimum cost flow problem and an optimal power flow problem with generation and storage at the nodes to demonstrate our decision variable reduction method.The main advantage of the proposed technique is that it retains the natural sparse/decomposable structure of network flow problems. As such, the reformulated problems are still amenable to distributed solutions. We demonstrate this by proposing a distributed alternating direction method of multipliers(ADMM)solution for a minimum cost flow problem. We also show that the communication cost of the distributed ADMM algorithm for our proposed cycle-based formulation of the minimum cost flow problem is lower than that of a distributed ADMM algorithm for the original arc-based formulation.

Dynamic Characteristics Analysis on MHTGR Plant’s Secondary Side Fluid Flow Network

Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.

On Minimizing Delay with Probabilistic Splitting of Traffic Flow in Heterogeneous Wireless Networks

In the paper,we propose a framework to investigate how to effectively perform traffic flow splitting in heterogeneous wireless networks from a queue point.The average packet delay in heterogeneous wireless networks is derived in a probabilistic manner.The basic idea can be understood via treating the integrated heterogeneous wireless networks as different coupled and parallel queuing systems.The integrated network performance can approach that of one queue with maximal the multiplexing gain.For the purpose of illustrating the effectively of our proposed model,the Cellular/WLAN interworking is exploited.To minimize the average delay,a heuristic search algorithm is used to get the optimal probability of splitting traffic flow.Further,a Markov process is applied to evaluate the performance of the proposed scheme and compare with that of selecting the best network to access in terms of packet mean delay and blocking probability.Numerical results illustrate our proposed framework is effective and the flow splitting transmission can obtain more performance gain in heterogeneous wireless networks.

BLOCKING CUTSET OF A NETWORK AND ITS DETERMINATION (Ⅱ) Research on the Blocking Flow in a Transport Network

ransport network in the paper is defined as follows: (1) Connected and directed network without self loop;(2) There is only one source vertex with zero in degree; (3) There is only one sink vertex with zero out degree;(4) The capacity of every arc is non negative integer Blocking flow is a kind of flow commonly happened in a transport network . Its formation is due to the existance of a blocking cutset in the network. In this paper the fundamental concepts and theorems of the blocking flow and the blocking cutset are introduced and a linear programming model for determining the blocking cutset in a network is set up. In order to solve the problem by graph theoretical approach a method called 'two way flow augmenting algorithm' is developed. With this method an iterative procedure of forward and backward flow augmenting process is used to determine whether a given cutset is a blocking one.

A dynamic network Qo S control mechanism based on traffic prediction

A dynamic network Qo S control mechanism was proposed based on traffic prediction. It first predicts network traffic flow and then dynamically distributes network resources, which makes full use of network flow self-similarity and chaos. So it can meet changing network needs very well. The simulation results show that the dynamic Qo S control mechanism based on prediction has better network performance than that based on measurement.

New Algorithm to Evaluate the Unreliability of Flow Networks Based on Minimal Cutsets

Several conclusions on minimal cutset are proposed, from which a new algorithm is deduced to evaluate the unreliability of flow networks. Beginning with one unreliability product of the network, disjointed unreliability products are branched out one by one, every of which is selected from the network minimal cutsets. Finally the unreliability of the network is obtained by adding all these unreliability products up.

Steady State Gas Flow in Pipeline Networks: Existence and Uniqueness of Solution

In this paper we discuss the uniqueness and existence of solution to a real gas flow network by employing graph theory. A directed graph is an efficient way to represent a gas network. We consider steady state real gas flow network that includes pipelines, compressors, and the connectors. The pipelines and compressors are represented as edges of the graph and the interconnecting points are represented as nodes of the graph representing the network. We show that a unique solution of such a system exists. We use monotonicity property of a mapping to proof uniqueness, and the contraction mapping theorem is used to prove existence.

Minimization of the Expected Total Net Loss in a Stationary Multistate Flow Network System

In the present paper, a three-component, stationary, multistate flow network system is studied. Detailed costs and incomes are specified. The aim is to minimize the expected total net loss with respect to the expected times the components spend in each state. This represents a novelty in that we connect the expected component times spent in each state to the minimal total net loss of the system, without first finding the component importance. This is of interest in the design phase where one may tune the components to minimize the expected total net loss. Due to the complex nature of the problem, we first study a simplified version. There the expected times spent in each state are assumed equal for each component. Then a modified version of the full model is presented. The optimization in this model is completed in two steps. First the optimization is carried out for a set of pre-chosen fixed expected life cycle lengths. Then the overall minimum is identified by varying these expectations. Both the simplified and the modified optimization problems are nonlinear. The setup used in this article is such that it can easily be modified to represent other flow network systems and cost functions. The challenge lies in the optimization of real life systems.