A Human-in-the-Loop Anomaly Detection Architecture for Big Traffic Data of Cellular Network

Abstract

In the era of mobile big data, smart mobile devices have become an integral part of our daily life, which brings many benefits to the digital society. However, their popularity and relatively lax security make them vulnerable to various cyber threats. Traditional network traffic analysis techniques utilizing pattern matching and regular expressions matching algorithms are becoming insufficient for mobile big data. Network traffic anomaly detection is an effective method to replace traditional methods. Network traffic anomaly detection can solve many new challenges brought by future network and protect the security of network. In this article, we propose a streaming network framework for mobile big data, referred to as SNMDF, which provides massive data traffic collection, processing, analysis, and updating functions, to cope with the tremendous amount of data traffic. In particular, by analyzing the specific characteristics of anomaly traffic data from flow and user behavior, our proposed SNMDF demonstrates its capability to offer real data-based advice to address new challenges for future wireless networks from the viewpoints of operators. Tested by real mobile big data, SNMDF has proven its efficiency and reliability. Furthermore, SNMDF is accessed for the digital twin of the space Internet, which validates that it can be generalized to other environments with massive data traffic or big data.

Keywords

• Cyber threats
• network traffic
• network security
• big data
• SNMDF