Real-Time Network Traffic Classification using Advanced Neural Embeddings

There are constantly new problems to overcome while analyzing network traffic in today's complex network systems, forcing researchers to develop new methodologies. We provide a unique technique for real-time network traffic categorization based on progressively complicated neural embeddings. Using the power of deep learning, this solution solves scalability, flexibility, and user privacy concerns while boosting the efficacy and accuracy of traffic classification. We compare our proposed technique to six current traffic classification strategies to illustrate its superiority. Our suggested method incorporates three key techniques: real-time traffic management, traffic data assessment, and the generation of neural embedding models. The act of arranging incoming network packets into flows and eliminating relevant information from those flows is known as traffic data preprocessing. The recommended methodology's remarkable accuracy of 98.5 percent is significantly more than the ordinary accuracy of 94.2 percent of conventional procedures. Furthermore, it has reduced rates of false positives (1.2% rate) and false negatives (2.3% rate) as compared to the gold standard of earlier approaches, which had rates of 6.8% and 8.9% for these two categories, respectively. Increased processing speed, scalability, adaptability, and privacy protection are just a few of the desirable characteristics exemplified by the proposed approach. Our research contributes considerably to the advancement of algorithms for categorizing network traffic, particularly in terms of real-time analysis. The suggested technique provides network administrators and security professionals with a highly precise and dependable tool for regulating and safeguarding network traffic in dynamic and changing situations.