• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.81-90
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• 2014

As mobile devices have become widely used and developed, PC based malwares can be moving towards mobile-based units. In particular, mobile Botnet reuses powerful malicious behavior of PC-based Botnet or add new malicious techniques. Different from existing PC-based Botnet detection schemes, mobile Botnet detection schemes are generally host-based. It is because mobile Botnet has various attack vectors and it is difficult to inspect all the attack vector at the same time. In this paper, to overcome limitations of host-based scheme, we compare two network-based schemes which detect mobile Botnet by applying HMM and SVM techniques. Through the verification analysis under real Botnet attacks, we present detection rates and detection properties of two schemes.

• Journal of the Korea Society of Computer and Information
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• v.22 no.3
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• pp.69-79
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• 2017

In this paper, we propose our four-phase life cycle of P2P botnet with corresponding detection methods and the future direction for more effective P2P botnet detection. Our proposals are based on the intensive analysis that compares existing P2P botnet detection schemes in different points of view such as life cycle of P2P botnet, machine learning methods for data mining based detection, composition of data sets, and performance matrix. Our proposed life cycle model composed of linear sequence stages suggests to utilize features in the vulnerable phase rather than the entire life cycle. In addition, we suggest the hybrid detection scheme with data mining based method and our proposed life cycle, and present the improved composition of experimental data sets through analysing the limitations of previous works.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.205-211
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• 2024

The increasing number of botnet attacks incorporating new evasion techniques making it infeasible to completely secure complex computer network system. The botnet infections are likely to be happen, the timely detection and response to these infections helps to stop attackers before any damage is done. The current practice in traditional IP networks require manual intervention to response to any detected malicious infection. This manual response process is more probable to delay and increase the risk of damage. To automate this manual process, this paper proposes to automatically select relevant countermeasures for detected botnet infection. The propose approach uses the concept of flow trace to detect botnet behavior patterns from current and historical network activity. The approach uses the multiclass machine learning based approach to detect and classify the botnet activity into IRC, HTTP, and P2P botnet. This classification helps to calculate the risk score of the detected botnet infection. The relevant countermeasures selected from available pool based on risk score of detected infection.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.3
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• pp.37-44
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• 2011

Recently malicious activities that is a DDoS, spam, propagation of malware, steeling person information, phishing on the Internet are related malicious botnet. To detect malicious botnet, Many researchers study a detection system for malicious botnet, but these applies specific protocol, action or attack based botnet. In this reason, we study a selection of measurement to detec malicious botnet in this paper. we collect a traffic of malicious botnet and analyze it for feature of network traffic. And we select a feature based measurement. we expect to help a detection of malicious botnet through this study.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.704-719
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• 2024

Botnet pandemics are becoming more prevalent with the growing use of mobile phone technologies. Mobile phone technologies provide a wide range of applications, including entertainment, commerce, education, and finance. In addition, botnet refers to the collection of compromised devices managed by a botmaster and engaging with each other via a command server to initiate an attack including phishing email, ad-click fraud, blockchain, and much more. As the number of botnet attacks rises, detecting harmful activities is becoming more challenging in handheld devices. Therefore, it is crucial to evaluate mobile botnet assaults to find the security vulnerabilities that occur through coordinated command servers causing major financial and ethical harm. For this purpose, we propose a hybrid analysis approach that integrates permissions and API and experiments on the machine-learning classifiers to detect mobile botnet applications. In this paper, the experiment employed benign, botnet, and malware applications for validation of the performance and accuracy of classifiers. The results conclude that a classifier model based on a simple decision tree obtained 99% accuracy with a low 0.003 false-positive rate than other machine learning classifiers for botnet applications detection. As an outcome of this paper, a hybrid approach enhances the accuracy of mobile botnet detection as compared to static and dynamic features when both are taken separately.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.1497-1499
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• 2009

최근 들어 스팸 메일, 키 로깅, DDoS와 같은 공격에 Botnet이 사용되고 있다. Botnet은 크래커에 의해 명령, 제어되는 Bot에 감염된 클라이언트로 이루어진 네트워크이다. 지금까지 유선망의 Botnet을 탐지하기 위한 많은 기법이 제안되었지만, 현재 많은 개발이 이루어지고 있는 6LoWPAN과 같은 무선 센서 네트워크상의 Botnet에 관한 연구와 그 대처방안은 전무한 상태이다. 본 논문에서는 6LoWPAN 환경에서 Botnet이 얼마나 위험할 수 있는지 살펴보고 이를 탐지하기 위한 메커니즘을 제안하고자 한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2591-2611
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• 2020

Botnets have become one of the most significant threats to Internet-connected smartphones. A botnet is a combination of infected devices communicating through a command server under the control of botmaster for malicious purposes. Nowadays, the number and variety of botnets attacks have increased drastically, especially on the Android platform. Severe network disruptions through massive coordinated attacks result in large financial and ethical losses. The increase in the number of botnet attacks brings the challenges for detection of harmful software. This study proposes a smart framework for mobile botnet detection using static analysis. This technique combines permissions, activities, broadcast receivers, background services, API and uses the machine-learning algorithm to detect mobile botnets applications. The prototype was implemented and used to validate the performance, accuracy, and scalability of the proposed framework by evaluating 3000 android applications. The obtained results show the proposed framework obtained 98.20% accuracy with a low 0.1140 false-positive rate.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.123-132
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• 2021

Malicious activities of Botnets are responsible for huge financial losses to Internet Service Providers, companies, governments and even home users. In this paper, we try to confirm the possibility of detecting botnet traffic by applying the deep learning model Convolutional Neural Network (CNN) using the CTU-13 botnet traffic dataset. In particular, we classify three classes, such as the C&C traffic between bots and C&C servers to detect C&C servers, traffic generated by bots other than C&C communication to detect bots, and normal traffic. Performance metrics were presented by accuracy, precision, recall, and F1 score on classifying both known and unknown botnet traffic. Moreover, we propose a stackable botnet detection system that can load modules for each botnet type considering scalability and operability on the real field.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.10
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• pp.4176-4197
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• 2020

Botnet is a type of dangerous malware. Botnet attack with a collection of bots attacking a similar target and activity pattern is called bot group activities. The detection of bot group activities using intrusion detection models can only detect single bot activities but cannot detect bots' behavioral relation on bot group attack. Detection of bot group activities could help network administrators isolate an activity or access a bot group attacks and determine the relations between bots that can measure the correlation. This paper proposed a new model to measure the similarity between bot activities using the intersections-probability concept to define bot group activities called as B-Corr Model. The B-Corr model consisted of several stages, such as extraction feature from bot activity flows, measurement of intersections between bots, and similarity value production. B-Corr model categorizes similar bots with a similar target to specify bot group activities. To achieve a more comprehensive view, the B-Corr model visualizes the similarity values between bots in the form of a similar bot graph. Furthermore, extensive experiments have been conducted using real botnet datasets with high detection accuracy in various scenarios.