Sharif Digital Repository

We present a design and implementation of a high-throughput deep packet inspection performing both stream categorization and intrusion detection on GPU platform using CUDA. This implementation is capable of matching 64 ethernet packet streams against 25 given regular expressions at 524 Mb/s rate on a computer system with GeForce GTX 295 graphic card  

Adaptive application detection in today's high-bandwidth networks is resource consuming and inaccurate due to the high volume, velocity, and variety characteristics of the networks traffic. To generate a robust classifier for identifying applications over encrypted traffic, we proposed DSCA as a DPI-based Stream Classification Algorithm. DSCA utilizes applications detected by the DPI, Deep Packet Inspection technique, as ground truth data and updates the classification model accordingly. To reduce the classification algorithms overhead without accuracy reduction, a feature selection method, named CfsSubsetEval, is deployed in DSCA. The proposed approach is implemented via the MOA tool and... 

Today's high-bandwidth networks require adaptive analyzing approaches to recognize the network variable behaviors. The analyzing approaches should be robust against the lack of prior knowledge and provide data to impose more complex policies. In this paper, ACoPE is proposed as an adaptive semi-supervised learning approach for complex-policy enforcement in high-bandwidth networks. ACoPE detects and maintains inter-flows relationships to impose complex-policies. It employs a statistical process control technique to monitor accuracy. Whenever the accuracy decreased, ACoPE considers it as a changed behavior and uses data from a deep packet inspection module to adapt itself with the change. The...