• Convergence Security Journal
• /
• v.11 no.6
• /
• pp.25-30
• /
• 2011

MANET has a weak construction in security more because it is consisted of only moving nodes and doesn't have central management system. The DDoS attack is a serious attack among these attacks which threaten wireless network. The DDoS attack has various object and trick and become intelligent. In this paper, we propose the technique to raise DDoS detection rate by classifying abnormal traffic pattern. Cluster head performs sentinel agent after nodes which compose MANET are made into cluster. The decision tree is applied to detect abnormal traffic pattern after the sentinel agent collects all traffics and it judges traffic pattern and detects attack also. We confirm high attack detection rate of proposed detection technique in this study through experimentation.

• The KIPS Transactions:PartC
• /
• v.12C no.6 s.102
• /
• pp.827-838
• /
• 2005

DDoS (Distributed Denial of Service) attack is a critical threat to current Internet. To solve the detection and response of DDoS attack on BcN, we have investigated detection algorithms of DDoS and Implemented anomaly detection modules. Recently too many technologies of the detection and prevention have developed, but it is difficult that the IDS distinguishes normal traffic from the DDoS attack Therefore, when the DDoS attack is detected by the IDS, the firewall just discards all over-bounded traffic for a victim or absolutely decreases the threshold of the router. That is just only a method for preventing the DDoS attack. This paper proposed the mechanism of response for the legitimated clients to be protected Then, we have designed and implemented the statistic based system that has the automated detection and response functionality against DDoS on Linux Zebra router environment.

• Journal of Information Technology Services
• /
• v.15 no.2
• /
• pp.157-167
• /
• 2016

Task environment for enterprises and public institutions are moving into cyberspace-based environment and structing the LTE wireless network. The applications "App" operated in the LTE wireless network are mostly being developed with Android-based. But Android-based malwares are surging and they are the potential DDoS attacks. DDoS attack is a major information security threat and a means of cyber attacks. DDoS attacks are difficult to detect in advance and to defense effectively. To this end, a DMZ is set up in front of a network infrastructure and a particular server for defensive information security. Because There is the proliferation of mobile devices and apps, and the activation of android diversify DDoS attack methods. a DMZ is a limit to detect and to protect against DDoS attacks. This paper proposes an information security method to detect and Protect DDoS attacks from the terminal phase using a Preemptive military strategy concept. and then DDoS attack detection and protection app is implemented and proved its effectiveness by reducing web service request and memory usage. DDoS attack detection and protecting will ensure the efficiency of the mobile network resources. This method is necessary for a continuous usage of a wireless network environment for the national security and disaster control.

• Journal of the Korea Society for Simulation
• /
• v.19 no.4
• /
• pp.139-149
• /
• 2010

Internet was designed for network scalability and best-effort service which makes all hosts connected to Internet to be vulnerable against attack. Many papers have been proposed about attack detection algorithms against the attack using IP spoofing and DoS/DDoS attack. Purpose of DoS/DDoS attack is achieved in short period after the attack begins. Therefore, DoS/DDoS attack should be detected as soon as possible. Attack detection algorithms using false alarm rates consist of the false negative rate and the false positive rate. Moreover, they are important metrics to evaluate the attack detections. In this paper, we analyze the performance of the attack detection algorithms using the impact of false negative rate and false positive rate variation to the normal traffic and the attack traffic by simulations. As the result of this, we find that the number of passed attack packets is in the proportion to the false negative rate and the number of passed normal packets is in the inverse proportion to the false positive rate. We also analyze the limits of attack detection due to the relation between the false negative rate and the false positive rate. Finally, we propose a solution to minimize the limits of attack detection algorithms by defining the network state using the ratio between the number of packets classified as attack packets and the number of packets classified as normal packets. We find the performance of attack detection algorithm is improved by passing the packets classified as attacks.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.203-209
• /
• 2014

Today, Distributed denial of service (DDoS) attack present a very serious threat to the stability of the internet. The DDoS attack, which is consuming all of the computing or communication resources necessary for the service, is known very difficult to protect. The DDoS attack usually transmits heavy traffic data to networks or servers and they cannot handle the normal service requests because of running out of resources. It is very hard to prevent the DDoS attack. Therefore, an intrusion detection system on large network is need to efficient real-time detection. In this paper, we propose the detection mechanism using analysis of flow information against DDoS attacks in order to guarantee the transmission of normal traffic and prevent the flood of abnormal traffic. The OPNET simulation results show that our ideas can provide enough services in DDoS attack.

• The KIPS Transactions:PartC
• /
• v.14C no.1 s.111
• /
• pp.45-54
• /
• 2007

As one of the most threatening attacks, DDoS attack makes distributed multiple agents consume some critical resources at the target within the short time, thus the extent and scope of damage is serious. Against the problems, the existing defenses focus on detection, traceback (identification), and filtering. Especially, in the hierarchical networks, the traffic congestion of a specific node could incur the normal traffic congestion of overall lower nodes, and also block the control traffic for notifying the attack detection and identifying the attack agents. In this paper, we introduce a DDoS attack tolerant network structure using a hierarchical overlay for hierarchical networks, which can convey the control traffic for defense such as the notification for attack detection and identification, and detour the normal traffic before getting rid of attack agents. Lastly, we analyze the overhead of overlay construction, the possibility of speedy detection notification, and the extent of normal traffic transmission in the attack case through simulation.

• Journal of Digital Convergence
• /
• v.12 no.1
• /
• pp.423-429
• /
• 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.

• KNOM Review
• /
• v.23 no.1
• /
• pp.10-17
• /
• 2020

As the threat of Distributed Denial-of-Service attacks that exploit weakly secure IoT devices has spread, research on source-side Denial-of-Service attack detection is being activated to quickly detect the attack and the location of attacker. In addition, a collaborative source-side attack detection technique that shares detection results of source-side networks located at individual sites is also being activated to overcome regional limitations of source-side detection. In this paper, we evaluate the performance of a collaborative source-side DDoS attack detection using statistical weights. The statistical weight is calculated based on the detection rate and false positive rate corresponding to the time zone of the individual source-side network. By calculating weighted sum of the source-side DoS attack detection results from various sites, the proposed method determines whether a DDoS attack happens. As a result of the experiment based on actual DNS request to traffic, it was confirmed that the proposed technique reduces false positive rate 2% while maintaining a high attack detection rate.

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

• The KIPS Transactions:PartC
• /
• v.11C no.6 s.95
• /
• pp.711-718
• /
• 2004

Packet sampling is the techniques to collect a part of the packets through network and analyze the characteristicsof the traffic for managing the network and keeping security. This paper presents a study on the sampling techniques applied to DDoS traffic and on the characteristics of the sampled traffic to detect DDoS attack efficiently and improve traffic analysis capacity. Three famous sampling techniques are evaluated with different sampling rates on various DDoS traffics. To analyze traffic characteristics, one of the DDoS attack detection method. Traffic Rate Analysis (TRA) is used. Simulation results verify that using sampling techniques preserve the traffic characteristics of DDoS and do not significantly reduce the detection accuracy.