A Semantic Model for Security Evaluation of Information Systems

Doynikova, Elena; Fedorchenko, Andrey; Kotenko, Igor

doi:10.13052/jcsm2245-1439.925

Cited by 7 publications

(5 citation statements)

References 13 publications

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“…Although the hierarchical network structure design makes the management of ICS more convenient and efficient, the overall security of the network has been threatened to a certain extent. Moreover, due to the different control tasks completed by each network layer, the requirements for security protection intensity vary greatly, so this hierarchical network structure even increases the cost of overall network security protection [26,27].…”

Section: Application Of Lightgbm Feature Algorithm Fusion With Multil...mentioning

confidence: 99%

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Deng

2024

JCSANDM

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Continuous improvement of machine learning technology has provided more support for intrusion security detection in the industrial Internet of Things (IoT). The intrusion security detection system based on LightGBM feature algorithm and multi-layer perception network fusion provides more options for improving security detection, further enhancing the effectiveness of industrial IoT intrusion security detection. By comparing the applications of different models in the security detection process, the model constructed based on the LightGBM feature algorithm can achieve higher accuracy and precision in industrial IoT intrusion security detection, as well as higher F1-score and AUC values; A more reasonable detection time also lays the foundation for improving the overall efficiency of industrial IoT intrusion security detection. Therefore, in the field of industrial IoT intrusion security detection, the detection model constructed in this article can provide more support for further improvement and improvement of IoT intrusion security detection performance.

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Section: Application Of Lightgbm Feature Algorithm Fusion With Multil...mentioning

confidence: 99%

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Deng

2024

JCSANDM

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“…For dealing with the complex systems of systems that are vulnerable to cyberattacks, it aims to integrate different types of evaluation into a single model for all activities at all levels of all organizations in an enterprise. Finally, Doynikova et al (2020) propose a semantic model for the security evaluation of information systems in which an ontology of security metrics is developed to trace dependencies among available security knowledge sources, available raw security data and metrics calculated on their base (divided by the security assessment goals) and security assessment goals. The key aspect of ontology is to process huge streams of gathered security-related data (both static, from open-source databases, and dynamic, from security monitoring tools).…”

Section: Role Of Assurance Contextmentioning

confidence: 99%

“…Hence, this paper seeks to address this research void and bridge the knowledge gap. The system under evaluation, stakeholder, purpose of evaluation (needs), standards (approved methods), environments Doynikova et al (2020) Product, infrastructure (host, network), configuration, attacker…”

Section: Ontology-based Conceptual Modeling For Security Assurance Ev...mentioning

confidence: 99%

Exploring the role of assurance context in system security assurance evaluation: a conceptual model

Wen,

Katt

2023

ICS

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Purpose Security assurance evaluation (SAE) is a well-established approach for assessing the effectiveness of security measures in systems. However, one aspect that is often overlooked in these evaluations is the assurance context in which they are conducted. This paper aims to explore the role of assurance context in system SAEs and proposes a conceptual model to integrate the assurance context into the evaluation process. Design/methodology/approach The conceptual model highlights the interrelationships between the various elements of the assurance context, including system boundaries, stakeholders, security concerns, regulatory compliance and assurance assumptions and regulatory compliance. Findings By introducing the proposed conceptual model, this research provides a framework for incorporating the assurance context into SAEs and offers insights into how it can influence the evaluation outcomes. Originality/value By delving into the concept of assurance context, this research seeks to shed light on how it influences the scope, methodologies and outcomes of assurance evaluations, ultimately enabling organizations to strengthen their system security postures and mitigate risks effectively.

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“…Assume that result obtained from the above formula is at a certain observation time t = 1. Then it is only necessary to calculate corresponding capability index of the node i based on its own safety status elements, s ij ∈ T (t) represents a certain service that the network node can provide at present, q 1 (•) represents the specific capability index calculation method, B i (t) represents the combination of attack sequences faced by the network node i in the observation period t, W i (t) represents the vulnerability set of the network node i in the observation period t, and E i (t) represents the network protection strategy set in the observation period t, which is calculated by [7]…”

Section: Quantitative Model Of Wn Safety Statusmentioning

confidence: 99%

“…Some research achievements have been obtained by some scientists, Pranav M. Pawar et al addressed behavioural modeling of medium access control security attacks in WSNs, which is benefit for establishing efficient and secure MAC clayer protocols [6]. Elena Doynikova et al considered development and application of a semantic model for security evaluation [7]. Shishir Kumar Shandilya proposed a network test-bed for examining new security concepts like cyber immunity [8].…”

Section: Introductionmentioning

confidence: 99%

Wireless Network Safety Status Prediction Based on Fuzzy Logic

Xue

Zheng

2023

JCSANDM

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In WN environment, network safety status means to state of information managing in various WN environments, WN transmission information safety status assessment is one of primary research directions in this territory. Existing network safety status awareness methods are difficult to adapt to real-time changes of network structure, and WN environment is complex and changeable, and they can only analyze the current network safety status, and it is difficult to predict and analyze overall tendency of WN safety status. In order to resist potential attacks, evaluate safety of network and detect attack means in network in a timely manner, this paper introduces fuzzy logic to propose a safety status prediction model for wireless sensor networks, which can help administrators to timely perceive and comprehensively grasp the real-time status of network and predict future advancement direction. In order to assess current network status, a safety status evaluation model for wireless network (WN) depended on fuzzy logic is presented. In cluster head node, neighborhood rough set is used for feature extraction to reduce energy consumption of redundant data on the node. Balance data by synthesizing a few over-sampling techniques, and then use random forest to detect attacks on the network to identify attack types. Combined with the status element acquisition mechanism, three status indicators, namely attack frequency, total number of attacks and threat factor, are extracted. According to the status indicators and status calculation method, the network safety status value is calculated, and current network safety status is evaluated by referring to network safety level divided by National Internet Emergency Response Center. Neighborhood rough set is applied to complete attribute reduction, which can effectively deal with underwater mixed data and obtain feature subsets with same classification capability as initial data. Safety status of WSN is predicted based on random forest. The risk degree of WN status is divided into fuzzy subsets, and the process of dynamic prediction of safety status is designed. Based on test values, highest input signal spectrum of the system is 30 mV, and the lowest input signal spectrum is -15 mV, which is consistent with the selected 120 groups of status data sequence diagram, the fluctuation amplitude of the input signal under 40~62 groups of samples is small, basically unchanged, consistent with the selected 120 groups of status data sequence diagram. Fuzzy logic model represented by star broken line has higher precision than decision tree and the limit learning machine in all five different attack types. mapped network safety status grade can also effectively express the actual network safety status. indicating that the prediction results of the system are accurate.

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A Semantic Model for Security Evaluation of Information Systems

Cited by 7 publications

References 13 publications

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Design of Industrial IoT Intrusion Security Detection System Based on LightGBM Feature Algorithm and Multi-layer Perception Network

Exploring the role of assurance context in system security assurance evaluation: a conceptual model

Wireless Network Safety Status Prediction Based on Fuzzy Logic

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