Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issues of malicious activities taken place by intruders, hackers and attackers in the form of authenticity obstruction, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for the identification of suspicious activities, and generates alarm and indication in the presence of malicious threats and worms. The performance of IDS is improved by using different machine learning algorithms. In this paper, the Nesterov-Accelerated Adaptive Moment Estimation–Stochastic Gradient Descent (HNADAM-SDG) algorithm is proposed to determine the performance of Intrusion Detection Systems IDS. The algorithm is used to optimize IDS systems by hybridization and tuning of hyperparameters. The performance of algorithm is compared with other classification algorithms such as logistic regression, ridge classifier and ensemble algorithms where the experimental analysis and computations show the improved accuracy with 99.8%, sensitivity with 99.7%, and specificity with 99.5%.

The exponential pace of the spread of the digital world has served as one of the assisting forces to generate an enormous amount of information flowing over the network. The data will always remain under the threat of technological suffering where intruders and hackers consistently try to breach the security systems by gaining personal information insights. In this paper, the authors proposed the HDTbNB (Hybrid Decision Tree-based Naïve Bayes) algorithm to find the essential features without data scaling to maximize the model’s performance by reducing the false alarm rate and training period to reduce zero frequency with enhanced accuracy of IDS (Intrusion Detection System) and to further analyze the performance execution of distinct machine learning algorithms as Naïve Bayes, Decision Tree, K-Nearest Neighbors and Logistic Regression over KDD 99 dataset. The performance of algorithm is evaluated by making a comparative analysis of computed parameters as accuracy, macro average, and weighted average. The findings were concluded as a percentage increase in accuracy, precision, sensitivity, specificity, and a decrease in misclassification as 9.3%, 6.4%, 12.5%, 5.2% and 81%.

One of the main components of Intelligent Transportation Systems (ITS) is traffic congestion predictions. Traffic congestion in metropolitan road networks has a substantial impact on sustainability. Effective congestion management lowers prolonged travel delays and pollution emissions. However, because traffic patterns are complicated, dynamic, and non-linear, accurately predicting the spread of congestion is still difficult. With the introduction of Internet of Things (IoT) devices, useful datasets that can aid in the creation of sustainable and intelligent transportation for contemporary cities have been made available. This work presents an Intelligent Deep Learning Framework that combines Bi-LSTM with the TabTransformer architecture for congestion prediction in the Internet of Vehicles (IoV). The Bi-LSTM enhances temporal modeling of traffic flow dynamics, and the TabTransformer employs self-attention to derive high-quality feature representations. The proposed framework is tested on a large-scale dataset of over 317,112 traffic instances, incorporating a wide range of contextual features such as time indicators, route characteristics, and environmental factors. The model is validated using stratified k-fold cross-validation, and class imbalance is handled by class weighting in each fold. Experiential evaluation indicates that the proposed hybrid framework achieves 99.52% accuracy, outperforming several baseline models, including Decision Tree (92%), Extra Trees (94%), LSTM (86.94%), GRU (87.09%), Bi-LSTM (87.77%), CNN-LSTM (98.41%), and TabTransformer (97.32%). Compared to the existing algorithms, the proposed work outperformed with the parameters MSE, MAE, and R ² . The better performance exhibits that BiLSTM with Tab Transformer is well-suited to predict congestion patterns in road networks.

A single Information security is of pivotal concern for consistently streaming information over the widespread internetwork. The bottleneck flow of incoming and outgoing data traffic introduces the issue of malicious activities taken place by intruders, hackers and attackers in the form of authenticity desecration, gridlocking data traffic, vandalizing data and crashing the established network. The issue of emerging suspicious activities is managed by the domain of Intrusion Detection Systems (IDS). The IDS consistently monitors the network for identifica-tion of suspicious activities and generates alarm and indication in presence of malicious threats and worms. The performance of IDS is improved by using different signature based machine learning algorithms. In this paper, the performance of IDS model is determined using hybridization of nestrov-accelerated adaptive moment estimation –stochastic gradient descent (HNADAM-SDG) algorithm. The performance of the algorithm is compared with other classi-fication algorithms as logistic regression, ridge classifier and ensemble algorithm by adapting feature selection and optimization techniques