An Effective Intrusion Detection System Using Homogeneous Ensemble Techniques

Abstract

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.

Research on intrusion detection method of party building network in colleges and universities based on machine learning

“…Explore the path to create a brand of party building work that closely meets the strategic needs of rural revitalization: relying on the concept of "holding high the banner of party building, building a solid red foundation, building a 'digital and intelligent courtyard', and assisting rural revitalization", focusing on the grassroots party building work of college students Let's take advantage of the leading and promoting role of the red "1+1" activity to explore innovative ways to create a brand for party building work. Focus on studying how to combine the grassroots party building work of college students with the red "1+1" activities to create a distinctive brand of party building work and enhance the influence and cohesion of party building work [2]. There is a lot of private data in grassroots party building network information, and the construction of network security systems needs to be strengthened to make the detection of malicious attacks a top priority for network security.…”