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US Patent 11075934 Hybrid network intrusion detection system for IoT attacks

Patent 11075934 was granted and assigned to King Abdulaziz University on July, 2021 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
King Abdulaziz University
King Abdulaziz University
Current Assignee
King Abdulaziz University
King Abdulaziz University
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
11075934
Date of Patent
July 27, 2021
Patent Application Number
17190831
Date Filed
March 3, 2021
Patent Citations
‌
US Patent 10848448 Spam filtering in multimodal mobile communication
Patent Citations Received
‌
US Patent 11785024 Deploying neural-trojan-resistant convolutional neural networks
‌
US Patent 11550923 Systems and/or methods for static-dynamic security testing using a test configurator to identify vulnerabilities and automatically repair defects
Patent Primary Examiner
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Jung W Kim
Patent abstract

A Deep Learning Dendritic Cell Algorithm (DeepDCA) is employed in an intrusion detection system (IDS) and method. The framework adopts both a Dendritic Cell Algorithm (DCA) and a Self Normalizing Neural Network (SNN). The IDS classifies interned of things (IoT) intrusion, while minimizing false alarm generation, and it automates and smooths the signal extraction phase which improves the classification performance. The IDSselects the convenient set of features from the IoT-Bot dataset, and performs their signal categorization using the SNN. Experimentation demonstrated that the IDS with DeepDCA performed well in detecting IoT attacks with a high detection rate demonstrating over 98.73% accuracy and a low false-positive rate. Also, IDS was capable of performing better classification tasks than SVM, NB, KNN and MLP classifiers.

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