Probabilistic data structures are so popular in membership queries, network applications, and databases and so on. Bloom Filter and Cuckoo Filter are two popular space efficient models that incorporate in set membership checking part of many important protocols. They are compact representations of data that use hash functions to randomize a set of items. Being able to store more elements while keeping a reasonable false positive probability is a key factor of design. A new algorithm is proposed to improve some of the performance properties of Cuckoo Filter such as false positive rate and insertion performance and solve some drawbacks of the Cuckoo algorithm such as endless loop. Main characteristic of the Bloom Filter is used to improve Cuckoo Filter, so we have a smart Cuckoo Filter which is modified by Bloom Filter so called as SCFMBF. SCFMBF uses the same table of buckets as Cuckoo Filter but instead of storing constant Fingerprints, it stores Bloom Filters. Bloom Filters can be accumulated in the table’ s buckets which leads to higher insertion feasibility. We also address the endless loop problem of Cuckoo Filter that means an inserted item is stuck in an iterative process of finding an empty bucket, so a smart algorithm is designed which not only solves endless loop problems but also prevents insertion failure. Hence our smart algorithm prevents double checking of a bucket and avoids making loops. Consequently, the capacity of SCFMBF is improved significantly. Results of comparison with Cuckoo Filter shows that false positive probability of SCFMBF method is enhanced compared to Cuckoo Filter.

Nowadays, routers are the main backbone of computer networks specifically the Internet. Moreover, the need for high-performance and high-speed routers has become a fundamental issue due to significant growth of information exchange through the Internet and intranets. On the other hand, flexibility and configurability behind the open-source routers has extended their usage via the networks. Furthermore, after assigning the last remaining IPv4 address block in 2011, development and improvement of IPv6-enabled routers especially the open-sources has become one of the first priorities for network programmers and researchers. In IPv6 because of its 128-bits address space compared to 32-bits in IPv4, much more space and time are required to be stored and searched that might cause a speed bottleneck in lookup of routing tables. Therefore, in this paper, Bird as an example of existing open source router which supports both IPv4 and IPv6 addresses is selected and Bloom-Bird (our improved version of Bird) is proposed which uses an extra stage for its IP lookups using Bloom filter to accelerate IP lookup mechanism. Based on the best of our knowledge this is the first application of Bloom filter on Bird software router. Moreover, false positive errors are handled in an acceptable rate because Bloom-Bird scales its Bloom filter capacity. The Bloom-Bird using real-world IP prefixes and huge number of inserted prefixes into its internal FIB (Forwarding Information Base), shows up to 61% and 56% speedup for IPv4 and IPv6 lookups over standard Bird, respectively. Moreover, using manually generated prefix sets in the best case, up to 93% speedup is gained.

In the light of increased network attacks, payload attribution is an essential part of any forensics analysis of the attack. Usually attribution has to be done based on the payload of the packets. In such techniques network traffic should be stored in its entirety while user privacy is preserved. Bloom filters have been an ideal tool for such requirements. Previous works in this area have tried to minimize the false positive error rate associated with the bloom filter while improving on the data reduction ratio but there has not been any notable research on practical implementations in computer networks. A payload attribution technique should provide a list of connections which are suspects of carrying a specific payload (i. e. malware signature). The problem arises with the fact that there are too many queries required, given the large number of connections and the number of bloom filters involved over long time periods, which results in a large aggregate error rate. In this work, we propose a technique with which a time-based hierarchical bloom filter configuration is proposed to tackle the noted problem. Our evaluation shows that with this proposed technique we are able to limit the false positive error rate of the system as compared to the previously proposed techniques. This leads to an overall error reduction in the payload attribution system. More specifically, the error rate compared to previous work drops from 5. 66% to 3. 98% which results in reducing the number of incorrectly identified flows by 8400.

In this paper, we focus on improving the performance of recommender systems. To do this, we propose a new algorithm named PBloofI which is a kind of hierarchical bloom filter. Actually, the Bloom filter is an array-based technique for showing the items’ features. Since the feature vectors of items are sparse, the Bloom filter reduces the space usage by using the hashing technique. And also, to reduce the time complexity we used the hierarchical version of bloom filter which is based on B+ tree of order d. Since Bloom filters can make a tradeoff between space and time, proposing a new hierarchical Bloom filter causes a remarkable reduction in space and time complexity of recommender systems. To increase the accuracy of the recommender systems we use Probabilistic version of hierarchical Bloom filter. By measuring the accuracy of the algorithm we show that the proposed algorithms not only decrease the time complexity but also have no significant effect on accuracy.

Occurances of cyanobacterial bloom typically appear in eutrophic lakes which either have been affected by anthropogenic nutrient loading or are naturally rich in nutrients. Microcystis blooms have been reported all over the world in freshwater ecosystems mainly due to increased population which results in increased the waste water production. The aim of this study was to recognize the basic reasons of Microcystis aeruginosa bloom in Aras reservoir. Water temperature, pH, DO, EC, TN, N-No2, N-No3, P-Po4, chl a and the number of Microcystis colonies were determined. The effects of these parameters on the microcystis increased growth was examined. Though we could not strictly relate a few other factors to this phenomenon, higher water temperature, pH and DO in the sampling site i.e.27oC, 9.16 and 17.2 mg/L respectively, surely favored theM.aeruginosa bloom. In brief, preventing or reducing the nutrients flow into the reservoir should be considered as a priority in order to reduce Microcystis scum forming colonies.