Review Article
Comparing and Analyzing Applications of Intelligent Techniques in Cyberattack Detection
Table 3
Comparative study among different machine learning techniques.
| Technique | Principle | Parameters | Advantages | Limitations |
| k-means | Find out k points called centers that are evaluated as the sum of the distances of all points to their respective cluster centers | Cluster center location | High computation, produce closet clusters | Calculation of K is a very tough task for a fixed number of clusters. The dissimilarity, the initial and final cluster partition |
| K-nearest neighbor | The input consists of k-closest training of the feature space by using instance-based learning | Class of nearest neighbor | It is easy to implement, less complex | Difficult to deal with arbitrary attributes |
| Support vector machine | The mapping of input data to the high-dimension space and also dealing with linearly separated data for classification | Features of high dimension | Having high accuracy, flexible and robust in dealing with errors | Takes large time for training, complex to handle learned function (weights) |
| Hidden Markov model | It is a statistical or sequence-based model that consists set of states, transitions represent the set of possible positions | Pixels in a vision-based input | High-scalable model and easy to understand | Many assumptions about the data. A large number of parameters required to be set. Highly needed training data |
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