Photonic Reservior Computing or Real-Time Malware Detection in Encrypted Network Traffic

Abstract

The development of encrypted network traffic has brought huge improvements to user privacy but has paradoxically introduced huge difficulties in the detection of real time malware attack traffic, since the traditional methods used to inspect payloads are rendered useless. Existing machine learning and deep learning models in the field of encrypted traffic analysis are, in particular, plagued by high computation costs, latency problems and poor scalability when deployed to high throughput networks. Photonic Reservoir Computing (PRC) is a new paradigm based on exploiting the nonlinear dynamics of optical systems that could represent an interesting alternative to the current paradigm, enabling ultra-fast, low-latency and energy-efficient data processing. This study investigates the use of PRC in identifying malware in the encrypted traffic by encoding flow-level statistical features into the photonic reservoirs for classification. The results show that PRC not only offers competitive detection accuracy in comparison to standard deep learning techniques but also shows better real-time performance and energy efficiency characteristics, and is suitable for deployment in large scale in Internet Service Providers (ISPs), critical infrastructures and IoT ecosystems. The results of this research make PRC a promising transformative strategy for next-generation intrusion detection systems to meet the dual challenges of privacy and security.