Adapting the Bloom filter to multithreaded environments

Voras, Ivan; Žagar, Mario

doi:10.1109/melcon.2010.5476244

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…On the one hand, we could design more flexible algorithms to build binary fuse filters: e.g., so that they become bulk updatable. On the other hand, we could seek to better document applications where immutable probabilistic filters are best suited: e.g., when the filter is sent over a network, bundled with a front-end or used in a multi-threaded environment where locking is undesirable [18,24].…”

mentioning

confidence: 99%

Binary Fuse Filters: Fast and Smaller Than Xor Filters

Graf

Lemire

2022

ACM J. Exp. Algorithmics

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Bloom and cuckoo filters provide fast approximate set membership while using little memory. Engineers use them to avoid expensive disk and network accesses. The recently introduced xor filters can be faster and smaller than Bloom and cuckoo filters. The xor filters are within 23% of the theoretical lower bound in storage as opposed to 44% for Bloom filters. Inspired by Dietzfelbinger and Walzer, we build probabilistic filters—called binary fuse filters —that are within 13% of the storage lower bound—without sacrificing query speed. As an additional benefit, the construction of the new binary fuse filters can be more than twice as fast as the construction of xor filters. By slightly sacrificing query speed, we further reduce storage to within 8% of the lower bound. We compare the performance against a wide range of competitive alternatives such as Bloom filters, blocked Bloom filters, vector quotient filters, cuckoo filters, and the recent ribbon filters. Our experiments suggest that binary fuse filters are superior to xor filters.

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mentioning

confidence: 99%

Binary Fuse Filters: Fast and Smaller Than Xor Filters

Graf

Lemire

2022

ACM J. Exp. Algorithmics

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“…The expected space overhead for optimal Bloom filters is 44%: it requires setting k = − log ϵ where ϵ is the desired bound on the false-positive probability. Bloom filters can be made concurrent [39].…”

Section: Bloom Filter Variantsmentioning

confidence: 99%

Xor Filters

Graf

Lemire

2020

ACM J. Exp. Algorithmics

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The Bloom filter provides fast approximate set membership while using little memory. Engineers often use these filters to avoid slow operations such as disk or network accesses. As an alternative, a cuckoo filter may need less space than a Bloom filter and it is faster. Chazelle et al. proposed a generalization of the Bloom filter called the Bloomier filter. Dietzfelbinger and Pagh described a variation on the Bloomier filter that can answer approximate membership queries over immutable sets. It has never been tested empirically, to our knowledge. We review an efficient implementation of their approach, which we call the xor filter. We find that xor filters can be faster than Bloom and cuckoo filters while using less memory. We further show that a more compact version of xor filters (xor+) can use even less space than highly compact alternatives (e.g., Golomb-compressed sequences) while providing speeds competitive with Bloom filters.

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“…To reduce the calculation time and the space of memory to detect the flooding DDoS attack, fthe detection methods using the bloom filter [21], [22] have been investigated [23]- [25]. The bloom filter was first designed by Burton Howard Bloom in 1970 [21] and has since then become a standard and widely used algorithm for a quick set with large sets and/or low memory conditions [22]. Since the bloom filter can reduce disk access frequency, it has been successfully used in various network-related tasks.…”

Section: Mitigation Approachesmentioning

confidence: 99%

“…Moreover, the large memory space for analyzing attack features is needed to execute processes. In order to defect the flooding DDoS attack at once with lightweight space for process, a bloom filter [21], [22] is utilized for the detection of the flooding DDoS attack [23]- [25]. The flooding DDoS attack is detected by recording the IP address into the bloom filter.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Detection Using Bloom Filter against Flooding DDoS Attack

Choi

Sasase³

2020

IEICE Trans. Inf. & Syst.

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The flooding DDoS attack is a serious problem these days. In order to detect the flooding DDoS attack, the survival approaches and the mitigation approaches have been investigated. Since the survival approach occurs the burden on the victims, the mitigation approach is mainly studied. As for the mitigation approaches, to detect the flooding DDoS attack, the conventional schemes using the bloom filter, machine learning, and pattern analyzation have been investigated. However, those schemes are not effective to ensure the high accuracy (ACC), the high true positive rate (TPR), and the low false positive rate (FPR). In addition, the data size and calculation time are high. Moreover, the performance is not effective from the fluctuant attack packet per second (pps). In order to effectively detect the flooding DDoS attack, we propose the lightweight detection using bloom filter against flooding DDoS attack. To detect the flooding DDoS attack and ensure the high accuracy, the high true positive rate, and the low false positive rate, the dec-all (decrement-all) operation and the checkpoint are flexibly changed from the fluctuant pps in the bloom filter. Since we only consider the IP address, all kinds of flooding attacks can be detected without the blacklist and whitelist. Moreover, there is no complexity to recognize the attack. By the computer simulation with the datasets, we show our scheme achieves an accuracy of 97.5%. True positive rate and false positive rate show 97.8% and 6.3%, respectively. The data size for processing is much small as 280bytes. Furthermore, our scheme can detect the flooding DDoS attack at once in 11.1sec calculation time.

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Adapting the Bloom filter to multithreaded environments

Cited by 4 publications

References 14 publications

Binary Fuse Filters: Fast and Smaller Than Xor Filters

Binary Fuse Filters: Fast and Smaller Than Xor Filters

Xor Filters

A Lightweight Detection Using Bloom Filter against Flooding DDoS Attack

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