• Journal of Internet Computing and Services
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• v.9 no.4
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• pp.85-96
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• 2008

An authentication procedure on wired and wireless network will be done based on the registration and management process storing both the user's IP address and client device's MAC address information. However, existent MAC address registration/administration mechanisms were weak in MAC Spoofing attack as the attacker can change his/her own MAC address to client's MAC address. Therefore, an advanced mechanism should be proposed to protect the MAC address spoofing attack. But, existing techniques sequentially compare a sequence number on packet with previous one to distinguish the alteration and modification of MAC address. However, they are not sufficient to actively detect and protect the wireless MAC spoofing attack. In this paper, both AirSensor and AP are used in wireless network for collecting the MAC address on wireless packets. And then proposed module is used for detecting and protecting MAC spoofing attack in real time based on MAC Address Lookup table. The proposed mechanism provides enhanced detection/protection performance and it also provides a real time correspondence mechanism on wireless MAC spoofing attack with minimum delay.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.4
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• pp.63-73
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• 2010

Malicious bot programs, the source of distributed denial of service attack, are widespread and the number of PCs which were infected by malicious bot program are increasing geometrically thesedays. The continuous distributed denial of service attacks are happened constantly through these bot PCs and some financial incident cases have found lately. Therefore researches to response distributed denial of service attack are necessary so we propose an effective feature generation method for distributed denial of service attack detection using entropy. In this paper, we apply our method to both the DARPA 2000 datasets and also the distributed denial of service attack datasets that we composed and generated ourself in general university. And then we evaluate how the proposed method is useful through classification using bayesian network classifier.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.130-140
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• 2010

SIP (Session Initiation Protocol) is a signaling protocol to provide IP-based VoIP (Voice over IP) service. However, many security vulnerabilities exist as the SIP protocol utilizes the existing IP based network. The SIP Malformed message attacks may cause malfunction on VoIP services by changing the transmitted SIP header information. Additionally, there are several threats such that an attacker can extract personal information on SIP client system by inserting malicious code into SIP header. Therefore, the alternative measures should be required. In this study, we analyzed the existing research on the SIP anomaly message detection mechanism against SIP attack. And then, we proposed a Co-occurrence based SIP packet analysis mechanism, which has been used on language processing techniques. We proposed a association rule generation and an attack detection technique by using the actual SIP session state. Experimental results showed that the average detection rate was 87% on SIP attacks in case of using the proposed technique.

• The KIPS Transactions:PartC
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• v.15C no.5
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• pp.351-358
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• 2008

Recently, as network flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems(IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network environment. In this paper we propose a lightweight and fast detection mechanism for traffic flooding attacks. Firstly, we use SNMP MIB statistical data gathered from SNMP agents, instead of raw packet data from network links. Secondly, we use a machine learning approach based on a Support Vector Machine(SVM) for attack classification. Using MIB and SVM, we achieved fast detection with high accuracy, the minimization of the system burden, and extendibility for system deployment. The proposed mechanism is constructed in a hierarchical structure, which first distinguishes attack traffic from normal traffic and then determines the type of attacks in detail. Using MIB data sets collected from real experiments involving a DDoS attack, we validate the possibility of our approaches. It is shown that network attacks are detected with high efficiency, and classified with low false alarms.

• The KIPS Transactions:PartC
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• v.10C no.5
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• pp.525-532
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• 2003

This paper describes intrusion detection rule management using mobile agents. Intrusion detection can be divided into anomaly detection and misuse detection. Misuse detection is best suited for reliably detecting known use patterns. Misuse detection systems can detect many or all known attack patterns, but they are of little use for as yet unknown attack methods. Therefore, the introduction of mobile agents to provide computational security by constantly moving around the Internet and propagating rules is presented as a solution to misuse detection. This work presents a new approach for detecting intrusions, in which mobile agent mechanisms are used for security rules propagation. To evaluate the proposed approach, we compared the workload data between a rules propagation method using a mobile agent and a conventional method. Also, we simulated a rules management using NS-2 (Network Simulator) with respect to time.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.33-40
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• 2012

In this paper we propose an anomaly detection scheme to detect new attack paths or new attack methods without false positives by monitoring HTTP Outbound Traffic after efficient training. Our proposed scheme detects web-based attacks by comparing tags or javascripts of HTTP Outbound Traffic with normal behavioral models which apply HMM(Hidden Markov Model). Through the verification analysis under the real-attacked environment, we show that our scheme has superior detection capability of 0.0001% false positive and 96% detection rate.

• Convergence Security Journal
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• v.5 no.2
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• pp.9-17
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• 2005

Recently It's difficult to deal with about variety of attack. And Simple Security management have a problem. It is that they don't develop system measuring their system envoirment and have efficient attack detector, countermeasure organization about large network. Therefore, need model about enterprise management of various security system and intrusion detection of each systems and response. In this paper, improve PBNM structure that manage wide network resources and presented suitable model in intrusion detection and response of security system. Also, designed policy-based enterprise security management system for effective intrusion detection and response by applying presented model to enterprise security management system.

• Journal of the Korea Society of Computer and Information
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• v.11 no.5 s.43
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• pp.157-164
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• 2006

The Recently, most of Internet attacks are zero-day types of the unknown attacks by Malware. Using already known Misuse Detection Technology is hard to cope with these attacks. Also, the existing information security technology reached the limits because of various attack's patterns over the Internet, as web based service became more affordable, web service exposed to the internet becomes main target of attack. This paper classifies the traffic type over the internet and suggests the Threat Management System(TMS) including the anomaly intrusion detection technologies which can detect and analyze the anomaly sign for each traffic type.

• Journal of the Korea Computer Industry Society
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• v.5 no.8
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• pp.781-790
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• 2004

This paper describes intrusion detection rule mangement using mobile agents. Intrusion detection can be divided into anomaly detection and misuse detection. Misuse detection is best suited for reliably detecting known use patterns. Misuse detection systems can detect many or all known attack patterns, but they are of little use for as yet unknown attack methods. Therefore, the introduction of mobile agents to provide computational security by constantly moving around the Internet and propagating rules is presented as a solution to misuse detection. This work presents a new approach for detecting intrusions, in which mobile agent mechanisms are used for security rules propagation. To evaluate the proposed appraoch, we compared the workload data between a rules propagation method using a mobile agent and a conventional method. Also, we simulated a rules management using NS-2(Network Simulator) with respect to time.

• Journal of Apiculture
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• v.32 no.3
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• pp.191-198
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• 2017

Vespa hornets are notorious predators of honeybees in Korean beekeeping. Detection of vespa hornet attacking on honeybee colony was tried through analysis of wing beat frequency profiling from Vespa mandarinia. Wing beat profiles of V. mandarinia during active flight and resting were distinctively different. From the wing beat profiling, algorithm of automated detection of vespa attack was encoded, and alert system was developed using Teensy 3.2 and Raspberry pi 3. From the laboratory testing, the prototype system successfully detected vespa wing beats and delivered the vespa attack information to the user wirelessly. Further development of the system could help precision alert system of the vespa attack to apiary.