Adaptive Bloom filter

A Bloom filter is a simple randomized data structure that answers membership query with no false negative and a small false positive probability. It is an elegant data compression technique for membership information, and has broad applications. In this paper, we generalize the traditional Bloom filter to Adaptive Bloom Filter, which incorporates the information on the query frequencies and the membership likelihood of the elements into its optimal design. It has been widely observed that in many applications, some popular elements are queried much more often than the others. The traditional Bloom filter for data sets with irregular query patterns and non-uniform membership likelihood can be further optimized. We derive the optimal configuration of the Bloom filter with query-frequency and membership-likelihood information, and show that the adapted Bloom filter always outperforms the traditional Bloom filter. Under reasonable frequency models such as the step distribution or the Zipf's distribution, the improvement of the false positive probability of the adaptive Bloom filter over that of the traditional Bloom filter is usually of orders of magnitude

Preventing DDoS using Bloom Filter: A Survey

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, 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 a 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.Comment: 9 pages, 1 figure. This article is accepted for publication in EAI Endorsed Transactions on Scalable Information System

Classifying DDoS packets in high-speed networks

Recently high-speed networks have been utilized by attackers as Distributed Denial of Service (DDoS) attack infrastructure. Services on high-speed networks also have been attacked by successive waves of the DDoS attacks. How to sensitively and accurately detect the attack traffic, and quickly filter out the attack packets are still the major challenges in DDoS defense. Unfortunately most current defense approaches can not efficiently fulfill these tasks. Our approach is to find the network anomalies by using neural network and classify DDoS packets by a Bloom filter-based classifier (BFC). BFC is a set of spaceefficient data structures and algorithms for packet classification. The evaluation results show that the simple complexity, high classification speed and accuracy and low storage requirements of this classifier make it not only suitable for DDoS filtering in high-speed networks, but also suitable for other applications such as string matching for intrusion detection systems and IP lookup for programmable routers.<br /

Asteroid lightcurves from the Palomar Transient Factory survey: Rotation periods and phase functions from sparse photometry

We fit 54,296 sparsely-sampled asteroid lightcurves in the Palomar Transient Factory to a combined rotation plus phase-function model. Each lightcurve consists of 20+ observations acquired in a single opposition. Using 805 asteroids in our sample that have reference periods in the literature, we find the reliability of our fitted periods is a complicated function of the period, amplitude, apparent magnitude and other attributes. Using the 805-asteroid ground-truth sample, we train an automated classifier to estimate (along with manual inspection) the validity of the remaining 53,000 fitted periods. By this method we find 9,033 of our lightcurves (of 8,300 unique asteroids) have reliable periods. Subsequent consideration of asteroids with multiple lightcurve fits indicate 4% contamination in these reliable periods. For 3,902 lightcurves with sufficient phase-angle coverage and either a reliably-fit period or low amplitude, we examine the distribution of several phase-function parameters, none of which are bimodal though all correlate with the bond albedo and with visible-band colors. Comparing the theoretical maximal spin rate of a fluid body with our amplitude versus spin-rate distribution suggests that, if held together only by self-gravity, most asteroids are in general less dense than 2 g/cm$^3$, while C types have a lower limit of between 1 and 2 g/cm$^3$, in agreement with previous density estimates. For 5-20km diameters, S types rotate faster and have lower amplitudes than C types. If both populations share the same angular momentum, this may indicate the two types' differing ability to deform under rotational stress. Lastly, we compare our absolute magnitudes and apparent-magnitude residuals to those of the Minor Planet Center's nominal $G=0.15$, rotation-neglecting model; our phase-function plus Fourier-series fitting reduces asteroid photometric RMS scatter by a factor of 3.Comment: 35 pages, 29 figures. Accepted 15-Apr-2015 to The Astronomical Journal (AJ). Supplementary material including ASCII data tables will be available through the publishing journal's websit