Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction error limits. This approach allows the automatic learning of threat traits as they change, without needing someone to do it by hand. The system was tested on the UNSW-NB15 data set, picked because it realistically includes many kinds of attacks, like Fuzzers, Shellcode, and Worms. Tests show that IntrusionNet gets an accuracy of 98.80% and an F1-score of 0.985, doing better than other systems, especially with less common attack types. Also, tests using Precision-Recall (PR) analysis and False Positive Rate (FPR) measurements prove that the model handles class imbalance well, which is key for real-world security. The suggested system can be scaled up easily and performs calculations fast, making it a possible key part of real-time detection in Security Information and Event Management (SIEM) systems.

The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition. To bridge this gap, the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition. The emphasis on practical data for system training enhances accuracy. The research further conducts a comparative analysis, pitting state-of-the-art (SOTA) deep learning models, including EfficientNetB0, VGG16, ResNet50, DenseNet201, InceptionV3, and Xception, against a custom Convolutional Neural Networks (CNN) model named “Our CNN.” The results showcase the superior performance of “Our CNN,” with a test accuracy of 99.97% and an outstanding F1 score of 99.95%. These metrics underscore its potential for automating city name recognition, particularly in postal services. The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures. It encourages future research avenues, including dataset expansion, algorithm refinement, exploration of recurrent neural networks and attention mechanisms, real-world deployment of models, and extension to other regional languages and scripts. These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.