An Improved Information Security Risk Assessments Method for Cyber-Physical-Social Computing and Networking

Li, Senyu; Bi, Fangming; Chen, Wei; Miao, Xuzhi; Liu, Jin; Tang, Chaogang

doi:10.1109/access.2018.2800664

Cited by 37 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Compared with the standard BPNN, BPNN optimized by genetic algorithm (GA-BP) neural network has lower simulation error and better fitting effect. Li et al 27 and Dong et al 28 use cuckoo search algorithm to optimize BPNN neural network parameters to avoid BP falling into local extreme values, thereby improving training speed and evaluation accuracy. Compared with genetic-BPNN algorithm, it has training time, error, and accuracy.…”

Section: Related Workmentioning

confidence: 99%

An efficient method for network security situation assessment

Tao

Kong

Zhao

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Network security situational assessment, the core task of network security situational awareness, can obtain security situation by comprehensively analyzing various factors that affect network status. Thus, network security situational assessment can provide accurate security state evaluation and security trend prediction for users. Although plenty of network security situational assessment methods have been proposed, there are still many problems to solve. First, because of high dimensionality of input data, computational complexity in model construction could be very high. Moreover, most of the existing schemes trade computational overhead for accuracy. Second, due to the lack of centralized standard, the weights of indicators are usually determined empirically or by subjective opinions of domain expert. To solve the above problems, we propose a novel network security situation assessment method based on stack autoencoding network and back propagation neural network. In stack autoencoding network and back propagation neural network, to reduce the data storage overhead and improve computational efficiency, we use stack autoencoding network to reduce the dimensions of the indicator data. And the low-dimensional data output by hidden layer of stack autoencoding network will be the input data of the error back propagation neural network. Then, the back propagation neural network algorithm is adopted to perform network security situation assessment. Finally, extensive experiments are conducted to verify the effectiveness of the proposed method.

show abstract

Section: Related Workmentioning

confidence: 99%

An efficient method for network security situation assessment

Tao

Kong

Zhao

et al. 2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…They could check the reasoning and experience can be borrowed from safety area on how to avoid and detecting fallacious reasoning in the arguments [30]. This can also help feedback into the organizations' risk assessment procedure as it usually requires the causal analysis of the incidents as well as the security recommendations to mitigate the risks [49,50,51,52,53]. The use of a graphical notation provides stakeholders with an overview of key issues before being forced to read the hundreds of pages of detailed prose that increasingly documents the findings of security investigations.…”

Section: Lessons Learned Sharing Using Diagramsmentioning

confidence: 99%

Security Assurance Modelling of Security Incident in Healthcare using the Generic Security Template (GST)

Luo

2020

Preprint

View full text Add to dashboard Cite

Background: The recent industry reports show that the number of security incidents in healthcare sector is still increasing, especially the high severity incident, such as data leakage incident and ransomware, which can lead to significant impact on healthcare services. It is imperative for the organizations to learn lessons from those incidents. Traditional ways to disseminate lessons learned are based on text approach, the linear format of which can obscure relationships among concepts and discourage readers from integrating information across ideas. Graphical diagrams can serve this purpose, as it can communicate both individual elements of information and relationships between them. Methods: The Generic Security Template (GST) has been proposed to support the exchange of lessons learned from security incidents. It utilises graphical notations to communicate both individual elements of information and relationships between them. This paper conducts a case study by adopting the GST to capture and structure the incident information of a data leakage incident in a UK healthcare organization in order to facilitate incident exchange. Results: The results show that, the GST was able to visualise and depict the key elements, including lessons learned, the associated security requirements and organizational contextual information identified from the selected data leakage incident case study from NHS. GST provides a unified way to communicate incident information. Conclusions: This research has significance for the healthcare organizations to improve their incident learning practices. It fosters an environment where different stakeholders can speak the same language while exchanging the lessons learned from the security incidents. Future work will consider apply the GST to analyse other complex security incidents such as the advanced persistent threats (APTs) in healthcare organizations and extend the use of the GST in other industries. Keywords: Security Assurance Modelling, Generic Security Template (GST), Security Incident, Healthcare Organization.

show abstract

“…Further they have concentrate on security factors. Senyu Li et al [2] have found delay speed in the developed neural network and information security is more dangerous due to the changing world of IoT. For indentifying risk factors in cyber-physical system and network most of the researcher suggested Bayesian Network and Wavelet Neural Network (WNN).…”

Section: Cyber Security In Cyber Physical Systemmentioning

confidence: 99%

Cyber Security Affairs in Empowering Technologies

2019

IJITEE

View full text Add to dashboard Cite

Digital world connected millions of people through internet for our day today activities, and therefore security, privacy, authentication issues is a key question today. The main objective of this paper is to study various issues in enabling technologies such as Cyber Physical System (CPS), Internet of Things (IoT), Big Data Analytics (BDA) and Artificial Intelligence (AI). IoT is a network of devices which link, interact and transfer data but security and privacy should concern while moving from traditional to modern world. CPS is a combination of data processing, networking and physical activities, through critical infrastructure attacker access computer devices and damages the system. Big data shared high volume, velocity, variety, context and content data to distributed system and key issue is data lost by harm. Artificial Intelligence can perform job as like human brain and solve real world problem but optical network can be easily attacked by hackers. This paper investigate different threats, limitation and future work in the cyber physical system, IoT etc and various methods are discussed from various articles and it is more helpful in doing further research work in this area and cyber security is the most important tool which can be implement to protect the cyberspace from the inside and outside attacker.

show abstract

An Improved Information Security Risk Assessments Method for Cyber-Physical-Social Computing and Networking

Cited by 37 publications

References 30 publications

An efficient method for network security situation assessment

An efficient method for network security situation assessment

Security Assurance Modelling of Security Incident in Healthcare using the Generic Security Template (GST)

Cyber Security Affairs in Empowering Technologies

Contact Info

Product

Resources

About