• Proceedings of the Korea Information Assurance Society Conference
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• 2004.05a
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• pp.333-337
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• 2004

The Internet worm is changed rapidly and virus vaccine can not defense the whole Internet worm. To prevent them form spreading into network and analysis specifications, we design and implement the Internet Worm Traffic Generator. In this research, we offer the real worm propagation environment through protocol and scenario specification.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.161-164
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• 2005

As the power of influence of the Internet grows steadily, attacks against the Internet can cause enormous monetary damages nowadays. A worm can not only replicate itself like a virus but also propagate itself across the Internet. So it infects vulnerable hosts in the Internet and then downgrades the overall performance of the Internet or makes the Internet not to work. To response this, worm detection and prevention technologies are developed. The worm detection technologies are classified into two categories, host based detection and network based detection. Host based detection methods are a method which checks the files that worms make, a method which checks the integrity of the file systems and so on. Network based detection methods are a misuse detection method which compares traffic payloads with worm signatures and anomaly detection methods which check inbound/outbound scan rates, ICMP host/port unreachable message rates, and TCP RST packet rates. However, single detection methods like the aforementioned can't response worms' attacks effectively because worms attack the Internet in the distributed fashion. In this paper, we propose a design of distributed worm detection system to overcome the inefficiency. Existing distributed network intrusion detection systems cooperate with each other only with their own information. Unlike this, in our proposed system, a worm detection system on a network in which worms select targets and a worm detection system on a network in which worms propagate themselves cooperate with each other with the direction-aware information in terms of worm's lifecycle. The direction-aware information includes the moving direction of worms and the service port attacked by worms. In this way, we can not only reduce false positive rate of the system but also prevent worms from propagating themselves across the Internet through dispersing the confirmed worm signature.

• Journal of Information Processing Systems
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• v.5 no.1
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• pp.33-40
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• 2009

Ever since the network-based malicious code commonly known as a 'worm' surfaced in the early part of the 1980's, its prevalence has grown more and more. The RCS (Random Constant Spreading) worm has become a dominant, malicious virus in recent computer networking circles. The worm retards the availability of an overall network by exhausting resources such as CPU capacity, network peripherals and transfer bandwidth, causing damage to an uninfected system as well as an infected system. The generation and spreading cycle of these worms progress rapidly. The existing studies to counter malicious code have studied the Microscopic Model for detecting worm generation based on some specific pattern or sign of attack, thus preventing its spread by countering the worm directly on detection. However, due to zero-day threat actualization, rapid spreading of the RCS worm and reduction of survival time, securing a security model to ensure the survivability of the network became an urgent problem that the existing solution-oriented security measures did not address. This paper analyzes the recently studied efficient dynamic network. Essentially, this paper suggests a model that dynamically controls the RCS worm using the characteristics of Power-Law and depth distribution of the delivery node, which is commonly seen in preferential growth networks. Moreover, we suggest a model that dynamically controls the spread of the worm using information about the depth distribution of delivery. We also verified via simulation that the load for each node was minimized at an optimal depth to effectively restrain the spread of the worm.

• Proceedings of the Zoological Society Korea Conference
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• 1996.04a
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• pp.56.1-56
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• 1996

No Abstract, See Full Text

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.6
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• pp.339-348
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• 2006

Recently, many researches on detecting and responding worms due to the fatal infrastructural damages explosively damaged by automated attack tools, particularly worms. Network service vulnerability exploiting worms have high propagation velocity, exhaust network bandwidth and even disrupt the Internet. Previous worm researches focused on signature-based approaches however these days, approaches based on behavioral features of worms are more highlighted because of their low false positive rate and the attainability of early detection. In this paper, we propose a Distributed Worm Detection Model based on packet marking. The proposed model detects Worm Cycle and Infection Chain among which the behavior features of worms. Moreover, it supports high scalability and feasibility because of its distributed reacting mechanism and low processing overhead. We virtually implement worm propagation environment and evaluate the effectiveness of detecting and responding worm propagation.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 2004.11a
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• pp.322-322
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• 2004

• Proceedings of the Korea Information Assurance Society Conference
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• 2004.05a
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• pp.327-331
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• 2004

The new cyber world has created by Internet that is prosperous rapidly. But with the expansion of Internet the hacking and intrusion are also increased very much. Actually there were many incidents in Internet, but the damage was restricted within a local area and local system. However, the Great 1.25 Internet Disturbance has paralyzed the national wide Internet environment. It because the Slammer Worm. The worm is a malformed program that uses both of the hacking and computer virus techniques. It autonomously attacks the vulnerability of Windows system, duplicates and spreads by itself. Jus like the Slammer Worm, almost every worms attack the vulnerability of Windows systems that installed in personal PC. Therefore, the vulnerability in personal PC could destroy the whole Internet world. So, in this paper we propose a Internet Worm Expanding Prevention System that could be installed in personal PC to prevent from expanding the Internet Worm. And we will introduce the results of developed system.

• Convergence Security Journal
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• v.2 no.2
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• pp.39-47
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• 2002

To recently with the love-letter and Back Orifice the same Worm-virus, with the Trojan and the Linux-virus back against the new species virus which inside and outside of the country to increase tendency the malignant new species virus which is the possibility of decreasing the damage which is enormous in the object appears and to follow a same network coat large scale PC is being quicker, it disposes spontaneously to respect, applied an artificial intelligence technique the research against the next generation malignant computer virus of new form is demanded. Will reach and to respect it analyzes the digital immunity system of the automatic detection which is quick against the next generation malignant virus which had become unconfirmed and the foreign countries which has an removal function.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.2
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• pp.30-36
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• 2007

The UPnP uses the same standard protocol as SSDP and UDP based on standard internet and technology like the TCP/IP, and is independent of other physical networking product. But the structure of the UPnP has the of vulnerability to the security countermeasure for home-networking technology since it is operated on the same protocol as the SSDP and UDP. In this paper, we analyze and report against the DoS attack, where the worm virus, using the vulnerability to the UPnP, eliminates the attack of all equipments that are based on networking and eliminates the information belonging to the equipments of the home-networking or transmits the massive data.

• Journal of KIISE:Information Networking
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• v.36 no.2
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• pp.69-79
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• 2009

Today's Internet is one of the necessaries of our life. Anomalies of the Internet provoke social problems. For that reason, Internet Measurement which studies characteristics on Internet traffic attracts pubic attention. Recently, Traffic Dispersion Graph (TDG), a novel traffic analysis method, was proposed. The TDG is not a statistical analysis method but a graphical visualization method on interactions among network components. In this paper, we propose a new anomaly detection paradigm and its technique using TDG. The existing studies have focused on detecting anomalous packets of flows. On the other hand, we focus on detecting the sources of anomalous traffic. To realize our paradigm, we designed the TDG Clustering method. Through this method, we could classify anomalous hosts infected by various worm viruses. We obtained normal traffic through dropping traffic of the anomalous hosts. Especially, we expect that the TDG clustering method can be applied to real-time anomaly detection because calculations of the method are fast.