Network Security Situational Awareness Model Based on Threat Intelligence

2021 ◽  
pp. 526-536
Author(s):  
Hongbin Zhang ◽  
Yan Yin ◽  
Dongmei Zhao ◽  
Bin Liu ◽  
Hongbin Gao
Author(s):  
Mei Hong Chen

To explore the prediction effect of network security situational awareness on network vulnerabilities and attacks under the background of big data, this study constructs a predictive index system based on the network security situational awareness model. Based on the improved cuckoo algorithm, the cuckoo search radial basis function neural network is used to predict the situation. The weight value in the model is determined by the hierarchical analysis method, vulnerability simulation is conducted by Nessus software and network attack simulation is conducted by Snort software, and then the situation is evaluated by a fuzzy comprehensive evaluation method. Finally, Jquery and Bootstrap software is used to develop the system. The results show that the cuckoo search radial basis function model proposed in this study could predict network security situations more accurately than the radial basis function model, cuckoo search back-propagation neural network model, genetic algorithm radial basis function model and Support vector machine model based on particle swarm optimization model.


2013 ◽  
Vol 846-847 ◽  
pp. 1632-1635
Author(s):  
Abasi

Security situational awareness has become a hot topic in the area of network securityresearch in recent years. The existing security situational awareness methods are analyzed and compared in details, and thus a newnetwork security situational awareness model based on information fusion is proposed. This modelfuses multi-source information from a mass of logs by introducing the modified D-S evidence theory,gets the values of nodes security situational awareness by situational factors fusion using attacks threat,and vulnerability information which network nodes have and successful attacks depend on, computesthe value of network security situational awareness by nodes situation fusion using service informationof the network nodes, and draws the security-situation-graph of network. Then, it analyzes the timeseries of the computing results by ARMA model to forecast the future threat in network security.Finally an example of actual network datasets is given to validate the network security situationalawareness model and algorithm. The results show that this model and algorithm is more effective andaccurate than the existing security situational awareness methods.


2014 ◽  
Vol 556-562 ◽  
pp. 6294-6297 ◽  
Author(s):  
Xiao Liang ◽  
Hong Wu Lv ◽  
Fang Fang Guo ◽  
Hui Qiang Wang

Network Security Situation Awareness (NSSA) is a hot topic in network security field, and cloud computing is a new technology integrated virtual storage and distributed computing. It has become the challenging questions how to provide efficient and reliable service for NSSA based on the cloud computing.This paper proposes a cloud security situation awareness model based on data mining, and puts forwarda parallelfrequent-tree Apriori algorithm (PFT-Apriori) for mining association rules. Compare with the traditional Apriori algorithm, the experimental results show that the performance of system is increased by 51% under PFT-algorithm.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yikun Zhu ◽  
Zhiling Du

In today’s increasingly severe network security situation, network security situational awareness provides a more comprehensive and feasible new idea for the inadequacy of various single solutions and is currently a research hotspot in the field of network security. At present, there are still gaps or room for improvement in network security situational awareness in terms of model scheme improvement, comprehensive and integrated consideration, algorithm design optimization, etc. A lot of scientific research investments and results are still needed to improve the form of network security in a long and solid way. In this paper, we propose a network security posture assessment model based on time-varying evidence theory for the existing multisource information fusion technology that lacks consideration of the problem of threat occurrence support rate over time and make the threat information reflect the law of time change by introducing a time parameter in the basic probability assignment value. Thus, the existing hierarchical threat posture quantitative assessment technique is improved and a hierarchical multisource network security threat posture assessment model based on time-varying evidence theory is proposed. Finally, the superiority of the proposed model is verified through experiments.


2021 ◽  
pp. 537-549
Author(s):  
Junwei Zhang ◽  
Huamin Feng ◽  
Biao Liu ◽  
Ge Ge ◽  
Jing Liu

Computers ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 18 ◽  
Author(s):  
Konstantinos Rantos ◽  
Arnolnt Spyros ◽  
Alexandros Papanikolaou ◽  
Antonios Kritsas ◽  
Christos Ilioudis ◽  
...  

Threat intelligence helps businesses and organisations make the right decisions in their fight against cyber threats, and strategically design their digital defences for an optimised and up-to-date security situation. Combined with advanced security analysis, threat intelligence helps reduce the time between the detection of an attack and its containment. This is achieved by continuously providing information, accompanied by data, on existing and emerging cyber threats and vulnerabilities affecting corporate networks. This paper addresses challenges that organisations are bound to face when they decide to invest in effective and interoperable cybersecurity information sharing and categorises them in a layered model. Based on this, it provides an evaluation of existing sources that share cybersecurity information. The aim of this research is to help organisations improve their cyber threat information exchange capabilities, to enhance their security posture and be more prepared against emerging threats.


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