Xen Network Flow Analysis for Intrusion Detection

Author(s):  
Reece Johnston ◽  
Sun-il Kim ◽  
David Coe ◽  
Letha Etzkorn ◽  
Jeffrey Kulick ◽  
...  
2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
Naixia Mou ◽  
Haonan Ren ◽  
Yunhao Zheng ◽  
Jinhai Chen ◽  
Jiqiang Niu ◽  
...  

Maritime traffic can reflect the diverse and complex relations between countries and regions, such as economic trade and geopolitics. Based on the AIS (Automatic Identification System) trajectory data of ships, this study constructs the Maritime Silk Road traffic network. In this study, we used a complex network theory along with social network analysis and network flow analysis to analyze the spatial distribution characteristics of maritime traffic flow of the Maritime Silk Road; further, we empirically demonstrate the traffic inequality in the route. On this basis, we explore the role of the country in the maritime traffic system and the resulting traffic relations. There are three main results of this study. (1) The inequality in the maritime traffic of the Maritime Silk Road has led to obvious regional differences. Europe, west Asia, northeast Asia, and southeast Asia are the dominant regions of the Maritime Silk Road. (2) Different countries play different maritime traffic roles. Italy, Singapore, and China are the core countries in the maritime traffic network of the Maritime Silk Road; Greece, Turkey, Cyprus, Lebanon, and Israel have built a structure of maritime traffic flow in the eastern Mediterranean Sea, and Saudi Arabia serves as a bridge for maritime trade between Asia and Europe. (3) The maritime traffic relations show the characteristics of regionalization; countries in west Asia and the European Mediterranean region are clearly polarized, and competition–synergy relations have become the main form of maritime traffic relations among the countries in the dominant regions. Our results can provide a scientific reference for the coordinated development of regional shipping, improvement of maritime competition, cooperation strategies for countries, and adjustments in the organizational structure of ports along the Maritime Silk Road.


Author(s):  
Mark Thomas ◽  
Leigh Metcalf ◽  
Jonathan Spring ◽  
Paul Krystosek ◽  
Katherine Prevost
Keyword(s):  

2021 ◽  
Author(s):  
Phan The Duy ◽  
Nghi Hoang Khoa ◽  
Hoang Hiep ◽  
Nguyen Ba Tuan ◽  
Hien Do Hoang ◽  
...  

Revolutionizing operation model of traditional network in programmability, scalability, and orchestration, Software-Defined Networking (SDN) has considered as a novel network management approach for a massive network with heterogeneous devices. However, it is also highly susceptible to security attacks like conventional network. Inspired from the success of different machine learning algorithms in other domains, many intrusion detection systems (IDS) are presented to identify attacks aiming to harm the network. In this paper, leveraging the flow-based nature of SDN, we introduce DeepFlowIDS, a deep learning (DL)-based approach for anomaly detection using the flow analysis method in SDN. Furthermore, instead of using a lot of network properties, we only utilize essential characteristics of traffic flows to analyze with deep neural networks in IDS. This is to reduce the computational and time cost of attack traffic detection. Besides, we also study the practical benefits of applying deep transfer learning from computer vision to intrusion detection. This method can inherit the knowledge of an effective DL model from other contexts to resolve another task in cybersecurity. Our DL-based IDSs are built and trained with the NSL-KDD and CICIDS2018 dataset in both fine-tuning and feature extractor strategy of transfer learning. Then, it is integrated with the SDN controller to analyze traffic flows retrieved from OpenFlow statistics to recognize the anomaly action in the network.


1990 ◽  
Vol 19 (1) ◽  
pp. 143-155 ◽  
Author(s):  
Dan Gusfield
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document