  The escalation of hazards to safety and hijacking of digital networks are
among the strongest perilous difficulties that must be addressed in the present
day. Numerous safety procedures were set up to track and recognize any illicit
activity on the network's infrastructure. IDS are the best way to resist and
recognize intrusions on internet connections and digital technologies. To
classify network traffic as normal or anomalous, Machine Learning (ML)
classifiers are increasingly utilized. An IDS with machine learning increases
the accuracy with which security attacks are detected. This paper focuses on
intrusion detection systems (IDSs) analysis using ML techniques. IDSs utilizing
ML techniques are efficient and precise at identifying network assaults. In
data with large dimensional spaces, however, the efficacy of these systems
degrades. correspondingly, the case is essential to execute a feasible feature
removal technique capable of getting rid of characteristics that have little
effect on the classification process. In this paper, we analyze the KDD
CUP-'99' intrusion detection dataset used for training and validating ML
models. Then, we implement ML classifiers such as Logistic Regression, Decision
Tree, K-Nearest Neighbour, Naive Bayes, Bernoulli Naive Bayes, Multinomial
Naive Bayes, XG-Boost Classifier, Ada-Boost, Random Forest, SVM, Rocchio
classifier, Ridge, Passive-Aggressive classifier, ANN besides Perceptron (PPN),
the optimal classifiers are determined by comparing the results of Stochastic
Gradient Descent and back-propagation neural networks for IDS, Conventional
categorization indicators, such as "accuracy, precision, recall, and the
f1-measure, have been used to evaluate the performance of the ML classification
algorithms.
