A survey of modern IP traceback methodologies

Balyk, Anatolii; Iatsykovska, Uliana; Karpinski, Mikolaj; Khokhlachova, Yuliia; Shaikhanova, Aigul; Korkishko, Lesia

doi:10.1109/idaacs.2015.7340783

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…• Attack source identification uses mechanisms to find the source IP address to block them. IP Traceback [4] is the most popular mechanism to identify the attacker source IP address.…”

Section: Ddos Defense Mechanismmentioning

confidence: 99%

Preventing DDoS using Bloom Filter: A Survey

Patgiri¹,

Nayak²,

Borgohain³

2018

ICST Transactions on Scalable Information Systems

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Distributed Denial-of-Service (DDoS) is a menace for service provider and prominent issue in network security. Defeating or defending the DDoS is a prime challenge. DDoS make a service unavailable for a certain time. This phenomenon harms the service providers, and hence, causes loss of business revenue. Therefore, DDoS is a grand challenge to defeat. There are numerous mechanism to defend DDoS, however, this paper surveys the deployment of Bloom Filter in defending the DDoS attack. The Bloom Filter is a probabilistic data structure for membership query that returns either true or false. Bloom Filter uses tiny memory to store information of large data. Therefore, packet information is stored in Bloom Filter to defend and defeat DDoS. This paper presents a survey on DDoS defending technique using Bloom Filter.

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“…• Attack source identification uses mechanisms to find the source IP address to block them. IP Traceback [4] is the most popular mechanism to identify the attacker source IP address.…”

Section: Ddos Defense Mechanismmentioning

confidence: 99%

Preventing DDoS using Bloom Filter: A Survey

Patgiri¹,

Nayak²,

Borgohain³

2018

ICST Transactions on Scalable Information Systems

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“…Currently, several defense mechanisms have been proposed for the SYN flooding attacks such as Syn cookies [9], Syn cache [10], Synkill [11], Syn proxying [12]. However to locate the flooding sources, the former mechanisms have to rely on the expensive IP traceback such as, hop-by-hop tracing, ICMP-based traceback, and Packet marking [13].…”

Section: Introductionmentioning

confidence: 99%

Detection of Distributed Denial of Service Flooding Attack Using Odds Ratio

Nashat¹,

Hussain²,

Jiang³

2021

J-NaNA

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Computer networks are vulnerable to many types of attacks while the Distributed Denial of Service attack (DDoS) serves as one of the top concerns for security professionals. The DDoS flooding attack denies the services by consuming the server resources to prevent the legitimate users from using their desired services. The hardness of detecting this attack lies in sending a stream of packets to the server with spoofed IP addresses, so that the internet routing infrastructure cannot distinguish the spoofed packets. Based on the odds ratio (OR) statistical measurement, in this work we propose a new detection method for the DDoS flooding attacks. By exploring the odds ratio to determine the risk factor of any incoming traffic to the server, the legitimate and attack traffic packets can be easily differentiated. Experimental results demonstrate the efficiency of the presented detection method in terms of its detection probability and detection time.

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