Random-Forests-Based Network Intrusion Detection Systems

By Jiong Zhang, Mohammad Zulkernine, Anwar Haque. IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews), 2008

564 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/e33d17a81fb13dae0a76c6d507630c91a9a6e082

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Citations in the corpus

Network Anomaly Detection: Methods, Systems and Tools (2014, unknown): “[200] 2008 2 C N P Numeric KDDcup99 C1 RFIDS model Aydin et al.”
A Survey of Random Forest Based Methods for Intrusion Detection Systems (2018, methodology): “However, Jiong Zhang et al. (2008) show that Random Forest performs slightly better when using oversampling and downsampling to reduce the training dataset imbalance.”
A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, methodology): “RELEVANT WORK In prior studies, a number of approaches based on traditional machine learning, including SVM [10], [11], K-Nearest Neighbour (KNN) [12], ANN [13], Random Forest (RF) [14], [15] and others [16], [17], have been proposed and have achieved success for an intrusion detection system.”
A Survey of Network-based Intrusion Detection Data Sets (2019, background): “, [91] or [92]) achieve high detection rates in data sets from the category other which is an indicator that the calculated attributes are promising for intrusion detection.”
A hybrid intelligent system for medical data classification (2014, background): “It has shown good results in many applications, which include automatic intrusion detection systems (Zhang et al., 2008) and medical data classification (Wu, Ye, Liu, & Ng, 2012).”
A network intrusion detection system based on a Hidden Naïve Bayes multiclass classifier (2012, methodology): “…& Cunningham, 2000; Zhang, 2001), fuzzy logic (Bridges & Vaughn, 2000), the hidden Markov model (Bo, Hui-Ye, & Yu-Hang, 2002), the random forest model (Zhang, Zulkernine, Haque, 2008), data mining (Lee, Stolfo, & Mok, 1999; Wu & Yen, 2009) and Bayesian analysis (Bar-bara, Wu, & Jajodia, 2001).”
Evaluation and Design of Robust Neural Network Defenses (2018, background): “And in the space of intrusion detection, there is a similar story of research on detecting intrusions [137, 138, 54, 47] (and again, even using neural networks [71]) and evading intrusion detection [120, 30].”
BAT: Deep Learning Methods on Network Intrusion Detection Using NSL-KDD Dataset (2020, background): “In [20], the authors propose a new framework to combine the misuse and the anomaly detection in which they apply the random forests algorithm.”
Mining data with random forests: A survey and results of new tests (2011, methodology): “Dj of RF [87,88] Task: variable selection in intrusion detection using a large balanced data set with 41 categorical and continuous variables.”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “Thus, the researchers further proposed the RF-based hybrid detection for both misuse and anomaly in the network [125], [126].”
A GA-LR wrapper approach for feature selection in network intrusion detection (2017, background): “It has the capability to deal efficiently with large datasets having many features (Zhang et al., 2008).”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “A few studies [35], [42], [52], [62] described their systems as”
Deep Learning Approach for Intelligent Intrusion Detection System (2019, methodology): “Reference [21] discussed random forest techniques in misuse detection by learning patterns of intrusions, anomaly detection with outlier detection mechanism, and hybrid detection by combining both the misuse and anomaly detection.”