The Effect of Key Nodes on theMalware Dynamics in the Industrial Control Network

As industrialization and informatization becomemore deeply intertwined,industrial control networks have entered an era of intelligence.The connection between industrial control networks and the external internet is becoming increasingly close,which leads to frequent security accidents.This paper proposes a model for the industrial control network.It includes a malware containment strategy that integrates intrusion detection,quarantine,and monitoring.Basedonthismodel,the role of keynodes in the spreadofmalware is studied,a comparisonexperiment is conducted to validate the impact of the containment strategy.In addition,the dynamic behavior of the model is analyzed,the basic reproduction number is computed,and the disease-free and endemic equilibrium of the model is also obtained by the basic reproduction number.Moreover,through simulation experiments,the effectiveness of the containment strategy is validated,the influence of the relevant parameters is analyzed,and the containment strategy is optimized.In otherwords,selective immunity to key nodes can effectively suppress the spread ofmalware andmaintain the stability of industrial control systems.The earlier the immunization of key nodes,the better.Once the time exceeds the threshold,immunizing key nodes is almost ineffective.The analysis provides a better way to contain the malware in the industrial control network.

RESEARCH ON KEY NODES OF WIRELESS SENSOR NETWORK BASED ON COMPLEX NETWORK THEORY

On the basis of complex network theory, the issues of key nodes in Wireless Sensor Networks (WSN) are discussed. A model expression of sub-network fault in WSN is given at first; subsequently, the concepts of average path length and clustering coefficient are introduced. Based on the two concepts, a novel attribute description of key nodes related to sub-networks is proposed. Moreover, in terms of node deployment density and transmission range, the concept of single-point key nodes and generalized key nodes of WSN are defined, and their decision theorems are investigated.

Analysis of identification methods of key nodes in transportation network

The identification of key nodes plays an important role in improving the robustness of the transportation network.For different types of transportation networks,the effect of the same identification method may be different.It is of practical significance to study the key nodes identification methods corresponding to various types of transportation networks.Based on the knowledge of complex networks,the metro networks and the bus networks are selected as the objects,and the key nodes are identified by the node degree identification method,the neighbor node degree identification method,the weighted k-shell degree neighborhood identification method(KSD),the degree k-shell identification method(DKS),and the degree k-shell neighborhood identification method(DKSN).Take the network efficiency and the largest connected subgraph as the effective indicators.The results show that the KSD identification method that comprehensively considers the elements has the best recognition effect and has certain practical significance.

Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.

Identification of key recovering node for spatial networks

Many networks in the real world have spatial attributes, such as location of nodes and length of edges, called spatial networks. When these networks are subject to some random or deliberate attacks, some nodes in the network fail, which causes a decline in the network performance. In order to make the network run normally, some of the failed nodes must be recovered. In the case of limited recovery resources, an effective key node identification method can find the key recovering node in the failed nodes, by which the network performance can be recovered most of the failed nodes. We propose two key recovering node identification methods for spatial networks, which are the Euclidean-distance recovery method and the route-length recovery method. Simulations on homogeneous and heterogeneous spatial networks show that the proposed methods can significantly recover the network performance.

KNEMAG:Key Node Estimation Mechanism Based on Attack Graph for IoT Security

With the rapid development and widespread application of the IoT,the at-tacks against IoT vulnerabilities have become more complex and diverse.Most of the previous research focused on node vulnerability and its risk analysis.There is little information available about the importance of the location of the node in the system.Therefore,an estimation mechanism is proposed to assess the key node of the IoT system.The estimation of the key node includes two parts:one is the utilization relationship between nodes,and the other is the impact on the system after the node is conquered.We use the node importance value and the node risk value to quantify these two parts.First,the node importance value is calculated by considering the attack path that pass through the node and the probability that the attacker will abandon the attack.Second,in addition to node vulnerabilities and the consequences of being attacked,two quantitative indicators are proposed to comprehensively assess the impact of nodes on the system security,and the node risk value is calculated based on the grey correlation analysis method.Third,the key node in the IoT system could be obtained by integrating the node importance value and risk value.Finally,the simulation experiment result shows that the presented method could find the key node of the system quickly and accurately.

Cause Analysis of Consumer‑Grade UAV Accidents Based on Grounded Theory‑Bayesian Network

In order to reduce the accident rate of consumer-grade unmanned aerial vehicles(UAVs)in daily use scenarios,the accident causes are analyzed based on the accident cases of consumer-grade UAVs.By extracting accident causing factors based on the Grounded theory,the relationship between these factors is analyzed.The Bayesian network for consumer-grade UAV accidents is constructed.With the Grounded theory-Bayesian network,the probability of four types of accidents is inferred:fall,air collision,disappearance,and personal injury.With the posterior probability of each factor being reversely reasoned,the causal chain with the maximum probability of each accident is obtained.After the sensitivity of each factor is analyzed,the key nodes in the network accordingly are inferred.Then the causing factors of consumer-grade UAV accidents are analyzed.The results show that the probability of fall accident is the highest,the fall accident is associated with the probabilistic maximum causal chain of personal injury,and the sensitivity analysis results of each type of accident as the result node are inconsistent.

Vulnerability of Regional Aviation Networks Based on DBSCAN and Complex Networks

To enhance the accuracy of performance analysis of regional airline network,this study applies complex network theory and Density-Based Spatial Clustering of Applications with Noise(DBSCAN)algorithm to investigate the topology of regional airline network,constructs node importance index system,and clusters 161 airport nodes of regional airline network.Besides,entropy power method and approximating ideal solution method(TOPSIS)is applied to comprehensively evaluate the importance of airport nodes and complete the classification of nodes and identification of key points;adopt network efficiency,maximum connectivity subgraph and network connectivity as vulnerability measurement indexes,and observe the changes of vulnerability indexes of key nodes under deliberate attacks and 137 nodes under random attacks.The results demonstrate that the decreasing trend of the maximum connectivity subgraph indicator is slower and the decreasing trend of the network efficiency and connectivity indicators is faster when the critical nodes of the regional airline network are deliberately attacked.Besides,the decreasing trend of the network efficiency indicator is faster and the decreasing trend of the maximum connectivity subgraph indicator is slower when the nodes of four different categories are randomly attacked.Finally,it is proposed to identify and focus on protecting critical nodes in order to better improve the security level of regional airline system.

An Energy-Efficient Query Based on Variable Region for Large-Scale Smart Grid

The state-of-the-art query techniques in power grid monitoring systems focus on querying history data, which typically introduces an unwanted lag when the systems try to discover emergency situations. The monitoring data of large-scale smart grids are massive, dynamic and highly dimensional, so global query, the method widely adopted in continuous queries in Wireless Sensor Networks(WSN), is rendered not suitable for its high energy consumption. The situation is even worse with increasing application complexity. We propose an energy-efficient query technique for large-scale smart grids based on variable regions. This method can query an arbitrary region based on variable physical windows, and optimizes data retrieve paths by a key nodes selection strategy. According to the characteristics of sensing data, we introduce an efficient filter into the each query subtree to keep non-skyline data from being retrieved. Experimental results show that our method can efficiently return the overview situation of any query region. Compared to TAG and ESA, the average query efficiency of our approach is improved by 79% and 46%, respectively; the total energy consumption of regional query is decreased by 82% and 50%, respectively.

Finding Key Node Sets in Complex Networks Based on Improved Discrete Fireworks Algorithm

Finding out the key node sets that affect network robustness has great practical significance for network protection and network disintegration.In this paper,the problem of finding key node sets in complex networks is defined firstly.Because it is an NP-hard combinatorial optimization problem,discrete fireworks algorithm is introduced to search the optimal solution,which is a swarm intelligence algorithm and is improved by the prior information of networks.To verify the effect of improved discrete fireworks algorithm(IDFA),experiments are carried out on various model networks and real power grid.Results show that the proposed IDFA is obviously superior to the benchmark algorithms,and networks suffer more damage when the key node sets obtained by IDFA are removed from the networks.The key node sets found by IDFA contain a large number of non-central nodes,which provides the authors a new perspective that the seemingly insignificant nodes may also have an important impact on the robustness of the network.

Vulnerability analysis for airport networks based on fuzzy soft sets: From the structural and functional perspective

Abstract Recently, much attention has been paid to the reliability and vulnerability of critical infrastructure. In air traffic systems, the vulnerability analysis for airport networks can be used to guide air traffic administrations in their prioritization of the maintenance and repair of airports, as well as to avoid unnecessary disturbances in the planning of flight schedules. In this paper, the evaluation methods of airport importance and network efficiency arc established. Firstly, the evaluation indices of airport importance are proposed from both the topological and functional perspectives. The topological characteristics come from the structure of airport network and the functional features stem from the traffic flow distribution taking place inside the network. Secondly, an integrated evaluation method based on fuzzy soft set theory is proposed to identify the key airports, which can fuse together importance indices over different time intervals. Thirdly, an airport network efficiency method is established for the purpose of assessing the accuracy of the evaluation method. Finally, empirical studies using real traffic data of US and China＇s airport networks show that the evaluation method proposed in this papcr is the most accuratc. Thc vulucrability of US and China＇s airport networks is compared. The similarities and differences between airport geography distribution and airport importance distribution are discussed here and the dynamics of airport importance is studied as well.