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$350
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Description
Experience Level: Expert
My project is about anomaly detection in network using machine learning algorithms. The problem is a multiclass classification. For this experiment we are using NSL-KDD dataset. you may have heard about this dataset. I used SVM, RF and KNN to build a multiclass classifier. The classifiers accuracy are not good between 56% to 73%. So I have decided to optimize the algorithms parameter to produce a better result. The accuracy must go up to 90%. For KNN we can optimize the k parameter or we can optimize the to find the best neighbors in a very short period of time. For SVM the parameters Gamma and Cost must be optimized. For RF I have not decided yet. The optimization will be done by Particle Swarm Optimization and Ant Colony optimization algorithms The parameters that determine overall performance are: Classification accuracy (confusion matrix), Time consumption (time to build the classifier and time to classify unknown attacks), Resource consumption (how much memory, CPU will be used during experiment). And detection rate. I want detection rate above 90%.
Classification is multiclass classification.
Classification is multiclass classification.
Tahira K.
100% (1)Projects Completed
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Freelancers worked with
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Projects awarded
44%
Last project
25 Apr 2024
South Africa
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