NETWORK INTRUSION DETECTION USING NAÏVE BAYES

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A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions (2020, background): “KB [108,109,111,113] HTTP attacks (Buffer overflow, Shell attacks) [111], DoS, Probe, R2L [109] -It requires very few samples for training [112].”
The evaluation of Network Anomaly Detection Systems: Statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set (2016, methodology): “The five techniques used are Naïve Bayes (NB) (Panda & Patra, 2007), Decision Tree (DT) (Bouzida & Cuppens, 2006), Artificial Neural Network (ANN) (Bouzida & Cuppens, 2006; Mukkamala et al., 2005), Logistic Regression (LR) (Mukkamala et al., 2005), and Expectation-Maximization (EM) Clustering…”
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection (2016, methodology): “1) Misuse Detection: Panda and Patra [90] employed the Naïve Bayes classifier from the Weka package [11] and used the KDD 1999 data set for training and testing.”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, methodology): “NB is usually used in IoT to detect intrusion detection in the network layer [93], [94] and anomaly detection [95], [96].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “NB have been used for network intrusion detection [143], [144] and anomaly detection [145], [146].”