• Journal of Internet Computing and Services
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• v.20 no.6
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• pp.11-20
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• 2019

Numerous enterprises, organizations and individual users are exposed to large DDoS (Distributed Denial of Service) attacks. DDoS attacks are performed through a BotNet, which is composed of a number of computers infected with a malware, e.g., zombie PCs and a special computer that controls the zombie PCs within a hierarchical chain of a command system. In order to detect a malware, a malware detection software or a vaccine program must identify the malware signature through an in-depth analysis, and these signatures need to be updated in priori. This is time consuming and costly. In this paper, we propose a botnet detection scheme that does not require a periodic signature update using an artificial neural network model. The proposed scheme exploits Word2Vec and accelerated hierarchical density-based clustering. Botnet detection performance of the proposed method was evaluated using the CTU-13 dataset. The experimental result shows that the detection rate is 99.9%, which outperforms the conventional method.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2881-2894
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• 2018

Cyber attacks are increasing continuously. On average about one million malicious codes appear every day, and attacks are expanding gradually to IT convergence services (e.g. vehicles and television) and social infrastructure (nuclear energy, power, water, etc.), as well as cyberspace. Analysis of large-scale cyber incidents has revealed that most attacks are started by PCs infected with malicious code. This paper proposes a method of detecting an attack IP automatically by analyzing the characteristics of the e-mail transfer path, which cannot be manipulated by the attacker. In particular, we developed a system based on the proposed model, and operated it for more than four months, and then detected 1,750,000 attack IPs by analyzing 22,570,000 spam e-mails in a commercial environment. A detected attack IP can be used to remove spam e-mails by linking it with the cyber removal system, or to block spam e-mails by linking it with the RBL(Real-time Blocking List) system. In addition, the developed system is expected to play a positive role in preventing cyber attacks, as it can detect a large number of attack IPs when linked with the portal site.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1849-1856
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• 2013

DOS attack, the short of Denial of Service attack is an internet intrusion technique which harasses service availability of legitimate users. To respond the DDoS attack, a lot of methods focusing attack source, target and intermediate network, have been proposed, but there have not been a clear solution. In this paper, we purpose the prevention of malicious activity and early detection of DDoS attack by detecting and removing the activity of botnets, or other malicious codes. For the purpose, the proposed method monitors the network traffic, especially DSN traffic, which is originated from botnets or malicious codes.

• Proceedings of the Korea Institutes of Information Security and Cryptology Conference
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• 2006.06a
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• pp.101-104
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• 2006

유행처럼 시도하던 DDoS 공격, 특정 목적을 위한 트로이 목마, 스팸메일을 통한 악성코드 유포, 개인 정보 유출 등 악의적인 공격들이 공격자에 의해 조종당하는 Bot에 의해 쉽게 이루어지고 있으며, 특별한 해결방안이 있는 것도 아니다. 이러한 Bot들이 공격자를 통해서가 아닌 자체적으로 P2P 네트워크를 자가 형성하여 공격한다면 탐지는 더욱 어려워지게 된다. 이와 같은 자가 형성에 대응한 효율적인 방어책을 로컬 시스템 간의 네트워크 모니터링 에이전트를 도입하여 해결하고, 에이전트들이 자가 형성하여 실시간으로 Bot에 대한 정보를 공유하고, 이 정보를 기반으로 시스템을 감시하여 Bot을 탐지하는 방어책에 대해 본 논문에서 기술한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1801-1816
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• 2014

Because HTTP-related ports are allowed through firewalls, they are an obvious point for launching cyber attacks. In particular, malware uses HTTP protocols to communicate with their master servers. We call this an HTTP-based command and control (C&C) server. Most previous studies concentrated on the behavioral pattern of C&Cs. However, these approaches need a well-defined white list to reduce the false positive rate because there are many benign applications, such as automatic update checks and web refreshes, that have a periodic access pattern. In this paper, we focus on finding new discriminative features of HTTP-based C&Cs by analyzing HTTP activity sets. First, a C&C shows a few connections at a time (low density). Second, the content of a request or a response is changed frequently among consecutive C&Cs (high content variability). Based on these two features, we propose a novel C&C analysis mechanism that detects the HTTP-based C&C. The HAS-Analyzer can classify the HTTP-based C&C with an accuracy of more than 96% and a false positive rate of 1.3% without using any white list.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.6
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• pp.1249-1254
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• 2014

As ICT ecosystem changes, security-related threat on individuals and corporations has increased. With the recent sophistication of hacking strategy, hacking serves commerce and its scale becomes larger than ever. Accordingly, the analysis on cyber intrusion is required. As a number one electronic government around the world, the government's role for security solution for realization of safe electronic government. This manuscript analyzes cyber intrusion cases, speculates the government's measures and suggests political recommendation for the current phenomena.

• The KIPS Transactions:PartC
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• v.18C no.3
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• pp.127-134
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• 2011

Malicious botnet has been used for more malicious activities, such as DDoS attacks, sending spam messages, steal personal information, etc. To prevent this, many studies have been preceded. But malicious botnets have evolved and evaded detection systems. In particular, HTTP GET Request attack that exploits the vulnerability of the application layer is used. ALAB of ALADDIN proposed by ETRI is DDoS attack detection system that HTTP GET, Incomplete GET request flooding attack detection algorithm is applied. In this paper, we extend Incomplete GET detection algorithm of ALAB and derive the optimal configuration parameters to verify the validity of the algorithm ALAB by the study of the normal and attack packets.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.543-546
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• 2023

봇넷은 지속적으로 사이버 범죄에 이용되고 있으며 네트워크 환경에 큰 위협이 되고 있다. 기존에는 봇들이 C&C 서버와 통신하는 것을 방지하기 위해 블랙리스트를 기반으로 DNS 서버에서 봇넷 도메인을 탐지하는 방식을 주로 사용하였다. 그러나 도메인 생성 알고리즘(DGA)을 이용하는 봇넷이 증가하면서 기존에 사용하던 블랙리스트 기반의 도메인 차단 방식으로는 더 이상 봇넷 도메인을 효율적으로 차단하기 어려워졌다. 이에 따라 봇넷 도메인 생성 알고리즘을 통해 생성되는 도메인의 특성을 분석하고 이를 토대로 봇넷 도메인을 식별하고 차단하고자 하는 시도가 계속되고 있다. 특히 연속적인 데이터 처리에 주로 사용되는 딥러닝 알고리즘을 이용하여 봇넷 도메인의 특징을 효과적으로 추출하고 정확도가 높은 탐지 모델을 구축하고자 하는 연구가 주를 이루고 있으며, 탐지뿐만 아니라 봇넷 그룹(Family) 분류까지 연구가 확장되고 있다. 이에 본 논문에서는 봇넷 도메인 생성 알고리즘에 의해 생성되는 봇넷 도메인을 식별 및 분류하기 위해 딥러닝 기술을 적용한 최근 연구 동향을 조사하고 앞으로의 연구 방향성을 논의하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.1
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• pp.61-68
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• 2015

Recently, with increasing use of smartphones, the security threats also have increased rapidly. Especially, the compromised smartphone is very dangerous because it could be exploited in a DDOS attacks such as cyberterrorism as well as in the leakage of personal information. However, most bot detection mechanisms are still unsuitable for smartphone with its lower computing capability and limited battery capacity because they incur additional computational overheads or require pre-defined signatures. In this paper, we present an efficient bot detection mechanism in smartphones. Our mechanism detects effectively bots in outgoing traffic by using a correlation between user events and network traffic. We have implemented its prototype in Android smartphone and measured its performance. The evaluation results show that our mechanism provides low overhead to detect bots in smartphones.