• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.807-816
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• 2015

During the last decade most of coordinated security breaches are performed by the means of botnets, which is a large overlay network of compromised computers being controlled by remote botmaster. Due to high volumes of traffic to be analyzed, the challenge is posed by managing tradeoff between system scalability and accuracy. We propose a novel Hadoop-based P2P botnet detection method solving the problem of scalability and having high accuracy. Moreover, our approach is characterized not to require labeled data and applicable to encrypted traffic as well.

• 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.

• Journal of Korea Multimedia Society
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• v.16 no.6
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• pp.678-688
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• 2013

Different Different from a single attack, in DDoS Attacks, the botnets that are distributed on network initiate attacks against the target server simultaneously. In such cases, it is difficult to take an action while denying the access of packets that are regarded as DDoS since normal user's convenience should also be considered at the target server. Taking these considerations into account, the DDoS botnet detection system that can reduce the strain on the target server by detecting DDoS attacks on each user network basis, and then lets the network administrator to take actions that reduce overall scale of botnets, has been implemented in this study. The DDoS botnet detection system proposed by this study implemented the program which detects attacks based on the database composed of faults and abnormalities collected through analyzation of hourly attack traffics. The presence of attack was then determined using the threshold of current traffic calculated with the standard deviation and the mean number of packets. By converting botnet-based detection method centering around the servers that become the targets of attacks to the network based detection, it was possible to contemplate aggressive defense concept against DDoS attacks. With such measure, the network administrator can cut large scale traffics of which could be referred as the differences between DDoS and DoS attacks, in advance mitigating the scale of botnets. Furthermore, we expect to have an effect that can considerably reduce the strain imposed on the target servers and the network loads of routers in WAN communications if the traffic attacks can be blocked beforehand in the network communications under the router equipment level.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.5
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• pp.1037-1046
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• 2024

The widespread adoption of the Internet of Things (IoT) has enhanced efficiency and convenience across various fields, but it has also led to a surge in security threats. Among these, IoT botnets are particularly concerning as they can rapidly infect a large number of devices and launch various types of attacks, making them a significant security threat. In IoT environments where implementing security measures on individual devices is challenging, establishing a security monitoring system for real-time detection and response is essential to mitigate the risks posed by botnets. In the field of security monitoring, it is crucial not only to detect botnets but also to analyze their detailed behaviors to devise effective countermeasures. Security experts devote considerable effort to analyzing the payloads of detected threats to understand botnet behavior and develop appropriate responses. However, analyzing all threats manually is time-consuming and costly. To address this, our study proposes an XAI-based network feature extraction methodology to enhance the effectiveness of IoT botnet behavior analysis. This study proposes a practical security monitoring methodology for IoT botnet behavior analysis and response, consisting of three steps: 1) BPE and TF-IDF based payload feature extraction, 2) XAI-based feature importance analysis, and 3) visualization of decision rationale based on feature importance. This approach provides security experts with intuitive visual evidence of IoT attacks and reduces analysis time, contributing to faster decision-making and response strategy development in security monitoring.

• 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 Security & Cryptology
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• v.21 no.3
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• pp.101-115
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• 2011

A botnet is a huge network of hacked zombie PCs. Recognizing the fact that the majority of email spam is sent out by botnets, a system that is capable of detecting botnets and zombie PCS will be designed in this study by analyzing email spam. In this study, spam data collected in "an email spam trail system", Korea's national spam collection system, were used for analysis. In this study, we classified the spam groups by the URLs or attached files, and we measured how much the group has the characteristics of botnet and how much the IPs have the characteristics of zombie PC. Through the simulation result in this study, we could extract 16,030 zombie suspected PCs for one hours and it was verified that email spam can provide considerably useful information in tracing zombie PCs.

• Journal of Internet Computing and Services
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• v.23 no.5
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• pp.103-110
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• 2022

Botnets have been exploited for a variety of purposes, ranging from monetary demands to national threats, and are one of the most threatening types of attacks in the field of cybersecurity. Botnets emerged as a centralized structure in the early days and then evolved to a P2P structure. Bitcoin is the first online cryptocurrency based on blockchain technology announced by Satoshi Nakamoto in 2008 and is the most widely used cryptocurrency in the world. As the number of Bitcoin users increases, the size of Bitcoin network is also expanding. As a result, a botnet using the Bitcoin network as a C&C channel has emerged, and related research has been recently reported. In this study, we propose an encrypted botnet C&C communication mechanism and technique in the Bitcoin network and validate the proposed method by conducting performance evaluation through various experiments after building it on the Bitcoin testnet. By this research, we want to inform the possibility of botnet threats in the Bitcoin network to researchers.

• 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.

• 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.