• Proceedings of the Korean Information Science Society Conference
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• 2004.10a
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• pp.442-444
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• 2004

서비스 거부 공격은 그 피해의 규모에 비해 방어하기가 무척 어려우며 충분히 대비를 한다 해도 알려지지 않은 새로운 서비스 거부 공격 기법에 피해를 입을 위험성이 항상 존재한다. 또한 최근 나타나고 있는 서비스 거부 공격 기법은 시스템 자원을 고갈시키는 분산 서비스 거부 공격(DDoS)에서 네트워크의 대역폭을 고갈시킴으로서 주요 네트워크 장비를 다운시키는 분산 반사 서비스 거부 공격(DRDoS)으로 진화하고 있다 이러한 공격 기법은 네트워크 트래픽의 이상 징후로서만 탐지될 뿐 개별 패킷으로는 탐지가 불가능하여 공격 징후는 알 수 있으되 자동화된 대응이 어려운 특징이 있다. 본 논문에서는 이미 알려진 공격뿐 아니라 새로운 서비스 거부 공격 패킷을 탐지하기 위하여, 패턴 분류 문제에 있어서 우수한 성능을 보이는 것으로 알려져 있는 Support Vector Machine(SVM)을 사용한 실험을 진행하였다. 테스트 결과. 학습된 공격 패킷에 대해서는 정확한 구분이 가능했으며 학습되지 않은 새로운 공격에 대해서도 탐지가 가능함을 보여주었다.

• Journal of the Korea Society of Computer and Information
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• v.17 no.5
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• pp.21-28
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• 2012

Recently, the necessity for good techniques of detecting network traffic attack has increased. In this paper, we suggest a new method of detecting abnormal patterns of network traffic data by visualizing their IP and port information into two dimensional images. The proposed approach first generates four 2D images from IP data of transmitters and receivers, and makes one 2D image from port data. Analyzing those images, it then extracts their major features such as linear patterns or high intensity values, and determines if traffic data contain DDoS or DoS Attacks. To comparatively evaluate the performance of the proposed algorithm, we show that our abnormal pattern detection method outperforms the existing algorithm in terms of accuracy and speed.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.351-360
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• 2006

Proposed security system attacks it, and detect it, and a filter generation, a business to be prompt of interception filtering dates at attack information public information. inner IPS to attack detour setting and a traffic band security, different connection security system, and be attack packet interceptions and service and port interception setting. Exchange new security rule and packet filtering for switch type implementation through dynamic reset memory by real time, and deal with a packet. The attack detection about DDoS, SQL Stammer, Bug bear, Opeserv worm etc. of the 2.5 Gbs which was an attack of a hacker consisted in network performance experiment by real time. Packet by attacks of a hacker was cut off, and ensured the normal inside and external network resources besides the packets which were normal by the results of active renewal.

• Journal of Digital Convergence
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• v.19 no.9
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• pp.463-468
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• 2021

Although the IoT is showing explosive growth due to the development of technology and the spread of IoT devices and activation of services, serious security risks and financial damage are occurring due to the activities of various botnets. Therefore, it is important to accurately and quickly detect the activities of these botnets. As security in the IoT environment has characteristics that require operation with minimum processing performance and memory, in this paper, the minimum characteristics for detection are selected, and KNN (K-Nearest Neighbor), Naïve Bayes, Decision Tree, Random A comparative study was conducted on the performance of machine learning algorithms such as Forest to detect botnet activity. Experimental results using the Bot-IoT dataset showed that KNN can detect DDoS, DoS, and Reconnaissance attacks most effectively and efficiently among the applied machine learning algorithms.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.12
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• pp.1670-1675
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• 2020

In this study, to provide the basis for establishing effective network based countermeasures against DRDoS(Distributed Reflection Denial of Service) attacks, we propose a new 'DRDoS attack multi-level detection method' that identifies the network based characteristics of DRDoS and applies probability and statistical techniques. The proposed method removes the limit to which normal traffic can be indiscriminately blocked by unlimited competition in network bandwidth by amplification of reflectors, which is characteristic of DRDoS. This means that by comparing 'Server to Server' and 'Outbound Session Incremental' for it, accurate DRDoS identification and detection is possible and only statistical and probabilistic thresholds are applied to traffic. Thus, network-based information security systems can take advantage of this to completely eliminate DRDoS attack frames. Therefore, it is expected that this study will contribute greatly to identifying and responding to DRDoS attacks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37B no.11
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• pp.1082-1089
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• 2012

Recent malicious attempts in Cyber space are intended to emerge national threats such as Suxnet as well as to get financial benefits through a large pool of comprised botnets. The evolved botnets use the Domain Name System(DNS) to communicate with the C&C server and zombies. DNS is one of the core and most important components of the Internet and DNS traffic are continually increased by the popular wireless Internet service. On the other hand, domain names are popular for malicious use. This paper studies on DNS-based cyber threats domain detection by data classification based on supervised learning. Furthermore, the developed cyber threats domain detection system using DNS traffic analysis provides collection, analysis, and normal/abnormal domain classification of huge amounts of DNS data.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.55-60
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• 2012

Recent botnets are widely using the DNS services at the connection of C&C server in order to evade botnet's detection. It is necessary to study on DNS analysis in order to counteract anomaly-based technique using the DNS. This paper studies collection of DNS traffic for experimental data and supervised learning for DNS traffic-based malicious domain classification such as query of domain name corresponding to C&C server from zombies. Especially, this paper would aim to determine significant features of DNS-based classification system for malicious domain extraction by the Principal Component Analysis(PCA).

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.1
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• pp.25-38
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• 2017

Many security threats are occurring around the world due to the characteristics of industrial control systems that can cause serious damage in the event of a security incident including major national infrastructure. Therefore, the industrial control system network traffic should be analyzed so that it can identify the attack in advance or perform incident response after the accident. In this paper, we research the visualization technique as network forensics to enable reasonable suspicion of all possible attacks on DNP3 control system protocol, and define normal action based rules and derive visualization requirements. As a result, we developed a visualization tool that can detect sudden network traffic changes such as DDoS and attacks that contain anormal behavior from captured packet files on industrial control system network. The suspicious behavior in the industrial control system network can be found using visualization tool with Digital Bond packet.

• The Journal of the Korea Contents Association
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• v.5 no.5
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• pp.172-181
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• 2005

The latest attack type against network traffic appeared by worm and bot that are advanced in DDoS. It is difficult to detect them because they are diversified, intelligent, concealed and automated. The exisiting traffic analysis method using SNMP has a vulnerable problem; it considers normal P2P and other application program to be harmful traffic. It also has limitation that does not analyze advanced programs such as worm and bot to harmful traffic. Therefore, we analyzed harmful traffic out Protocol and Port analysis. We also classified traffic by protocol, well-known port, P2P port, existing attack port, and specification port, apply singularity weight to detect, and analyze attack availability. As a result of simulation, it is proved that it can effectively detect P2P application, worm, bot, and DDoS attack.

• The Journal of the Korea Contents Association
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• v.21 no.2
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• pp.587-593
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• 2021

In this thesis, we will break away from the classic DDoS response system for large-scale denial-of-service attacks that develop day by day, and effectively endure intelligent denial-of-service attacks by utilizing artificial intelligence-based technology, one of the core technologies of the 4th revolution. A possible service model development plan was proposed. That is, a method to detect denial of service attacks and minimize damage through machine learning artificial intelligence learning targeting a large amount of data collected from multiple security devices and web servers was proposed. In particular, the development of a model for using artificial intelligence technology is to detect a Western service attack by focusing on the fact that when a service denial attack occurs while repeating a certain traffic change and transmitting data in a stable flow, a different pattern of data flow is shown. Artificial intelligence technology was used. When a denial of service attack occurs, a deviation between the probability-based actual traffic and the predicted value occurs, so it is possible to respond by judging as aggressiveness data. In this paper, a service denial attack detection model was explained by analyzing data based on logs generated from security equipment or servers.