Flow classification employs machine learning techniques to identify attacks on computer
networks. This classification relies on quantitative features that synthesize the information of …

Investigating network flows is an approach of detecting attacks by identifying known
patterns. Flow statistics are used to discover anomalies by aggregating network traces and …

Accurate traffic classification is important for various network activities such as accurate
network management and proper resource utilization. Port-based approaches, deep packet …

Recent in-network flow classification methods are able to run within the data plane of
network switches allowing intrusion detection at linerate. Although this enables fine-grained …

Detecting and classifying anomalous behaviors in computer networks remains a formidable
challenge. This work outlines a machine learning technique that uses deep neural networks …

Featured Application Optimal design of feature vectors for early-phase attack detection in
large communication networks. Abstract The consolidation of encryption and big data in …

As the 5G rolls out around the world, many edge applications will be deployed by app
vendors and accessed by massive end-users. Efficient detection of malicious network …

Reliable network traffic classification is essential to management and security tasks.
Therefore, it is beneficial to analyze and improve existing techniques. Some of the most …

In this paper, we present Real-Time Flow Filter (RTFF)-a system that adopts a middle ground
between coarse-grained volume anomaly detection and deep packet inspection. RTFF was …

Traffic classification has a vital role in tasks as wide ranging as trend analyses, adaptive
network-based QoS marking of traffic, dynamic access control and lawful interception. The …