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.

翻译：对数字网络安全的威胁和劫持是当今亟需应对的最严峻危险之一。已建立多种安全程序来跟踪和识别网络基础设施上的任何非法活动。入侵检测系统（IDS）是抵御和识别互联网连接及数字技术入侵的最佳方式。机器学习（ML）分类器越来越被用于将网络流量分类为正常或异常。结合机器学习的IDS能提高安全攻击检测的准确性。本文聚焦于使用ML技术对入侵检测系统进行分析。采用ML技术的IDS在识别网络攻击方面高效且精确。然而，在高维空间数据中，这些系统的效能会下降。相应地，有必要执行一种可行的特征去除技术，以剔除对分类过程影响微小的特征。本文中，我们分析了用于训练和验证ML模型的KDD CUP-'99'入侵检测数据集。然后，我们实现了逻辑回归、决策树、K近邻、朴素贝叶斯、伯努利朴素贝叶斯、多项朴素贝叶斯、XG-Boost分类器、Ada-Boost、随机森林、SVM、Rocchio分类器、Ridge、被动攻击分类器、人工神经网络（ANN）以及感知机（PPN）等ML分类器，并通过比较随机梯度下降和反向传播神经网络在IDS中的结果来确定最优分类器。使用传统分类指标，如准确率、精确率、召回率和F1值，来评估ML分类算法的性能。