Dual Mathematical Model for Calculating of Deep Packet Inspection

2021 ◽  
Author(s):  
Boris Goldstein ◽  
Vadim Fitsov
Keyword(s):  
Mathematical Model ◽  
Deep Packet Inspection ◽  
Packet Inspection

scholarly journals Software Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Method

2021 ◽  
Vol 7 (1) ◽  
pp. 132-140
Author(s):  
V. Fitsov
Keyword(s):  
Mathematical Model ◽  
Traffic Flow ◽  
Communication Networks ◽  
Inspection System ◽  
Deep Packet Inspection ◽  
Maximum Element ◽  
Software Methodology ◽  
Monotonically Decreasing Function ◽  
Inspection Systems ◽  
Packet Inspection

Deep packet inspection systems on communication networks are used to identify the application generating a specific traffic flow. The issues related to modeling and design of deep packet inspection systems remain poorly understood. In this paper, a software technique for evaluating the effectiveness of the hardware composition of the servers of the deep packet inspection system is presented, using a mathematical model of such a system and software search methods. The description of the program search by the maximum element method and the Hook - Jeeves method is given. A modernization of the Hook-Jeeves method for a monotonically decreasing function is proposed. Comparison of the methods by the number of search steps is performed.

Efficient Regular Expression Compression Algorithm for Deep Packet Inspection

2009 ◽  
Vol 20 (8) ◽  
pp. 2214-2226 ◽  
Author(s):  
Qian XU ◽  
Yue-Peng E ◽  
Jing-Guo GE ◽  
Hua-Lin QIAN
Keyword(s):  
Regular Expression ◽  
Compression Algorithm ◽  
Deep Packet Inspection ◽  
Packet Inspection

scholarly journals Research on QoS Classification of Network Encrypted Traffic Behavior Based on Machine Learning

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1376
Author(s):  
Yung-Fa Huang ◽  
Chuan-Bi Lin ◽  
Chien-Min Chung ◽  
Ching-Mu Chen
Keyword(s):  
Machine Learning ◽  
Majority Voting ◽  
Deep Packet Inspection ◽  
Traffic Classification ◽  
K 13 ◽  
Packet Inspection ◽  
Majority Voting Method ◽  
Encrypted Traffic ◽  
Traffic Behavior

In recent years, privacy awareness is concerned due to many Internet services have chosen to use encrypted agreements. In order to improve the quality of service (QoS), the network encrypted traffic behaviors are classified based on machine learning discussed in this paper. However, the traditional traffic classification methods, such as IP/ASN (Autonomous System Number) analysis, Port-based and deep packet inspection, etc., can classify traffic behavior, but cannot effectively handle encrypted traffic. Thus, this paper proposed a hybrid traffic classification (HTC) method based on machine learning and combined with IP/ASN analysis with deep packet inspection. Moreover, the majority voting method was also used to quickly classify different QoS traffic accurately. Experimental results show that the proposed HTC method can effectively classify different encrypted traffic. The classification accuracy can be further improved by 10% with majority voting as K = 13. Especially when the networking data are using the same protocol, the proposed HTC can effectively classify the traffic data with different behaviors with the differentiated services code point (DSCP) mark.

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