An Enhanced DNN Model for Cyber Attack Detection using Seagull Adapted Elephant Herding Optimization Algorithm
Keywords:
Cyber Attack Detection, Deep Neural Network, Seagull Adapted Elephant Herding Optimization Algorithm, Feature Selection, Categorization Accuracy, Network SecurityAbstract
Life today is significantly more comfortable thanks to numerous digital devices and the internet to support them. Every good thing has a bad side, and the same is true in today's digital world. The internet has made a beneficial difference to our lives today, but it also presents a significant difficulty in protecting private information. This gives rise to cyberattacks. Attack Detection is a major challenge in network security. Traditional Gradient Boosting Algorithms Such as GBM, XGBoost, LightGBM, CatBoost Algorithm Performs Poor Detection of Different attacks, such as malicious software attacks, phishing attacks, and denial-of-service attacks. This paper introduces a novel DNN- Seagull Adapted Elephant Herding Optimization Algorithm (DNN- SAEHOA) to Improve Detection Attacks automatically with Publicly Available Input dataset UNSW NB-15 with Variance Threshold (VT) is a simple approach to feature selection. It removes all features whose variance cannot meet a defined threshold. Improve accuracy.
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