• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2750-2763
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• 2012

The Domain Name System (DNS) is a critical Internet infrastructure that provides name to address mapping services. In the past decade, Denial-of-Service (DoS) attacks have targeted the DNS infrastructure and threaten to disrupt this critical service. While the flooding DoS attacks may be alleviated by the DNS caching mechanism, we show in this paper that flooding DoS attacks utilizing name error queries is capable of bypassing the cache of resolvers and thereby impose overwhelming flooding attacks on the name servers. We analyze the impacts of such DoS attacks on both name servers and resolvers, which are further illustrated by May 19 China's DNS Collapse. We also propose the detection and defense approaches for protecting DNS servers from such DoS attacks. In the proposal, the victim zones and attacking clients are detected through monitoring the number of corresponding responses maintained in the negative cache. And the attacking queries can be mitigated by the resolvers with a sample proportion adaptive to the percent of queries for the existent domain names. We assess risks of the DoS attacks by experimental results. Measurements on the request rate of DNS name server show that this kind of attacks poses a substantial threat to the current DNS service.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.7
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• pp.1493-1500
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• 2004

Today's Commercial security server system which provide secrecy, integrity and availability may still be vulnerable to denial-of-service attacks. Authentication system whith use a public key cryptography and process RSA encryption is relatively slow and the slowness has become a major security threat specifically in service flooding attacks caused by authentication requests. The service flooding attacks render the server incapable of providing its service to legitimitive clients. Therefore the importance of implementing systems that prevent denial of service attacks and provide service to legitimitive users cannot be overemphasized. In this paper, we propose a puzzle protocol which applies to authentication model. our gradually strengthening authentication model improves the availability and continuity of services and prevent denial of service attacks and we implement flooding of service tolerance system to verify the efficiency of our model. This system is expected to be ensure in the promotion of reliability.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.276-282
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• 2022

One of the most essential foundations upon which big institutions rely in delivering cloud computing and hosting services, as well as other kinds of multiple digital services, is the security of infrastructures for digital and information services throughout the world. Distributed denial-of-service (DDoS) assaults are one of the most common types of threats to networks and data centers. Denial of service attacks of all types operates on the premise of flooding the target with a massive volume of requests and data until it reaches a size bigger than the target's energy, at which point it collapses or goes out of service. where it takes advantage of a flaw in the Transport Control Protocol's transmitting and receiving (3-way Handshake) (TCP). The current study's major focus is on an architecture that stops DDoS attacks assaults by producing code for DDoS attacks using a cloud controller and calculating Round-Tripe Time (RTT).

• Journal of the Korea Society of Computer and Information
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• v.14 no.9
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• pp.55-66
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• 2009

In this paper, I will discuss how the Internet has spread rapidly in our lives. Large portals and social networks experience service attacks that access personal customers' databases. This interferes with normal service through DDoS (Distribute Denial of Service Attack), which is the topic I want to discuss. Among the types of DDoS, TCP SYN Flooding attacks are rarely found because they use few traffics and its attacking type is regular transaction. The purpose of this study is to find and suggest the method for accurate detection of the attacks. Through the analysis of TCP SYN Flooding attacks, we find that these attacks cause Backscatter effect. This study is about the algorithm which detects the attacks of TCP SYN Flooding by the study of Backscatter effect.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.7
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• pp.1831-1853
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• 2012

By sending periodically short bursts of traffic to reduce legit transmission control protocol (TCP) traffic, the low-rate denial of service (LDoS) attacks are hard to be detected and may endanger covertly a network for a long period. Traditionally, LDoS detecting methods mainly concentrate on the attack stream with feature matching, and only a limited number of attack patterns can be detected off-line with high cost. Recent researches divert focus from the attack stream to the traffic anomalies induced by LDoS attacks, which can detect more kinds of attacks with higher efficiency. However, the limited number of abnormal characteristics and the inadequacy of judgment rules may cause wrong decision in some particular situations. In this paper, we address the problem of detecting LDoS attacks and present a scheme based on the fluctuant features of legit TCP and acknowledgment (ACK) traffic. In the scheme, we define judgment criteria which used to identify LDoS attacks in real time at an optimal detection cost. We evaluate the performance of our strategy in real-world network topologies. Simulations results clearly demonstrate the superiority of the method proposed in detecting LDoS attacks.

• ETRI Journal
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• v.42 no.3
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• pp.433-445
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• 2020

A denial-of-service (DoS) attack is a serious attack that targets web applications. According to Imperva, DoS attacks in the application layer comprise 60% of all the DoS attacks. Nowadays, attacks have grown into application- and business-layer attacks, and vulnerability-analysis tools are unable to detect business-layer vulnerabilities (logic-related vulnerabilities). This paper presents the business-layer dynamic application security tester (BLDAST) as a dynamic, black-box vulnerability-analysis approach to identify the business-logic vulnerabilities of a web application against DoS attacks. BLDAST evaluates the resiliency of web applications by detecting vulnerable business processes. The evaluation of six widely used web applications shows that BLDAST can detect the vulnerabilities with 100% accuracy. BLDAST detected 30 vulnerabilities in the selected web applications; more than half of the detected vulnerabilities were new and unknown. Furthermore, the precision of BLDAST for detecting the business processes is shown to be 94%, while the generated user navigation graph is improved by 62.8% because of the detection of similar web pages.

• Convergence Security Journal
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• v.8 no.3
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• pp.1-8
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• 2008

The pattern matching part of Intrusion Detection System based on misuse-detection mechanism needs much processing time and resources, and it has become a bottleneck in system performance. Moreover, it derives denial-of-service attack. In this paper, we propose (1) framework architecture that is strong against denial-of-service attack and (2) efficient pattern matching method especially for web server system. By using both of these 2 methods, we can maintain web server system efficiently secure against attacks including denial-of-service.

• Convergence Security Journal
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• v.12 no.6
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• pp.101-106
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• 2012

As the existing strategy of preventing DDoS(Distributed Denial of Service) attacks has limitations, this study is intended to suggest the more effective method of preventing DDoS attacks which reduces attack power and distributes attack targets. Currently, DDoS attacks have a wide range of targets such as individuals, businesses, labs, universities, major portal sites and financial institutions. In addition, types of attacks change from exhausting layer 3, network band to primarily targeting layer 7. In response to DDoS attacks, this study suggests how to distribute and decrease DDoS threats effectively and efficiently using Proxy Server Group and Dynamic DNS.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.5256-5262
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• 2014

Recently, Distributed Denial of Service (DDoS) attacks, such as spreading malicious code, cyber-terrorism, have occurred in government agencies, the press and the financial sector. DDoS attacks are the simplest Internet-based infringement attacks techniques that have fatal consequences. DDoS attacks have caused bandwidth consumption at the network layer. These attacks are difficult to detect defend against because the attack packets are not significantly different from normal traffic. Abnormal traffic is threatening the stability of the network. Therefore, the abnormal traffic by generating indications will need to be detected in advance. This study examined the abnormal traffic detection technique using a forecasting model-based trend model.