International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Palli DhilleswariJyothi MusireddyP Vamsi Krishna Raja 8 4 (July - September) 2026 08 July-September enCybercrime DetectionMLDLCNNLSTMAutoencoderKDD Cup 99Anomaly Detection. As a technology that connects even more systems and services to the Internet with each passing day cybercrime has grown out of control globally Significant problems with traditional IDSs have been uncovered when they are exposed to new attack vectors and advanced evasion strategies not contained in their rule sets andor signature databases This paper contains comprehensive research and empirical analysis of 9 machine learning and deep learning algorithms for binary classification of network traffics into normal or malicious network traffic The benchmark dataset used in this study is the KDD Cup 1999 dataset which consists of around 494021 network connection records derived from a relatively large network characterized with 41 different features of continuous discrete and categorical attribute types Its performance was compared with five classical machine learning algorithms namely decision tree random forest support vector machine with a linear kernel Knearest neighbours and gaussian naive bayes Furthermore four deep learning architectures were studied a fully connected Artificial Neural Network a one dimensional Convolutional Neural Network CNN a Long ShortTerm Memory LSTM recurrent network and an Autoencoder based model for anomaly detection AD These models were thoroughly validated with accuracy precision recall F1 score and ROCAUC on an 8020 stratified train test partition Experimental results showed that the ensemble of Random Forest classifiers has the best overall performance with an overall accuracy of 9998 and a nearperfect value of ROCAUC 9999 Decision Tree obtained a value of 9997 in terms of accuracy and a nearperfect value of 9998 for the ROCAUC measure The best deep learning models were the ANN accuracy of 9995 and the LSTM accuracy of 9995 with their nearest architectures followed by the unsupervised model of Autoencoder accuracy of 9894 with the reconstruction error thresholding The results highlight that the ensemble tree based methods are still highly effective for the structured network traffic classification cases and a Deep learningbased approach presents an even more competitive solution with the task of feature selection through raw data which opens doors of opportunity to extract features by various means from raw data for these cases In addition the model was deployed practically with a RESTful API built using FastAPI allowing for the realtime analysis of live traffic with the trained models Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2026 All Rights Reserved