• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.1109-1112
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• 2006

DDoS(Distributed Denial-of-Service) 공격은 인터넷 침해가운데 가장 위협적인 공격들 중 하나이며 이러한 공격을 실시간으로 탐지하기 위한 연구는 활발히 이루어져 왔다. 하지만 기존의 탐지 메커니즘이 가지고 있는 높은 오탐지율은 여전히 보완해야할 과제로 남아 있다. 따라서 본 논문에서는 DDoS공격 탐지의 근거로 사용된 기존의 트래픽 볼륨(traffic volume), 엔트로피(entropy), 그리고 카이제곱(chi-square)을 이용한 비정상 행위탐지(Anomaly detection)방식의 침임탐지시스템이 가지는 오탐지율(false alarm rate)을 개선할 수 있는 방안을 제안한다. 또한 공격 탐지 시 프로토콜, TCP 플래그(flag), 그리고 포트 번호를 이용하여 네트워크 관리자에게 보다 자세한 공격 정보를 제공함으로써 효율적으로 공격에 대처할 수 있는 시스템을 설계한다.

• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.594-600
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• 2010

Many studies are DDoS in Internet network, but the study is the fact that is not enough in a voice network. Therefore, we designed the extended TRW algorithm that was a DDoS attack traffic detection algorithm for the voice network which used an IP data network to solve upper problems in this article and evaluated it. The algorithm that is proposed in this paper analyzes TRW algorithm to detect existing DDoS attack in Internet network and, design connection and end connection to apply to a voice network, define probability function to count this. For inspect the algorithm, Set a threshold and using NS-2 Simulator. We measured detection rate by an attack traffic type and detection time by attack speed. At the result of evaluation 4.3 seconds for detection when transmitted INVITE attack packets per 0.1 seconds and 89.6% performance because detected 13,453 packet with attack at 15,000 time when transmitted attack packet.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.135-142
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• 2017

DDoS (Distributed Denial of Service) exhausts the target server's resources using the large number of zombie pc, As a result normal users don't access to server. DDoS Attacks steadly increase by many attacker, and almost target of the attack is critical system such as IT Service Provider, Government Agency, Financial Institution. In this paper, We will introduce the CNN (Convolutional Neural Network) of deep learning based real-time detection system for DNS amplification Attack (DNS DDoS Attack). We use the dataset which is mixed with collected data in the real environment in order to overcome existing research limits that use only the data collected in the experiment environment. Also, we build a deep learning model based on Convolutional Neural Network (CNN) that is used in pattern recognition.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.713-716
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• 2011

인터넷 유저가 증가하면서 DDoS 공격은 인터넷 안정성에 매우 중요한 위협을 가하고 있다. 서비스 거부 공격이란 서비스를 제공하는데 있어 필요한 컴퓨팅 및 통신 자원을 고갈시키는 공격으로 원천적으로 해결하기가 매우 힘든 것으로 알려져 있다. 인터넷과 같은 대규모 망을 대상으로 한 네트워크 공격은 효과적인 탐지 방법이 요구된다. 그러므로 대규모 망에서 침입 탐지 시스템은 효율적인 실시간 탐지가 필요하다. 본 논문에서는 DDoS 공격에 따른 비정상적인 트래픽 범람을 방지하고 합법적인 트래픽 전송을 보장하기 위하여 엔트로피기반의 DDoS 공격 대응 기법을 제안한다. OPNET을 이용해 구현한 결과 DDoS 공격중에 원활한 서비스를 제공할 수 있는 것을 확인하였다.

• KNOM Review
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• v.23 no.1
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• pp.10-17
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• 2020

As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.

• Journal of Digital Convergence
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• v.12 no.1
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• pp.423-429
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• 2014

DDoS attacks is upgrade of DoS attacks. Botnet is being used by DDoS attack, so it is able to attack a millions of PCs at one time. DDoS attacks find the root the cause of the attack because it is hard to find sources for it, even after the treatment wavelength serious social problem in this study, the analysis and countermeasures for DDoS attack is presented.

• Proceedings of the KAIS Fall Conference
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• 2011.05a
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• pp.53-56
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• 2011

지난 7.7 DDoS(Distributed Denial of Service), 3.3 DDoS 대란을 통해서 보여주듯 DDoS 공격이 네트워크 주요 위협요소로 매우 부각되고 있으나, 공격에 대해서 실시간으로 감지하고 대응하기에 어렵다. 그리고 현재 여러 분야에서 매우 많은 용도로 사용되는 SMS(Short Message Service)도 DDoS 공격 수단으로 사용되어 이동전화 시스템에 큰 혼란을 야기할 수 있다. 기존의 Bloom Filter 탐지 기법은 구조가 간단하고 실시간 탐지가 가능한 장점을 갖지만 오탐지율에 대한 문제점을 가진다. 본 논문에서는 목적지 기반의 다중의 해시함수를 사용한 Counting Bloom Filter 기법을 이용하여 임계치 이상 카운트된 동일한 목적지로 발송되는 SMS에 대하여 공격으로 탐지하고 SMSC에 통보하여 차단시키는 시스템을 제안한다.

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.55-66
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• 2009

In this paper, I will discuss how the Internet has spread rapidly in our lives. Large portals and social networks experience service attacks that access personal customers' databases. This interferes with normal service through DDoS (Distribute Denial of Service Attack), which is the topic I want to discuss. Among the types of DDoS, TCP SYN Flooding attacks are rarely found because they use few traffics and its attacking type is regular transaction. The purpose of this study is to find and suggest the method for accurate detection of the attacks. Through the analysis of TCP SYN Flooding attacks, we find that these attacks cause Backscatter effect. This study is about the algorithm which detects the attacks of TCP SYN Flooding by the study of Backscatter effect.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.1
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• pp.203-209
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• 2014

Today, Distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. It is very hard to prevent the DDoS attack. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the detection mechanism using analysis of flow information against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack.

• The KIPS Transactions:PartC
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• v.14C no.1 s.111
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• pp.45-54
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• 2007

As one of the most threatening attacks, DDoS attack makes distributed multiple agents consume some critical resources at the target within the short time, thus the extent and scope of damage is serious. Against the problems, the existing defenses focus on detection, traceback (identification), and filtering. Especially, in the hierarchical networks, the traffic congestion of a specific node could incur the normal traffic congestion of overall lower nodes, and also block the control traffic for notifying the attack detection and identifying the attack agents. In this paper, we introduce a DDoS attack tolerant network structure using a hierarchical overlay for hierarchical networks, which can convey the control traffic for defense such as the notification for attack detection and identification, and detour the normal traffic before getting rid of attack agents. Lastly, we analyze the overhead of overlay construction, the possibility of speedy detection notification, and the extent of normal traffic transmission in the attack case through simulation.