Intrusion detection using an ensemble of intelligent paradigms

By Srinivas Mukkamala, Andrew H. Sung, Ajith Abraham. Journal of Network and Computer Applications, 2005

433 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/3f1fdab57ba96a1aca3c54eddbbc5ccf676f575d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Zhiyuan Tan
2. S. Maybank
3. Priyadarsi Nanda
4. V. Varadharajan
5. Xiangjian He
6. U. Tupakula
7. K. Rhee
8. Asaf Shabtai

In Computer Networks and Communications

1. Zhiyuan Tan
2. S. Maybank
3. Priyadarsi Nanda
4. V. Varadharajan
5. Xiangjian He
6. U. Tupakula
7. K. Rhee
8. Asaf Shabtai

In Signal Processing

1. S. Maybank
2. V. Varadharajan
3. U. Tupakula
4. K. Rhee
5. Asaf Shabtai
6. Yuval Elovici
7. Marco Comuzzi
8. Wei Hu

Citations in the corpus

• A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “If we consider the case of multiple classifiers without feature selection, Ensemble of ANN, SVM and MARS [124] is achieving the highest detection rate of 99.”
• An Implementation of Intrusion Detection System Using Genetic Algorithm (2012, background): “There are several papers [12][13][14][15] related to IDS which has certain level of impact in network security.”
• Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, background): “[6] investigated the possibility of assembling various learningmethods, including Artificial Neural Networks (ANN), SVMs and Multivariate Adaptive Regression Splines (MARS) to detect intrusions.”
• The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set (2016, neutral): “…Naïve Bayes (NB)(Panda & Patra, 2007), Decision Tree (DT) (Bouzida & Cuppens, 2006), Artificial Neural Network (ANN) (Bouzida & Cuppens, 2006; Mukkamala, Sung, & Abraham, 2005), Logistic Regression (LR) (Mukkamala et al., 2005), and Expectation-Maximisation (EM) Clustering (Sharif,…”
• AdaBoost-Based Algorithm for Network Intrusion Detection (2008, methodology): “It was developed for intrusion detection evaluation by the Defense Advanced Research Projects Agency [19].”
• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection (2018, methodology): “In [20], the authors evaluated the ANN, among other classification algorithms, in the task of network intrusion detection, and proposed a solution based on an ensemble of classifiers using connection-based features.”
• Applying convolutional neural network for network intrusion detection (2017, methodology): “41 features were composed of 34 continuous and 7 discrete valued and grouped into basic features in the range [1-9], content features in the range [10-22], traffic features with in a time window of in the range [23-31] and features based on host in the range [3241].”
• Support vector machines : theory and applications (2005, neutral): “Support Vector Machines (SVMs) [1, 2, 3] have been widely applied to pattern classification problems [4, 5, 6, 7, 8] and nonlinear regressions [9, 10, 11].”
• TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent Anomaly-Based Intrusion Detection System (2019, background): “, bagging [23]–[25], boosting [25], [26], voting [27]–[29], random forest [10], [30], and other ensemble approaches [31]–[34].”
• A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, background): “[65] 255 misuse DARPA 1998 Ensemble – Random Forest Gharibian and Ghorbani [63] 19 misuse KDD 1999 Ensemble – Random Forest Bilge et al.”