Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles

Lee, Jonghoon; Kim, Jong-Hyun; Kim, Ikkyun; Han, Kijun

doi:10.1109/access.2019.2953095

Cited by 88 publications

(23 citation statements)

References 47 publications

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“…UBA solutions study patterns of human behavior and then apply algorithms to detect anomalies of these patterns. For example, in [5], the case is considered in which an attacker on a company's network can use stolen credentials to secretly collect confidential data. This behavior is difficult to detect unless it triggers an access violation or data leak warning.…”

Section: Discussionmentioning

confidence: 99%

Dataset of User Reactions When Filling Out Web Questionnaires

Magomedov

Ilin

Silaeva

et al. 2020

Data

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This paper presents the dataset and the results of the analysis of user reactions when filling out questionnaires. Based on the analysis of 1980 results of users’ responses to simple questionnaire questions, patterns in user reactions were revealed. Data analysis shows that a user is characterized by reactions when answering a variety of questions, reflecting the individual skills of the interface, reading speed, speed of choosing an answer, which can be used to supplement personal verification in information systems. The built-in reaction time does not significantly load the data volumes for logging and transferring and does not contain confidential information. The data would be of interest for further research by specialists in the field of psychology, information security, and information systems design.

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Section: Discussionmentioning

confidence: 99%

Dataset of User Reactions When Filling Out Web Questionnaires

Magomedov

Ilin

Silaeva

et al. 2020

Data

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“…Jonghoon Lee et al [21] proposed a Man-made intelligence strategy for cyber-threats recognition, given artificial neural organizations. The proposed procedure changes a huge number of gathered safety measures to singular events summary and utilizes a profound learning-based recognition technique.…”

Section: Related Workmentioning

confidence: 99%

Cybersecurity Threats Detection In IoT Using Krill Based Deep Neural Network Stacked Auto Encoders

Rana¹,

Chauhan

Patil³

2021

Preprint

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The Internet of things (IoT) has concerned much significance for some manufacturing sectors including clinical fields, co-ordinations following, savvy urban communities, and automobiles. Anyway as a worldview, it is sensitive to different sorts of cyber-attacks. Customary very good quality security resolutions for guarantee an IoT structure are not reasonable. This deduces clever organization-based security plans as AI arrangements ought to be made. In this work, we propose Cyber Security Threats recognition in IoT utilizing Krill Based Deep Neural Network Stacked Auto Encoders (KDNN-SAE). In our proposed approach, first, the information pre-processing measure was acted in the underlying development before isolating the dataset into two segments: preparing and test. At that point, flow-based features are extracted from the pre-processed information. By then, the properties to be utilized by the algorithms are chosen in the attribute determination utilizing the Genetic Algorithm (GA). At last, our methodology completes with the execution of the machine learning algorithm KDNN-SAE. The exploratory results show that the introduced method beats the existing techniques to different execution measures.

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“…eir prediction model uses an expectation maximization (EM) algorithm [13][14][15]. Ghorbanian et al [16] proposed a framework to incorporate predictive analysis technology into CEP applications.…”

Section: Introductionmentioning

confidence: 99%

Big Data-Driven Hierarchical Local Area Network Security Risk Event Prediction Algorithm

Zhou¹

2022

Scientific Programming

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Big data processing technology has attracted a lot of attention due to its forecasting and warning of Internet security situation. The current risk assessment system still has problems such as high false alarm rate and excessive reliance on expert knowledge in the security defense system. Based on the big data-driven principle, this paper constructs a hierarchical local area network security risk event prediction model and proposes a predictive complex event processing method. The model building process is evolved and improved on the basis of the scoring function. The establishment method of vulnerability database and vulnerability association database is introduced in detail. At the same time, the problem of the difference between the structure and identification method of the information in the information database and the vulnerability database is solved, and the effect of timely modification when the data do not match is realized. Experimental results show that the algorithm has an accuracy of 98.75% and a fault tolerance rate of 0.0035, which promotes the accuracy of the network risk assessment results based on multistage network attacks.

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Cyber Threat Detection Based on Artificial Neural Networks Using Event Profiles

Cited by 88 publications

References 47 publications

Dataset of User Reactions When Filling Out Web Questionnaires

Dataset of User Reactions When Filling Out Web Questionnaires

Cybersecurity Threats Detection In IoT Using Krill Based Deep Neural Network Stacked Auto Encoders

Big Data-Driven Hierarchical Local Area Network Security Risk Event Prediction Algorithm

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