The continuous evolution of malicious programs (malware families) poses a significant threat to the security of computing environments. Machine learning and deep learning methods are gaining popularity in terms of effective classification among malware families. However, the multiclass malware classification problem, considering imbalanced complex Application Program Interface(API) call sequences, is an ongoing research challenge. This article proposes a data preprocessing technique called Continuous Duplicate Subsequence Removal (CDSR) to remove obfuscated API calls in dynamic API call sequences and address the horizontal imbalance problem in the dataset. Machine learning and deep learning models were trained using the Skip-gram API embeddings as a feature weight matrix to ensure improved classification capabilities. The proposed Ensemble Probability for Class Prediction (EPCP) technique is a novel method that ensembles the classification probabilities of several deep learning and machine learning models to ensure better classification results. Comparing the experimental findings of EPCP to the most well-known results on two benchmark dynamic API sequence datasets, MAL-API-2019 and APIMDS, reveals a notable performance improvement. Additionally, for MAL-API-2019, the average $$F_1$$ score, accuracy, and AUC scores are 67%, 68%, and 91%, while for APIMDS, they are 91%, 80%, and 97%, respectively.

Access to the internet inside moving vehicles with the proper speed and reliability creates several challenges to avail the service to user's satisfaction, unmanned car operation, traffic control, etc. A Vehicular ad-hoc Network (VANET) is familiar as the appropriate model for broadcasting data among running objects in a certain topographical range and behaves as an intelligent system for road safety and traffic management. A Greedy routing scheme is widely preferred to set a dedicated dynamical path from the source to the destination with the moving bodies that result in several unwanted intermediate nodes as the obstacle to the signal frequencies. The greater number of forwarding nodes in a certain range creates several issues such as process delay, packet loss, etc. leading to a slower and resource-consuming process that is not suitable as the next-generation data dissemination model for ad-hoc network. Hence, a novel Neuro-fuzzy forwarder scheme is proposed for the elimination of undesirable nodes for the achievement of better signal strength with improved performance. Furthermore, the comparative study demonstrates expected performance in path reliability with less latency in end-to-end connection after the node optimization with the proposed system.