• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1989-2009
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• 2013

How to discover router vulnerabilities effectively and automatically is a critical problem to ensure network and information security. Previous research on router security is mostly about the technology of exploiting known flaws of routers. Fuzzing is a famous automated vulnerability finding technology; however, traditional Fuzzing tools are designed for testing network applications or other software. These tools are not or partly not suitable for testing routers. This paper designs a framework of discovering router protocol vulnerabilities, and proposes a mathematical model Two-stage Fuzzing Test Cases Generator(TFTCG) that improves previous methods to generate test cases. We have developed a tool called RPFuzzer based on TFTCG. RPFuzzer monitors routers by sending normal packets, keeping watch on CPU utilization and checking system logs, which can detect DoS, router reboot and so on. RPFuzzer' debugger based on modified Dynamips, which can record register values when an exception occurs. Finally, we experiment on the SNMP protocol, find 8 vulnerabilities, of which there are five unreleased vulnerabilities. The experiment has proved the effectiveness of RPFuzzer.

• Journal of Digital Convergence
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• v.10 no.6
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• pp.297-302
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• 2012

In this paper, we present a object detection and tracking method based on image context analysis. It is robust from the image variations such as complicated background, dynamic movement of the object. Image context analysis is carried out using the hybrid network of k-means and RBF. The proposed object detection employs context-driven adaptive Bayesian framework to relive the effect due to uneven object images. The proposed method used feature vector generator using 2D Haar wavelet transform and the Bayesian discriminant method in order to enhance the speed of learning. The system took less time to learn, and learning in a wide variety of data showed consistent results. After we developed the proposed method was applied to real-world environment. As a result, in the case of the object to detect pass outside expected area or other changes in the uncertain reaction showed that stable. The experimental results show that the proposed approach can achieve superior performance using various data sets to previously methods.

• Convergence Security Journal
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• v.13 no.2
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• pp.155-163
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• 2013

Connection hijacking attack using the vulnerability of the TCP protocol to redirect TCP stream goes through your machine actively (Active Attack). The SKEY such as one-time password protection mechanisms that are provided by a ticket-based authentication system such as Kerberos or redirection, the attacker can bypass.Someone TCP connection if you have access on TCP packet sniffer or packet generator is very vulnerable. Sniffer to defend against attacks such as one-time passwords and token-based authentication and user identification scheme has been used. Active protection, but these methods does not sign or encrypt the data stream from sniffing passwords over insecure networks, they are still vulnerable from attacks. For many people, an active attack is very difficult and so I think the threat is low, but here to help break the illusion successful intrusion on the UNIX host, a very aggressive attack is presented. The tools available on the Internet that attempt to exploit this vulnerability, known as the recent theoretical measures is required. In this paper, we propose analysis techniques on a wireless network intruder detection.