Prosiectau fesul blwyddyn
Crynodeb
As more business transactions and information services have been implemented via communication networks, both personal and organization assets encounter a higher risk of attacks. To safeguard these, a perimeter defence like NIDS (network-based intrusion detection system) can be effective for known intrusions. There has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks, where obfuscation techniques are applied to disguise patterns of intrusive traffics. The current research focuses on non-payload connections at the TCP (transmission control protocol) stack level that is applicable to different network applications. In contrary to the wrapper method introduced with the benchmark dataset, three new filter models are proposed to transform the feature space without knowledge of class labels. These ECT (ensemble clustering based transformation) techniques, i.e., ECT-Subspace, ECT-Noise and ECT-Combined, are developed using the concept of ensemble clustering and three different ensemble generation strategies, i.e., random feature subspace, feature noise injection and their combinations. Based on the empirical study with published dataset and four classification algorithms, new models usually outperform that original wrapper and other filter alternatives found in the literature. This is similarly summarized from the first experiment with basic classification of legitimate and direct attacks, and the second that focuses on recognizing obfuscated intrusions. In addition, analysis of algorithmic parameters, i.e., ensemble size and level of noise, is provided as a guideline for a practical use.
Iaith wreiddiol | Saesneg |
---|---|
Tudalennau (o-i) | 2479-2498 |
Nifer y tudalennau | 20 |
Cyfnodolyn | Computers, Materials and Continua |
Cyfrol | 74 |
Rhif cyhoeddi | 2 |
Dynodwyr Gwrthrych Digidol (DOIs) | |
Statws | Cyhoeddwyd - 31 Hyd 2022 |
Ôl bys
Gweld gwybodaeth am bynciau ymchwil 'Classification of Adversarial Attacks Using Ensemble Clustering Approach'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.Prosiectau
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Recent Findings in Technology Described by Researchers from Aberystwyth University (Classification of Adversarial Attacks Using Ensemble Clustering Approach)
08 Mai 2023
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