• Journal of information and communication convergence engineering
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• v.21 no.3
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• pp.192-197
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• 2023

A Distributed reflection denial of service (DRDoS) is a variant of DDoS attacks that threatens the availability of services to legitimate users. In response to this evolving threat landscape, the cybersecurity industry and service providers have intensified their efforts to develop effective countermeasures. Despite these efforts, attackers continue to innovate, developing new strategies and tools while becoming more sophisticated. Consequently, DRDoS attacks continue to be harmful. Therefore, ongoing research and development is essential to improve defense against DRDoS attacks. To advance our understanding and analysis of DRDoS attacks, this study examines the unique characteristics of DRDoS attacks and quantifies the risks involved. Additionally, it adopts a quantitative rather than traditional qualitative methods to derive and apply risk, particularly the probability of loss that can be caused by DRDoS attacks.

• Journal of the Korea Society of Computer and Information
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• v.27 no.7
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• pp.101-108
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• 2022

We propose an intrusion detection model that detects denial-of-service(DoS) and distributed reflection denial-of-service(DRDoS) attacks, based on the empirical data of each internet of things(IoT) device by training system and network metrics that can be commonly collected from various IoT devices. First, we collect 37 system and network metrics from each IoT device considering IoT attack scenarios; further, we train them using six types of machine learning models to identify the most effective machine learning models as well as important metrics in detecting and distinguishing IoT attacks. Our experimental results show that the Random Forest model has the best performance with accuracy of over 96%, followed by the K-Nearest Neighbor model and Decision Tree model. Of the 37 metrics, we identified five types of CPU, memory, and network metrics that best imply the characteristics of the attacks in all the experimental scenarios. Furthermore, we found out that packets with higher transmission speeds than larger size packets represent the characteristics of DoS and DRDoS attacks more clearly in IoT networks.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.143-145
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• 2022

With the spread of the Internet around the world, devices connected to the Internet are gradually increasing. In addition, the number of distributed reflection service attacks (DrDoS), an attack that maliciously requests large responses by deceiving IPs as if the attacker was a victim, using vulnerabilities in application protocols such as DNS, NTP, and CLDAP, is increasing rapidly. It is believed that the security threat of distributed reflection service attacks will not disappear unless ISPs establish appropriate countermeasures to IP Spoofing. Therefore, this paper describes the security threat and response status of distributed reflection service attacks based on IP Spoofing.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.108-110
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• 2021

Since 2015, attacks using the IoT protocol have been continuously reported. Among various IoT protocols, attackers attempt DDoS attacks using SSDP(Simple Service Discovery Protocol), and as statistics of cyber shelters, Korea has about 1 million open SSDP servers. Vulnerable SSDP servers connected to the Internet can generate more than 50Gb of traffic and the risk of attack increases gradually. Until recently, distributed denial of service attacks and distributed reflective denial of service attacks have been a security issue. Accordingly, the purpose of this study is to analyze the request packet of the existing SSDP protocol to identify an amplification attack and to avoid a response when an amplification attack is suspected, thereby preventing network load due to the occurrence of a large number of response packets due to the role of traffic reflection amplification.

• 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.