Enhancing intrusion detection with feature selection and neural network

Abstract

Intrusion detection systems are widely implemented to protect computer networks from threats. To identify unknown attacks, many machine learning algorithms like neural networks have been explored for anomaly based detection. However, in real-world applications, the performance of classifiers might be fluctuant with different data sets, while one main reason is due to some redundant or ineffective features. To mitigate this issue, this study investigates some feature selection methods and introduces an ensemble of Neural Networks and Random Forest to improve the detection performance. In particular, we design an intelligent system that can choose an appropriate algorithm in an adaptive way. In the evaluation, we study the feasibility of our approach with KDD99 data set and evaluate its practical performance with a real data set collected from a Honeynet environment. The experimental results indicate that as compared with similar approaches, our approach can overall provide a better result, through identifying important and closely related features.

Progressive selection‐channel networks for image steganalysis

“…However, the semantic meaning of the modification probability map is quite weak, and it is difficult to generate precise maps directly using simple backbone networks. To this end, we design a multistage progressive network to estimate the probability maps in an "easy-to-hard" learning paradigm, whose recursive computation process [37][38][39][40] also significantly reduces the network parameters for fast inference.…”

Improving the performance of the intrusion detection systems by the machine learning explainability

“…In recent years, the researchers also use deep learning techniques to power the IDSs. For instance, in the work of Diro and Chilamkurti (2018), the authors use the multi-layer feed-forward neural networks for the task of classification, while Wu and Li (2021) used the ensemble power of random forest (Kam, 1995) and neural networks for feature selection to improve the performance of the model. The ensemble methods are used in other works such as Folino et al (2021) or Jayanthi et al (2021).…”

Application of visual mechanical signal detection and loading platform with super‐resolution based on deep learning

“…9 Deep learning is strongly linked to computational statistics, which concentrate to the use of computers for prediction. 10 The research of algorithms optimization provides means, theories, and application areas for the field of deep learning. 11 The model of deep learning is superior to the mathematical model of statistics and econometrics, which is an important reason for data mining to become one of the research fields of deep learning.…”