Traditional Network Layer Research Articles

SI-STIN: A Smart Identifier Framework for Space and Terrestrial Integrated Network

Currently, many hybrid satellite-aerial-terrestrial networks are under construction. The problem is how to coordinate heterogeneous communication networks and platforms to provide more effective services, which is seriously hindering the further development of networks. To mitigate this and other problems, a novel heterogeneous wireless network architecture named the Smart Identifier for Space and Terrestrial Integrated Network (SI-STIN) is designed. Based on the Smart Identifier Network (SINET), SI-STIN aims to solve the problem of the heterogeneous convergence of the integrated network layer, break through the triple binding of the traditional network layer design, and ultimately achieve high efficiency for resource integration and interconnection. The SI-STIN includes three layers and two domains: the smart pervasive service layer, dynamic resource adaption layer, and collaborative network component layer, and the entity domain and behavior domain, in which many separate identifiers and behavior descriptions are applied. Then the details, workflow, applications, and challenges of the STIN are presented. Experimental analysis verifies that the SI-STIN improves the performance in regard to network security and transmission efficiency and thus has great potential to satisfy various network demands.

Anomaly Detection for Application Layer User Browsing Behavior Based on Attributes and Features

Application layer distributed denial of service (App-DDoS) attacks has posed a great threat to the security of the Internet. Since these attacks occur in the application layer, they can easily evade traditional network layer and transport layer detection methods. In this paper, we extract a group of user behavior attributes from our intercept program instead of web server logs and construct a behavior feature matrix based on nine user behavior features to characterize user behavior. Subsequently, principal component analysis (PCA) is applied to profile the user browsing behavior pattern in the feature matrix and outliers from the pattern are used to recognize normal users and attackers. Experiment results show that the proposed method is good to distinguish normal users and attackers. Finally, we implement three machine learning algorithms (K-means, DBSCAN and SVM) to further validate the effectiveness of the proposed attributes and features.

The Design of Information Security Management System in College

Research shows that with hundreds of application systems, the general colleges and universities have different developers in uneven professional levels, such as professional personnel from outside of the school, from independent departments, and even the students. The operating environment of application system is scattered in a managed way, by cloud services or directly in the office. The various application systems and the complex operating environment in colleges and universities, and some unprofessional developers would make bugs and difficult to manage. Nowadays, the universities have been widely deployed firewalls, intrusion and other network security equipment, so that hackers have been difficult to attack the websites through the traditional network layer. However, due to the widespread bugs of web application, hackers often use them to attack the sites. Thus, it is urgent to develop a set of security management service platform to help the information security administrator manage the basic information, find bugs before the attacker and timely repair. Based on the above reasons and combined with the actual needs, the campus network security management service plat form is designed and developed.