• Journal of Korea Multimedia Society
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• v.13 no.4
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• pp.594-600
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• 2010

Many studies are DDoS in Internet network, but the study is the fact that is not enough in a voice network. Therefore, we designed the extended TRW algorithm that was a DDoS attack traffic detection algorithm for the voice network which used an IP data network to solve upper problems in this article and evaluated it. The algorithm that is proposed in this paper analyzes TRW algorithm to detect existing DDoS attack in Internet network and, design connection and end connection to apply to a voice network, define probability function to count this. For inspect the algorithm, Set a threshold and using NS-2 Simulator. We measured detection rate by an attack traffic type and detection time by attack speed. At the result of evaluation 4.3 seconds for detection when transmitted INVITE attack packets per 0.1 seconds and 89.6% performance because detected 13,453 packet with attack at 15,000 time when transmitted attack packet.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.12
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• pp.2555-2562
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• 2009

The algorithm that is proposed in this paper defined a probability function to count connection process and connection-end process to apply TRW algorithm to voice network. Set threshold to evaluate the algorithm that is proposed, Based on the type of connection attack traffic changing the probability to measure the effectiveness of the algorithm, and Attack packets based on the speed of attack detection time was measured. At the result of evaluation, proposed algorithm shows that DDoS attack starts at 10 packets per a second and it detects the attack after 1.2 seconds from the start. Moreover, it shows that the algorithm detects the attack in 0.5 second if the packets were 20 per a second.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.1
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• pp.39-52
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• 2011

An application-layer attack can effectively achieve its objective with a small amount of traffic, and detection is difficult because the traffic type is very similar to that of legitimate users. We have discovered a unique characteristic that is produced by a difference in client intention: Both a legitimate user and DDoS attacker establish a session through a 3-way handshake over the TCP/IP layer. After a connection is established, they request at least one HTTP service by a Get request packet. The legitimate HTTP user waits for the server's response. However, an attacker tries to terminate the existing session right after the Get request. These different actions can be interpreted as a difference in client intention. In this paper, we propose a detection algorithm for application layer DDoS attacks based on this difference. The proposed algorithm was simulated using traffic dump files that were taken from normal user networks and Botnet-based attack tools. The test results showed that the algorithm can detect an HTTP-Get flooding attack with almost zero false alarms.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.374-386
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• 2022

Nowadays, research in deep learning leveraged automated computing and networking paradigm evidenced rapid contributions in terms of Software Defined Networking (SDN) and its diverse security applications while handling cybercrimes. SDN plays a vital role in sniffing information related to network usage in large-scale data centers that simultaneously support an improved algorithm design for automated detection of network intrusions. Despite its security protocols, SDN is considered contradictory towards DDoS attacks (Distributed Denial of Service). Several research studies developed machine learning-based network intrusion detection systems addressing detection and mitigation of DDoS attacks in SDN-based networks due to dynamic changes in various features and behavioral patterns. Addressing this problem, this research study focuses on effectively designing a multistage hybrid and intelligent deep learning classifier based on modified deep forest classification to detect DDoS attacks in SDN networks. Experimental results depict that the performance accuracy of the proposed classifier is improved when evaluated with standard parameters.

• 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.

• Convergence Security Journal
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• v.23 no.5
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• pp.73-79
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• 2023

Cyber threats are evolving and becoming more sophisticated with the development of new technologies, and consequently the number of service failures caused by DDoS attacks are continually increasing. Recently, DDoS attacks have numerous types of service failures by applying a large amount of traffic to the domain address of a specific service or server. In this paper, after generating the data of the Syn Flooding attack, which is the representative attack type of bandwidth exhaustion attack, the data were compared and analyzed using Random Forest, Decision Tree, Multi-Layer Perceptron, and KNN algorithms for the effective detection of attacks, and the optimal algorithm was derived. Based on this result, it will be useful to use as a technique for the detection policy of Syn Flooding attacks.

• 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.11 no.4
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• pp.2149-2170
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• 2017

Application-layer Distributed Denial-of-Service (DDoS) attack is one of the leading security problems in the Internet. In recent years, the attack strategies of application-layer DDoS have rapidly developed. This paper introduces a new attack strategy named Path Vulnerabilities-Based (PVB) attack. In this attack strategy, an attacker first analyzes the contents of web pages and subsequently measures the actual response time of each webpage to build a web-resource-weighted-directed graph. The attacker uses a Top M Longest Path algorithm to find M DDoS vulnerable paths that consume considerable resources when sequentially accessing the pages following any of those paths. A detection mechanism for such attack is also proposed and discussed. A finite-state machine is used to model the dynamical processes for the state of the user's session and monitor the PVB attacks. Numerical results based on real-traffic simulations reveal the efficiency of the attack strategy and the detection mechanism.

• The KIPS Transactions:PartC
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• v.16C no.1
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• pp.13-20
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• 2009

Recently, as traffic flooding attacks such as DoS/DDoS and Internet Worm have posed devastating threats to network services, rapid detection and proper response mechanisms are the major concern for secure and reliable network services. However, most of the current Intrusion Detection Systems (IDSs) focus on detail analysis of packet data, which results in late detection and a high system burden to cope with high-speed network traffic. In this paper we propose an SNMP-based lightweight and fast detection algorithm for traffic flooding attacks, which minimizes the processing and network overhead of the detection system, minimizes the detection time, and provides high detection rate. The attack detection algorithm consists of three consecutive stages. The first stage determines the detection timing using the update interval of SNMP MIB. The second stage analyzes attack symptoms based on correlations of MIB data. The third stage determines whether an attack occurs or not and figure out the attack type in case of attack.