• Convergence Security Journal
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• v.9 no.2
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• pp.41-48
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• 2009

Distributed denial of service attack detection for more development and research is underway. The method of using statistical techniques, the normal packets and abnormal packets to identify efficient. In this paper several statistical techniques, using a mix of various offers a way to detect the attack. To verify the effectiveness of the proposed technique, it set packet filtering on router and the proposed DDoS attacks detection method on a Linux router. In result, the proposed technique was detect various attacks and provide normal service mostly.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11c
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• pp.1985-1988
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• 2003

As the Distributed Denial of Service attack technique is getting smarter, defense method have been developed by various means. Existing defense method baseds on detection technique is not effective to DDoS attack. Because it depend on rule set that is used to detect attack and DDoS attack pattern has become very similar to real traffic pattern. So the rule set is not efficient method to find DDoS attack. To solve this problem, DDoS defense mechanism based on QoS technique has been suggested. In this paper, we summarize existing DDoS defense mechanism and focus on method based on QoS, and introduce a new DDoS defense framework.

• 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.

• Convergence Security Journal
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• v.9 no.4
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• pp.7-12
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• 2009

It is very difficult to completely block the DDoS attack, which paralyzes services by depleting resources or occupying the network bandwidth by transmitting a vast amount of traffic to the specific website or server from normal users' PCs that have been already infected by an outside attacker. In order to defense or endure the DDoS attack, we usually use various solutions such as IDS (Intrusion Detection System), IPS (Intrusion Prevention System), ITS (Intrusion Tolerance System), FW (Firewall), and the dedicated security equipment against DDoS attack. However, diverse types of security appliances cause the cost problem, besides, the full function of the equipments are not performed well owing to the unproper setting without considering connectivity among systems. In this paper, we present the effective connectivity of security equipments and countermeasure methodology against DDoS attack. In practice, it is approved by experimentation that this designed methdology is better than existing network structure in the efficiency of block and endurance. Therefore, we would like to propose the effective security control measures responding and enduring against discriminated DDoS attacks through this research.

• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3B
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• pp.152-158
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• 2008

Recently, DDoS (Distributed Denial of Service) attack has emerged as one of the major threats and it's main characteristic is to send flood of data packets toward a specific victim. Thus, several attack detection schemes which monitor the destination IP address of packets have been suggested. The existing Bloom Filter based attack detection scheme is simple and can support real-time monitoring. However, since this scheme monitors the separate fields of destination IP address independently, wrong detection is comparatively high. In this paper, in order to solve this drawback, an efficient Bloom Filter based destination address monitoring scheme is proposed, which monitors not only separate fields but also relationship among separate fields. In the results of simulation, the proposed monitoring scheme outperforms the existing Bloom Filter based detection scheme. Also, to improve the correctness of detection, multi-layerd structure is proposed and the correctness of result is improved according to the number of layers and extra tables.

• 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.

• 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.

• 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.

• 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.