• 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 KIISE:Computing Practices and Letters
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• v.14 no.5
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• pp.517-521
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• 2008

A worm is a malware that propagates quickly from host to host without any human intervention. Need of early worm detection has changed research paradigm from signature based worm detection to the behavioral based detection. To increase effectiveness of proposed solution, in this paper we present mechanism of detection and prevention of worm in distributed fashion. Furthermore, to minimize the worm destruction; upon worm detection we propagate the possible attack aleγt to neighboring nodes in secure and organized manner. Considering worm behavior, our proposed mechanism detects worm cycles and infection chains to detect the sudden change in network performance. And our model neither needs to maintain a huge database of signatures nor needs to have too much computing power, that is why it is very light and simple. So, our proposed scheme is suitable for the ubiquitous environment. Simulation results illustrate better detection and prevention which leads to the reduction of infection rate.

• The KIPS Transactions:PartC
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• v.12C no.6 s.102
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• pp.847-854
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• 2005

Since the propagation speed of the Internet worms is quite fast, worm detection in early propagation stage is very important for reducing the damage. Virus throttling technique, one of many early worm detection techniques, detects the Internet worm propagation by limiting the connection requests within a certain ratio.[6, 7] The typical throttling technique increases the possibility of false detection by treating destination IP addresses independently in their delay queue managements. In addition, it uses a simple decision strategy that determines a worn intrusion if the delay queue is overflown. This paper proposes a two dimensional delay queue management technique in which the sessions with the same destination IP are linked and thus a IP is not stored more than once. The virus throttling technique with the proposed delay queue management can reduce the possibility of false worm detection, compared with the typical throttling since the proposed technique never counts the number of a IP more than once when it chicks the length of delay queue. Moreover, this paper proposes a worm detection algorithm based on weighted average queue length for reducing worm detection time and the number of worm packets, without increasing the length of delay queue. Through deep experiments, it is verified that the proposed technique taking account of the length of past delay queue as well as current delay queue forecasts the worn propagation earlier than the typical iuぉ throttling techniques do.

• Journal of KIISE:Information Networking
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• v.34 no.3
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• pp.186-192
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• 2007

The virus throttling technique[5,6] is the one of well-known worm early technique. Virus throttling reduce the worm propagration by delaying connection packets artificially. However the worm detection time is not sufficiently fast as expected when the worm generated worm packets at a low rate. This is because the virus throttling technique use only delay queue length. In this paper we use the trend of weighted average delay queue length (TW AQL). By using TW AQL, the worm detection time is not only shorten at a low rate Internet worm, but also the false alarm does not largely increase. By experiment, we also proved our proposed algorithm had better performance.

• The KIPS Transactions:PartC
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• v.13C no.5 s.108
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• pp.559-566
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• 2006

Virus throttling technique, one of many early worm detection techniques, detects the Internet worm propagation by limiting the connect requests within a certain ratio. The typical virus throttling detects worm occurrence by monitoring the length of delay queue with the fixed period of rate limiter. In this paper, we propose an algorithm that controls the period of rate limiter autonomically by utilizing the weighted average delay queue length and suggest various period determination policies that use the weighted average delay queue length as an input parameter. Through deep experiments, it is verified that the proposed technique is able to lessen inconvenience of users by reducing the connection delay time with haying just little effect on worm detection time.

• Convergence Security Journal
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• v.6 no.4
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• pp.75-82
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• 2006

Internet worm gives various while we paralyze the network and flow the information out damages. In this paper, I suggest the method to prevent this. This method detect internet worm in PC first. and present the method to do an automatic confrontation. This method detect a traffic foundation network scanning of internet worm which is the feature and accomplish the confrontation. This method stop the process to be infected at the internet worm and prevent that traffic is flowed out to the outside. and This method isolate the execution file to be infected at the internet worm and move at a specific location for organizing at the postmortem so that we could accomplish the investigation about internet worm. Such method is useful to the radiation detection indication and computation of unknown internet worm. therefore, Stable network operation is possible through this method.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.71-76
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• 2013

A computer worm is a standalone malware computer program that probes and exploits vulnerabilities of systems. It replicates and spreads itself to other computers via networks. In this paper, we study how to detect and prevent worms. First, we generated Codered II traffic on the emulated testbed called Deterlab. Then we identified dubious parts using Earlybird and wrote down Snort rules using Wireshark. Finally, by applying the Snort rules to the traffic, we could confirmed that worm detection was successfully done.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.9
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• pp.675-684
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• 2009

Internet worm can cause a traffic problem through DDoS(Distributed Denial of Services) or other kind of attacks. In those manners, it can compromise the internet infrastructure. In addition to this, it can intrude to important server and expose personal information to attacker. However, current detection and response mechanisms to worm have many vulnerabilities, because they only use local characteristic of worm or can treat known worms. In this paper, we propose a new framework to detect unknown worms. It uses macroscopic characteristic of worm to detect unknown worm early. In proposed idea, we define the macroscopic behavior of worm, propose a worm detection method to detect worm flow directly in IP packet networks, and show the performance of our system with simulations. In IP based method, we implement the proposed system and measure the time overhead to execute our system. The measurement shows our system is not too heavy to normal host users.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.67-74
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• 2010

Recently, the early detection and prevention of worm research is mainly studying based on the analysis of generalized worm propagation property. However, it is not easy to do Worm early detection with its attributes because the modeling method for Worm propagation is vague and not specified yet. Worm scanning method is exceedingly effect to Worm propagation process. This paper describes a modeling method and its simulations to estimate various worm growth patterns and their corresponding propagation algorithms. It also tests and varies the impact of various improvements, starting from a trivial simulation of worm propagation and the underlying network infrastructure. It attempts to determine the theoretical maximum propagation speed of worms and how it can be achieved. Moreover, we present the feasibility of the proposed model based on real testbed for verification.

• 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.