• Title/Summary/Keyword: IPSpoofing

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Designing Mutual Cooperation Security Model for IP Spoofing Attacks about Medical Cluster Basis Big Data Environment (의료클러스터 기반의 빅 데이터 환경에 대한 IP Spoofing 공격 발생시 상호협력 보안 모델 설계)

  • An, Chang Ho;Baek, Hyun Chul;Seo, Yeong Geon;Jeong, Won Chang;Park, Jae Heung
    • Convergence Security Journal
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    • v.16 no.7
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    • pp.21-29
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    • 2016
  • Our society is currently exposed to environment of various information that is exchanged real time through networks. Especially regarding medical policy, the government rushes to practice remote medical treatment to improve the quality of medical services for citizens. The remote medical practice requires establishment of medical information based on big data for customized treatment regardless of where patients are. This study suggests establishment of regional medical cluster along with defense and protection cooperation models that in case service availability is harmed, and attacks occur, the attacks can be detected, and proper measures can be taken. For this, the study suggested forming networks with nationwide local government hospitals as regional virtual medical cluster bases by the same medical information system. The study also designed a mutual cooperation security model that can real time cope with IP Spoofing attack that can occur in the medical cluster and DDoS attacks accordingly, so that the limit that sole system and sole security policy have can be overcome.

The Design of Authentication Model based on Symmetric Key Encryption for Improving Network Availability in Cloud Environment (클라우드 환경에서 네트워크 가용성 개선을 위한 대칭키 암호화 기반 인증 모델 설계)

  • Baek, Yong-Jin;Hong, Suk-Won;Kim, Sang-Bok
    • Convergence Security Journal
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    • v.19 no.5
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    • pp.47-53
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    • 2019
  • Network-based sharing of information has evolved into a cloud service environment today, increasing its number of users rapidly, but has become a major target for network-based illegal attackers.. In addition, IP spoofing among attackers' various attack techniques generally involves resource exhaustion attacks. Therefore, fast detection and response techniques are required. The existing detection method for IP spoofing attack performs the final authentication process according to the analysis and matching of traceback information of the client who attempted the connection request. However, the simple comparison method of traceback information may require excessive OTP due to frequent false positives in an environment requiring service transparency. In this paper, symmetric key cryptography based on traceback information is used as mutual authentication information to improve this problem. That is, after generating a traceback-based encryption key, mutual authentication is possible by performing a normal decryption process. In addition, this process could improve the overhead caused by false positives.

An Improved Model Design for Traceback Analysis Time Based on Euclidean Distance to IP Spoofing Attack (IP 스푸핑 공격 발생 시 유클리드 거리 기반의 트레이스 백 분석시간 개선 모델)

  • Liu, Yang;Baek, Hyun Chul;Park, Jae Heung;Kim, Sang Bok
    • Convergence Security Journal
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    • v.17 no.5
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    • pp.11-18
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    • 2017
  • Now the ways in which information is exchanged by computers are changing, a variety of this information exchange method also requires corresponding change of responding to an illegal attack. Among these illegal attacks, the IP spoofing attack refers to the attack whose process are accompanied by DDoS attack and resource exhaustion attack. The way to detect an IP spoofing attack is by using traceback information. The basic traceback information analysis method is implemented by comparing and analyzing the normal router information from client with routing information existing in routing path on the server. There fore, Such an attack detection method use all routing IP information on the path in a sequential comparison. It's difficulty to responding with rapidly changing attacks in time. In this paper, all IP addresses on the path to compute in a coordinate manner. Based on this, it was possible to analyze the traceback information to improve the number of traceback required for attack detection.