DDoS Attack detection method and mitigation using pattern of the flow

Sanmorino, Ahmad; Yazid, Setiadi

doi:10.1109/icoict.2013.6574541

Cited by 29 publications

(15 citation statements)

References 6 publications

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“…However, the method cannot support all additional requirements. b) Use flow-level data: Sanmorino and Yazid [65] proposed a protocol independent detection method that uses flow patterns. All incoming packets with identical 5 tuple (viz.…”

Section: A Detection Methods Against Network/transport Layer Ddos Flmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

101

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Section: A Detection Methods Against Network/transport Layer Ddos Flmentioning

confidence: 99%

Security Data Collection and Data Analytics in the Internet: A Survey

Jing

Yan

Pedrycz

2019

IEEE Commun. Surv. Tutorials

101

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“…SYN flood [4] attack exploits the weakness in TCP protocol (Three way handshaking) [5]. SYN packet is a type of request packet to create a connection.…”

Section: Syn Floodingmentioning

confidence: 99%

Characterization, Detection and Mitigation of Low-Rate DoS attack

Kumawat

Meena

2014

Proceedings of the 2014 International Conference on Information and Communication Technology for Competitive Strategies

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Now a day's web services become key aspect of life. Unfortunately there are several threats to these services. These threats are phishing, e-mail borne viruses, Trojan horse programs, Denial of Service etc. Among of them Distributed Denial of Service attack is a strong and more energetic attack on the Internet. Launching of DDoS attack is an explicit attempt to exhaust the resources of server to prevent legitimate users to access services. The common flooding DDoS attacks can be detected and mitigated by some mechanisms easily cause of the characteristics as flow rate, size of attack packets, but still it is difficult to detect and identifies the Low-Rate DoS attack because the attacker periodically send short burst packets which behave as legitimate traffic to the server. The attack is only detected when the server goes down. So an efficient detection system is required which effectively Characterize the legitimate and attack traffic to detect the attack and stop it to mitigate the effect of these sort of threats. In this article we proposed a complete framework for "Characterization, Detection and Mitigation of Low-Rate DoS Attacks" which effectively characterize the flows as attack or legitimate, detects the low-rate DoS attack on the basis of characteristics of low rate and mitigate the effect of this by stopping the attack flow near the source. The effectiveness of this approach have validated by simulation in ns-2, with Shell scripting, on a Linux platform.

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“…This work finished the job in less time compared with the other detection process because it didn't maintain the list of source IP's and it uses non-parametric change point modelling technique for smelling the flooding attacks on a real-time process. The author [13] has proposed the new concept for detection the DDoS attack using pattern matching process in the flow of the packets and also used for improving the firewall services. This method is successfully filtered the unwanted attacking packets.…”

Section: Introductionmentioning

confidence: 99%

Chi-Square and Entropy (CS-E):A Hybrid Method for DDoS Attack Detection and Trace Back

2019

IJRTE

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Internet becomes unavoidable and it provides us with a wealth of information and allows us to keep in touch with the outside world. However, there can also be risks on the internet that is, for example, even a naive hacker can access information and easily learn to generate a large scale DDoS attack with the help of downloadable user-friendly attacking tools. Nowadays, this has made even small businesses in trouble. One of the extensive DDoS attacks was done on October 2016 which is called “Mirai botnet”. In that, the attackers send 30 million packets per second to attack the financial department, industries, home system, etc. were affected. In the future, the attackers may hit the hardest even as banks, government sectors, and corporate sectors, etc. On DDoS attack time, the attackers are sending a lot of malicious packets to the server/victims. So the attacker’s throughput is increased and legitimate user throughput is decreased on time of the attack. In this paper, a novel approach is proposed to detect the DDoS attacks using Chi-Square method which compares the normal packets and current packets statistics to discriminate whether the particular flow is DDoS or not. Further; it identifies the IP address of attacking source using entropy statistic. The proposed method can be used to control internet crimes. The experimental results show that the proposed method outperforms the existing approaches by detecting the DDoS attack and also by identifying the wrongdoer IP address. In addition, it takes minimum time to perform the above.

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DDoS Attack detection method and mitigation using pattern of the flow

Cited by 29 publications

References 6 publications

Security Data Collection and Data Analytics in the Internet: A Survey

Security Data Collection and Data Analytics in the Internet: A Survey

Characterization, Detection and Mitigation of Low-Rate DoS attack

Chi-Square and Entropy (CS-E):A Hybrid Method for DDoS Attack Detection and Trace Back

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