Feature Selection and Intrusion Detection in Cloud Environment based on Machine Learning Algorithms
This work addresses the need for efficient intrusion detection in cloud computing, but it appears incremental as it builds on existing methods without specifying novel breakthroughs.
The study tackled the problem of detecting intrusions in cloud environments by proposing a new method that combines feature selection with machine learning algorithms, resulting in increased detection accuracy.
Characteristics and way of behavior of attacks and infiltrators on computer networks are usually very difficult and need an expert In addition; the advancement of computer networks, the number of attacks and infiltrations are also increasing. In fact, the knowledge coming from an expert will lose its value over time and must be updated and made available to the system and this makes the need for the expert person always felt. In machine learning techniques, knowledge is extracted from the data itself which has diminished the role of the expert. Various methods used to detect intrusions, such as statistical models, safe system approach, neural networks, etc., all weaken the fact that it uses all the features of an information packet rotating in the network for intrusion detection. Also, the huge volume of information and the unthinkable state space is also an important issue in the detection of intrusion. Therefore, the need for automatic identification of new and suspicious patterns in an attempt for intrusion with the use of more efficient methods Lower cost and higher performance is needed more than before. The purpose of this study is to provide a new method based on intrusion detection systems and its various architectures aimed at increasing the accuracy of intrusion detection in cloud computing. Keywords : intrusion detection, feature Selection, classification Algorithm, machine learning, neural network.