A novel hybrid intrusion detection method integrating anomaly detection with misuse detection

By Gisung Kim, Seungmin Lee, Sehun Kim. Expert systems with applications, 2014

601 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/87a75d7cd83e695ab4de1160519ae92acacef862

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Citations in the corpus

Deep Learning Approach Combining Sparse Autoencoder With SVM for Network Intrusion Detection (2018, unknown): “Various machine learning algorithms or approaches are applied in NIDS to detect and distinguish between normal traffic and anomalies or attacks in network traffic; these approaches include decision tree [11], k-nearest neighbor (K-NN) [12], naïve Bayes network [13], [14], SOM [15], [16], SVM, and ar”
CANN: An intrusion detection system based on combining cluster centers and nearest neighbors (2015, methodology): “[17] and Nadiammai and Hemalatha [23] obtained times of 11.”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, background): “In an expert system, the rules are usually manually defined by a knowledge engineer working in collaboration with a domain expert (Kim et al., 2014).”
A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “In context of intrusion detection DTs have potential for use as classifier [125,126].”
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review (2021, methodology): “On network trafﬁc, data clas-siﬁcation algorithms such as Support Vector Machine (SVM) [111], [112], Decision Tree [113], [114], KNN [100] have given important and powerful results.”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, unknown): “…Zulkernine 2005; Zhao et al. 2013), Random Forests (Hasan et al. 2016; Jiong Zhang et al. 2008; Malik et al. 2015; Singh et al. 2014), Clustering (Li et al. 2015; Lin et al. 2015; Mai and Park 2016), and ensemble of methods (Comar et al. 2013; Dubey and Dubey 2015; Ji et al. 2016; Kim et al. 2014).”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, methodology): “The anomaly detection approach is adopted in this proposal due to its efficiency in detecting unknown attacks (Kim et al., 2014).”
Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm (2016, methodology): “1, some other detection methods [18], [19], [20], [21], [22], [23] were evaluated using other intrusion detection datasets, such as NSL-KDD [24] and Kyoto 2006+ [25].”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “Various machine learning techniques were applied for misuse-based detection [127], [384]–[388], anomaly-based detection [389]–[392] and hybrid-based detection [393]–[397].”
A Detailed Investigation and Analysis of Using Machine Learning Techniques for Intrusion Detection (2019, methodology): “[28] proposed multiple classifier methods which hierarchically integrates misuse detection model with anomaly”
Intrusion detection model using fusion of chi-square feature selection and multi class SVM (2016, background): “Comparing the run time with the most recent related works, (ie. testing times) Lin et al. (2015), Kim et al. (2014) and Nadiammai and Hemalatha (2014) obtained times of 13, 11.2 and 8 s which had the complete KDD data set containing about 4,00,000 data packets whereas our experiments deal with…”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “A DT is used as a main classifier or collaborative classifier with other ML classifiers in security applications, such as intrusion detection [118, 119].”