A novel SVM-kNN-PSO ensemble method for intrusion detection system

By Abdulla Amin Aburomman, Mamun Bin Ibne Reaz. Applied Soft Computing, 2016

445 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e8a3306cceffd3d8f19c253137fc93664e4ef8f6

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. Ansam Khraisat
2. Rania Kora
3. Gilberto Fernandes
4. Luiz Fernando Carvalho
5. Joel J. P. C. Rodrigues
6. Joarder Kamruzzaman
7. V. Varadharajan
8. U. Tupakula

In Biomedical Engineering

1. Ansam Khraisat
2. Rania Kora
3. Joarder Kamruzzaman
4. Saoussen Krichen
5. Peter Vamplew
6. Ammar Mohammed
7. M. Iqbal
8. I. Ahmad

In Computer Networks and Communications

1. Ansam Khraisat
2. Rania Kora
3. Joarder Kamruzzaman
4. V. Varadharajan
5. U. Tupakula
6. Saoussen Krichen
7. Suhuai Luo
8. Peter Vamplew

Citations in the corpus

• A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, unknown): “Detection of intrusion, anomalies and malware [163-167].”
• A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, background): “Aburomman and Reaz [104] proposed an ensemble ML based intrusion detection approach in which six k-NN and six SVM classifiers are trained over KDD’99.”
• Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “Stacking combines various classification via a meta-classifier (Aburomman & Reaz, 2016).”
• A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “Several existing IDSs are based on combining multiple techniques to improve the system performance (Aburomman and Reaz, 2016; Kavitha et al., 2012), whereas others are based on new techniques (Zhang et al., 2015), techniques inspired from existing ones (Lin et al., 2015), or inspired from nature…”
• A comprehensive review on ensemble deep learning: Opportunities and challenges (2023, methodology): “Traditional ensemble learning is based on integrating traditional machine learning models and applying them in different fields (Tsai et al., 2011; Abellán and Mantas, 2014; Catal et al., 2015; Da Silva et al., 2014; Aburomman and Reaz, 2016).”
• A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “The value of the k th data point affects the overall performance of the classifier [216].”
• Performance Comparison of Support Vector Machine, Random Forest, and Extreme Learning Machine for Intrusion Detection (2018, methodology): “Thus, Aburomman and Reaz [3] proposed an ensemble classifier method, which is a combination of PSO and SVM; this classifier outperformed other approaches with 92.”
• A comprehensive survey on network anomaly detection (2018, background): “Another classification method is Support Vector Machine (SVM) [111], which is also used in pattern recognition.”