Preventing DDoS using Bloom Filter: A Survey

Patgiri, Ripon; Nayak, Sabuzima; Borgohain, Samir Kumar

doi:10.4108/eai.19-6-2018.155865

Cited by 23 publications

(18 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They extend the approach in Reference to distribute the Bloom filters close to the routers. For a recent survey on the usage of of Bloom Filter for DDoS mitigation we refer the reader to Reference . To put it in a nutshell, although there is a stream of history‐based approaches in the literature such as References , , and , those approaches rely merely on distance between IP addresses ignoring actual geographical proximity.…”

Section: Related Workmentioning

confidence: 99%

Mitigating DDoS using weight‐based geographical clustering

Kongshavn¹,

Haugerud

Yazidi

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

Distributed denial of service (DDoS) attacks have for the last two decades been among the greatest threats facing the internet infrastructure. Mitigating DDoS attacks is a particularly challenging task as an attacker tries to conceal a huge amount of traffic inside a legitimate traffic flow. This article proposes to use data mining approaches to find unique hidden data structures which are able to characterize the normal traffic flow. This will serve as a mean for filtering illegitimate traffic under DDoS attacks. In this endeavor, we devise three algorithms built on previously uncharted areas within mitigation techniques where clustering techniques are used to create geographical clusters in regions which are likely to contain legitimate traffic. We argue through extensive experimental results that establishing clusters around this narrative is a superior solution to clustering algorithms which rely on bitwise distances between IP addresses. In addition, the DDoS filtering algorithm is deployed in a virtual Linux environment using Nfqueue and tested in a simulated real-life DDoS attack.

show abstract

Section: Related Workmentioning

confidence: 99%

Mitigating DDoS using weight‐based geographical clustering

Kongshavn¹,

Haugerud

Yazidi

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“….. Article [4] defines false positive, true positive, false negative, and true negative as follows-• False positive: If K j ∈ S and Bloom Filter returns K j ∈ B, then the result of Bloom Filter is a false positive.…”

Section: Bloom Filtermentioning

confidence: 99%

“…If a system can tolerate negligible overhead, then Bloom Filter can enhance a performance of a system. Therefore, Bloom Filter is deployed to various domains, namely, Big Data [9], Deduplication [10], [11], [12], Network Security [4], [13], Network Traffic control [14], Name Lookup [15], [16], IP address lookup [17], [18], Biometric [19], [20], Bioinformatics [21], [22], File System [23], Indexing, and many more. However, Bloom Filter is not suitable in case of correct query-answer requirements.…”

Section: Bloom Filtermentioning

confidence: 99%

Role of Bloom Filter in Big Data Research: A Survey

Patgiri¹,

Nayak²,

Borgohain³

2018

ijacsa

Self Cite

View full text Add to dashboard Cite

Big Data is the most popular emerging trends that becomes a blessing for human kinds and it is the necessity of day-to-day life. For example, Facebook. Every person involves with producing data either directly or indirectly. Thus, Big Data is a high volume of data with exponential growth rate that consists of a variety of data. Big Data touches all fields, including Government sector, IT industry, Business, Economy, Engineering, Bioinformatics, and other basic sciences. Thus, Big Data forms a data silo. Most of the data are duplicates and unstructured.To deal with such kind of data silo, Bloom Filter is a precious resource to filter out the duplicate data. Also, Bloom Filter is inevitable in a Big Data storage system to optimize the memory consumption. Undoubtedly, Bloom Filter uses a tiny amount of memory space to filter a very large data size and it stores information of a large set of data. However, functionality of the Bloom Filter is limited to membership filter, but it can be adapted in various applications. Besides, the Bloom Filter is deployed in diverse field, and also used in the interdisciplinary research area. Bioinformatics, for instance. In this article, we expose the usefulness of Bloom Filter in Big Data research.

show abstract

“…To solve this issue, we identify two approaches that provide quick reaction time through the efficient and scalable use of the limited size of flow tables: Bloom Filters (BFs) and Wildcard Rules. Applying BFs for attack detection seems to be a promising solution since it has a good space and time complexity . BF is a probabilistic data structure used for membership query, that is, to check the presence of an element in a set by returning true or false.…”

Section: Introductionmentioning

confidence: 99%

A real‐time attack defense framework for 5G network slicing

et al. 2020

View full text Add to dashboard Cite

Network Slicing (NS) is a key enabler to support 5G network services on-demand. However, since NS is a result of the recent advancement in Software-Defined Networking and Network Function Virtualization, it introduces new security issues which include attacks against an NS instance within an operator network and interslice security threats. In this scenario, identifying and mitigating attacks in real-time is of paramount importance to improve security aspects. However, it is far from being straightforward. Therefore, this work proposes the FrameRTP4, a P4-based framework that aims to deliver real-time attack detection and mitigation mechanisms in 5G NS scenarios. For this, it provides a P4-based switch that implements an Service Function Chaining protocol layer, an efficient and scalable Access Control List for the detection and mitigation of known attacks, and a monitoring system aiming to reduce the overhead induced on the control channel. Furthermore, it delivers an orchestrator that aims to control all switches in order to enable lifecycle management of NS instances and P4 table rules. Besides, it also performs some autonomous tasks such as the wildcard rules generation and the detection of new threats by using machine learning algorithms. Preliminary results point to the potential benefits of FrameRTP4 to be part of a 5G NS infrastructure. K E Y W O R D S5G, bloom filter, cybersecurity, network function virtualization, network slice, P4

show abstract

Preventing DDoS using Bloom Filter: A Survey

Cited by 23 publications

References 47 publications

Mitigating DDoS using weight‐based geographical clustering

Mitigating DDoS using weight‐based geographical clustering

Role of Bloom Filter in Big Data Research: A Survey

A real‐time attack defense framework for 5G network slicing

Contact Info

Product

Resources

About