Fuzziness based semi-supervised learning approach for intrusion detection system

By Rana Aamir Raza, Xizhao Wang, Joshua Zhexue Huang, Haider Abbas, Yulin He. Information Sciences, 2017

544 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/191ff0c02d7fd09f7f85ca9a9651be51656ae4b4

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. Len Hamey
3. Mahmood Yousefi-Azar
4. Shehzad Khalid
5. Fannia Pacheco
6. Muhammad Munwar Iqbal
7. Mathieu Gineste
8. Yasir Saleem

In Computer Networks and Communications

1. Ansam Khraisat
2. Joarder Kamruzzaman
3. V. Varadharajan
4. U. Tupakula
5. Yuefei Zhu
6. Ernesto Exposito
7. Peter Vamplew
8. Mian Dai

Citations in the corpus

• Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey (2019, background): “The work in [146] analyses time based and host based features for intrusion detection in the network.”
• Enhanced Network Anomaly Detection Based on Deep Neural Networks (2018, methodology): “[17] to implement an IDS architecture using NSLKDD.”
• Autoencoder-based feature learning for cyber security applications (2017, methodology): “41% Proposed algorithm-(Experiment-2) [40] 84.”
• Building an efficient intrusion detection system based on feature selection and ensemble classifier (2019, methodology): “The highest detection accuracy is achieved by the proposed approach based on the experimental results on KDDTest+, which outperforms the other recent IDS techniques, including FSSL [9], FSSL-EL [34], and TSE-IDS [82].”
• Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, methodology): “[21] developed a new method by using the fuzziness approach based on semi-supervised learning for intrusion detection.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, unknown): “supervised learning based anomaly detection approach is presented in [304].”
• A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, background): “In the latest literature [23]–[25], all the researchers use the NSL-KDD as the benchmark dataset, which not only effectively solves the inherent redundant records problems of the KDD Cup 1999 dataset but also makes the number of records reasonable in the training set and testing set, in such a way t”
• Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, background): “Typical examples are graph-based model [76], self-training [77], co-training [78], generative model [79], low-density separation [80], and heuristic model [81].”
• Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “The work also uses SVM based classifier and achieves detection rate similar to (Chen et al., 2005).”
• A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “In this context, researchers have been proposing more sophisticated methods, such as those based on Machine-Learning techniques, for example, Support Vector Machines (SVMs) (Gan et al. 2013; Mukkamala et al. 2002), Artificial Neural Networks (Ashfaq et al. 2017; Ramadas et al. 2003), Fuzzy Logic (Br”