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- W4206103323 abstract "Port scanning attack is a common cyber-attack where an attacker directs packets with diverse port numbers to scan accessible services aiming to discover open/weak ports in a network. Hence, several detection/prevention techniques were developed to frustrate such cyber-attacks. In this paper, we propose a new inclusive discovery scheme that evaluate five supervised machine learning classifiers, including logistic regression, decision trees, linear/quadratic discriminant, naïve Bayes, and ensemble boosted trees. We compared the performance of these models via detection accuracy using a contemporary dataset for port scanning attacks (PSA-2017). As a result, the best performance results have recorded for logistic regression based detection scheme with 99.4%, 99.9%, 99.4%, 99.7%, and 0.454 µSec registered for accuracy, precision, recall, F-score, and detection overhead. Lastly, the comparison with existing models exhibited the proficiency and advantage of our model with enhanced attack discovery speed." @default.
- W4206103323 created "2022-01-26" @default.
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- W4206103323 date "2021-12-06" @default.
- W4206103323 modified "2023-10-01" @default.
- W4206103323 title "Detecting Port Scan Attacks Using Logistic Regression" @default.
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- W4206103323 doi "https://doi.org/10.1109/isaect53699.2021.9668562" @default.
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