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 and/or 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 494,021 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, K-nearest 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 Short-Term 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 ROC-AUC on an 80-20 stratified train test partition. Experimental results showed that the ensemble of Random Forest classifiers has the best overall performance with an overall accuracy of 99.98% and a near-perfect value of ROC-AUC (99.99%). Decision Tree obtained a value of 99.97% in terms of accuracy and a near-perfect value of 99.98% for the ROC-AUC measure. The best deep learning models were the ANN (accuracy of 99.95%) and the LSTM (accuracy of 99.95%) with their nearest architectures, followed by the unsupervised model of Autoencoder (accuracy of 98.94%) 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 learning-based 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 real-time analysis of live traffic with the trained models.

Keywords : Cybercrime Detection, ML, DL, CNN, LSTM, Autoencoder, KDD Cup 99, Anomaly Detection.

Authors : Palli Dhilleswari , Jyothi Musireddy , P Vamsi Krishna Raja

Title : Cybercrime Detection Approaches using Machine Learning and Deep Learning Techniques

Volume/Issue : 2026;8(4 (July - September))

Page No : 17 - 27

Neuro Aid is a groundbreaking machine learning solutions to early detection of Parkinson's disease, which is tackling an urgent healthcare need for millions of people around the world. If not diagnosed until symptoms such as a slight tremor in one hand and stiffness in the body appear, then Parkinson's gradually worsens over time. Though a predictive analytics system has been developed which merges the concepts of K-means clustering, Decision Trees and Support Vector Machines (SVM) to provide insights on patient information, there is a need to improve the accuracy of the prediction. In this context, the use of Random Forest classification method is proposed, looking into an expectation to achieve more than 90% accuracy. In addition to these sophisticated classification methods, there are also ways to de-noise data and models to make them more stable and effective. Next to these sophisticated classification methods, there are also ways to de-noise data and models to assure their stability and efficacy. By normalizing the values of each feature using MinMaxScaler, you can reduce the impact of scale differences among the features, which results in a more balanced contribution from each feature. The Parkinson's framework seeks to obtain a minimal error rate by combining advanced machine learning methodologies to make it more useful in research and clinical practice. The combined effectiveness of this approach not only fosters progress in Parkinson's research and knowledge but also highlights the potential impact of machine learning on solving intricate healthcare problems.

Keywords : Fraud Detection, Gmail Security, Email Fraud, Spam Detection, Phishing Emails ,Cybersecurity.

Authors : Adimalla Rama Rao

Title : Neuro Aid : A Machine Learning Approach To Parkinson’s Disease Detection In Early Stages

Volume/Issue : 2026;8(4 (July - September))

Page No : 8 - 16

Email fraud has become one of the most common cyber threats, causing financial losses and compromising sensitive information. Traditional spam filtering techniques often struggle to identify sophisticated phishing and fraudulent emails. This project presents an intelligent Gmail fraud detection system that combines machine learning techniques with Reddit-inspired ranking algorithms to improve the accuracy of email classification. The system analyses email content using Natural Language Processing (NLP), extracts textual and behavioural features such as suspicious links, keywords, sender information, and message patterns, and then applies a trained machine learning model to classify emails into Ham, Spam, or Fraud categories. To enhance explainability and ranking effectiveness, a Reddit-based scoring mechanism is incorporated to assign risk scores according to the presence of suspicious characteristics and Community-inspired relevance metrics. The proposed system provides not only fraud predictions but also detailed explanations highlighting the factors that influenced each decision. A user-friendly Flask web interface enables real-time email analysis, probability visualization, and feature-based reasoning. Experimental results demonstrate that the integration of machine learning and ranking algorithms significantly improves fraud detection performance, helping users identify malicious emails more effectively and strengthening email security against evolving cyber threats.

Keywords : Fraud Detection, Gmail Security, Email Fraud, Spam Detection, Phishing Emails ,Cybersecurity.

Authors : Md Bushra , Ch Sravani , Sk Akbar

Title : Fraud Detection GMails Using Reddit Ranking Algorithms

Volume/Issue : 2026;8(4 (July - September))

Page No : 1 - 7