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Improved Recommendation Algorithm Based On Knowledge Graph

Published: 05 February 2024 Publication History

Abstract

In order to solve the problem that It is difficult for network security operation and maintenance personnel to identify the required data in a timely and accurate manner when analyzing network security events, this paper proposed a recommendation algorithm of network security event based on knowledge graph, it uses the network threat framework ATT&CK to construct an ontology model, establishes a network threat knowledge graph based on the ontology model, extract discrete security data into interrelated security knowledge. This article extracts entity data based on knowledge graph, obtains entity vectors through TransH algorithm, and uses entity vectors to calculate data similarity between entities in network threat data. We extract network security data entities from literature on network security event handling as disposal behaviors, construct a disposal behavior matrix, and use the behavior matrix to achieve vectorized representation of network threat data. it calculates the similarity of network threat data entities based on disposal behaviors. Finally, the similarity between the network threat data and the threat data under network security event handling behavior is fused to form a data recommendation list for network security events, achieving correlation between network threat domains based on user behavior. The algorithm incorporates disposition behavior similarity on the basis of data similarity, which is closer to factual disposition behavior. Compared with other algorithms, this algorithm has a significant advantage in recall rate and accuracy within the range of recommended data volume less than 10.

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        CECCT '23: Proceedings of the 2023 International Conference on Electronics, Computers and Communication Technology
        November 2023
        266 pages
        ISBN:9798400716300
        DOI:10.1145/3637494
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Published: 05 February 2024

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