Modeling intrusion detection system using hybrid intelligent systems

By Sandhya Peddabachigari, Ajith Abraham, Crina Grosan, Johnson P. Thomas. Journal of Network and Computer Applications, 2007

520 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/9174e86478731fb85d8e9adbbe7f98c1cf1d292c

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. Mohammad Ali Salahuddin
2. Monowar Hussain Bhuyan
3. Cherukuri Aswani Kumar
4. Sara Ayoubi
5. Mamun Bin Ibne Reaz
6. Chih-Fong Tsai
7. K. Rhee
8. Shih-Wen Ke

In Computer Networks and Communications

1. Mohammad Ali Salahuddin
2. Monowar Hussain Bhuyan
3. Cherukuri Aswani Kumar
4. Sara Ayoubi
5. Chih-Fong Tsai
6. K. Rhee
7. Shih-Wen Ke
8. Raouf Boutaba

In Signal Processing

1. Monowar Hussain Bhuyan
2. Cherukuri Aswani Kumar
3. Chih-Fong Tsai
4. K. Rhee
5. Shih-Wen Ke
6. Marco Comuzzi
7. Wei‐Chao Lin
8. Bayu Adhi Tama

Citations in the corpus

• 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 comprehensive survey on machine learning for networking: evolution, applications and research opportunities (2018, methodology): “[354] propose an IDS that leverages an ensemble of DT, SVM with polynomial kernel based function, and hybrid DTSVM to detect various different cases of misuse.”
• 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.”
• Machine Learning Techniques for Anomaly Detection: An Overview (2013, unknown): “[41], proposed decision trees (DT) and support vector machines (SVM) as intrusion-detection model.”
• Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, methodology): “, 2010) and hybrid intelligent system (Peddabachigari et al., 2007) are explored to build an IDS.”
• CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[26] DT + SVM KDD-Cup 99’ Anomaly and misuse detection Accuracy SVM, DT No”
• A survey of multiple classifier systems as hybrid systems (2014, unknown): “The interesting proposed hybrid intrusion detection was presented in [186], where decision trees and support vector machines are combined as a hierarchical hybrid intelligent system model.”
• A novel SVM-kNN-PSO ensemble method for intrusion detection system (2016, background): “The dea of combining responses produced by multiple classifiers into a single response s known as the ensemble approach [2].”
• Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “[199] 2007 2 C N P Numeric KDDcup99 C1 DT-SVM hybrid model Zhang et al.”