A Novel Approach for Protection of Accounts' Names against Hackers Combining Cluster Analysis and Chaotic Theory
Desislav Andreev (TU-Sofia), Simona Petrakieva (TU-Sofia), Ina, Taralova (ECN, LS2N)

TL;DR
This paper introduces a novel method combining cluster analysis and chaos theory to detect false usernames and protect user accounts from hackers, enhancing data security in various online applications.
Contribution
It presents a new approach that integrates machine learning and chaos theory to identify false accounts and analyze username creation trends.
Findings
Effective detection of false usernames using the proposed method
Identification of username creation patterns and trends
Improved accuracy in distinguishing real from fake accounts
Abstract
The last years of the 20 th century and the beginning of the 21 th mark the facilitation trend of our real life due to the big development and progress of the computers and other intelligent devices. Algorithms based on artificial intelligence are basically a part of the software. The transmitted information by Internet or LAN arises continuously and it is expected that the protection of the data has been ensured. The aim of the present paper is to reveal false names of users' accounts as a result of hackers' attacks. The probability a given account to be either false or actual is calculated using a novel approach combining machine learning analysis (especially clusters' analysis) with chaos theory. The suspected account will be used as a pattern and by classification techniques clusters will be formed with a respective probability this name to be false. This investigation puts two main…
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Taxonomy
TopicsChaos-based Image/Signal Encryption · Network Security and Intrusion Detection · Neural Networks and Applications
