Security of edge devices using hybrid machine learning approach
Budget: ₹750 – ₹1,250 INR
Edge computing is much vulnerable to various types of security attacks. Various approaches for
detecting security threats in edge computing have been presented. Machine learning has been
proposed by various researchers for attack detection. The schemes which have already been proposed have
various phases of data preprocessing, feature extraction, and classification. It is analyzed that machine
learning algorithms like SVM, KNN, DT, etc. give low accuracy for vulnerability detection. A novel or hybrid
machine learning algorithm is required which increases accuracy for vulnerability detection in edge
computing.
I have the flow of the project and the structure of the project the requirement is to implement it.
Files :
1. Dataset - KDD test and train data
2. pseudo code
3. flow chart
4. sequence diagram
detecting security threats in edge computing have been presented. Machine learning has been
proposed by various researchers for attack detection. The schemes which have already been proposed have
various phases of data preprocessing, feature extraction, and classification. It is analyzed that machine
learning algorithms like SVM, KNN, DT, etc. give low accuracy for vulnerability detection. A novel or hybrid
machine learning algorithm is required which increases accuracy for vulnerability detection in edge
computing.
I have the flow of the project and the structure of the project the requirement is to implement it.
Files :
1. Dataset - KDD test and train data
2. pseudo code
3. flow chart
4. sequence diagram