“…According to the input metrics detailed in [23], malicious network flows can be outlined in four principal categories (Login, Inputs, Downloads and Geo-location), depending on specific missions for attackers. In general, well-known SL algorithms, including Logistic Regression (LG) [24], Support Vector Machine (SVM) [25], Artificial Neural Networks (ANN) [26], Decision Trees (DT) [27], Random Forest (RF) [27], Bayesian Networks (BN) [28], and Deep Learning (DL) Networks [29] have been fitted to overcome different menaces that directly depend on the conditions, circumstances, and settings in which botnet and IDS attacks are monitored and framed. Remarkable evidence is taken from IRC connections, P2P bots, DNS queries, anomalous traffic footprints, blacklisted IP addresses, irregular or malformed packet lengths, and abnormal intervals of multiple requests and responses over various network protocols [22].…”