A New Method for Intrusion Detection in Computer Networks using Computational Intelligence Algorithms

This paper introduces a novel and integrated approach to intrusion detection in computer networks that makes use of the benefits of both abuse detection and anomaly detection techniques. The proposed method combines anomaly detection and abuse detection technologies to enhance intrusion detection functionality. The intrusion detection system is implemented using a set of algorithms and models in the proposed approach. The frog jump algorithm has been utilized to choose the system's ideal input attributes. The decision tree is utilized in this system's abuse detection portion. Support vector machines or basic-radial neural network models have been utilized to find anomalies in this system. In the process of training neural networks, other techniques like particle swarm or genetic optimization are also utilized. The NSL-KDD dataset was used the experiment, and the findings were published. These findings demonstrate that, in comparison to using only anomaly or abuse detection, the proposed approach can increase the effectiveness of intrusion detection in the network. Additionally, a model that uses the frog leap algorithm for feature selection and classification and combines decision tree and support vector machine techniques with ten chosen input features has a detection rate of 98.2%. This is true despite the fact that the detection rates of the systems trained using comparable data in prior studies with 33 and 14 selected input features to the trainer have been 83.2% and 84.2%, respectively. Additionally, the algorithm execution performance increases up to 29 times faster than the aforementioned approaches when the intrusion detection rate is maintained at the level of other competing methods that were simulated in this work.

@article{HAO2023,
  title     = {A New Method for Intrusion Detection in Computer Networks using Computational Intelligence Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Yanrong HAO and Shaohui YAN},
  doi       = {10.14569/IJACSA.2023.01409122},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409122}
}