Findings. The designed Aquila deer hunting optimization-enabled deep belief network technique achieved improved performance with an accuracy of 92.8%, a true positive rate of 92.8% and a true negative rate of 93.6.
Feb 5, 2024
Request PDF | DoS attack detection using Aquila deer hunting optimization enabled deep belief network | Purpose Denial-of-service (DoS) attacks develop ...
Purpose Denial-of-service (DoS) attacks develop unauthorized entry to various network services and user information by building traffic that creates ...
DoS attack detection using Aquila deer hunting optimization enabled deep belief network ... Deep Learning Model for Detecting Denial-of-Service Attacks.
DoS attack detection using Aquila deer hunting optimization enabled deep belief network. M Thomas, M BB. International Journal of Web Information Systems 20 (1) ...
International Journal of Web Information Systems: Vol. 20 Iss. 1
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DoS attack detection using Aquila deer hunting optimization enabled deep belief network · Merly Thomas, Meshram B.B.. Denial-of-service (DoS) attacks ...
DoS attack detection using Aquila deer hunting optimization enabled deep belief network. International journal of Web information systems, Jan 26, 2024.
It has a 92% attack detection rate and 20% false positive rate [19] . In 2021, Deepak Kshirsagar introduced weight based reduction method for security attacks ...
DoS attack detection using Aquila deer hunting optimization enabled deep belief network. ... using feature fusion and optimized Deep Neuro Fuzzy Network ...
Apr 22, 2021 · In this paper, an LSTM(Long Short Term Memory) based source-side DoS attack detection technique is proposed in order to keep high performance ...