Bloom Filter

Aiming at the problem of low interconnection efficiency caused by the wide variety of data in SWIM (System-Wide Information Management) and the inconsistent data naming methods, this paper proposes a new TLC (Type-Length-Content) structure hybrid data naming scheme combined with Bloom filters. This solution can meet the uniqueness and durability requirements of SWIM data names, solve the “suffix loopholes” encountered in prefix-based route aggregation in hierarchical naming, and realize scalable and effective route state aggregation. Simulation verification results show that the hybrid naming scheme is better than prefix-based aggregation in the probability of route identification errors. In terms of search time, this scheme has increased by 17.8% and 18.2%, respectively, compared with the commonly used hierarchical and flat naming methods. Compared with the other two naming methods, scalability has increased by 19.1% and 18.4%, respectively.

Enormous web pages are visited each day over a network and malicious websites might infect user machines. To identify malicious web sites, the most reliable approach is honeypot, an execution-based method. The vast amount of http traffic makes sandboxing all the web pages impossible. It is not practical in the real environments as it consumes too much computational resource and time. Only 1.3% of websites are malicious. Crawlerbased approach might be not easy to find malicious websites effectively, as it has no clue if they are visited by users. Therefore, the proposed system examines only user web requests, not from crawler, in order to catch the real drive-by download web attacks. The challenge is that the web traffic is huge in real networks and an efficient filtering is desired to process large scale user requests efficiently. Based on our observation, the domains of drive-by download attacks often are unreliable and exhibit distinct attributes from the normal. To classify massive volume of web traffic in a real network, this study proposes a two-stage drive-by download attack detection mechanism: first identifying suspicious websites based on domain reputation and then sandboxing only the suspicious ones to reduce the detection time. Such detection not only reduces the required computation resources and time, but also remains the efficiency benefited from sandbox-based detection. As WHOIS database is not reliable for not every domain query can be resolved. Therefore, this study relies on queries from DNS server and proposes novel reputation attributes to distinguish the benign and the suspicious. The experimental results show that the proposed filtering yields the accuracy of 94% in simulated real network environment and efficiently saves more than 12 times of the computing time with the comparison of an improved sandboxing approach. Such twostage detection system implemented in a real network environment with 560 thousand URL requests per day demonstrates its practicality and efficiency under large scale web requests. During the deployment on the real network, unknown malicious websites are identified which are not listed in the public accessible blacklist websites.

This paper describes and analyzes the key issues underlying the design of the Internet Protocol version 6 (IPv6). The paper highlights gains made by the new design and analyzes possible weaknesses as well. This gives an overview of the features which will be available in IPv6, why they arc included, and how they wiII be implemented. ft discusses certain significant algorithms which wilI form part of the new design. Differences between IPv6 and the existing p;rotocol, IPv4, have been pointed out wherever a new feature has been introduced or an old one changed. The main focus of the paper is the provisions that the desr'gners of IPv6 sought to ma)<e, which were not covered by IPv4. For this reason, issues like security, autoconfiguration and real-time communication have been dealt with in detail Additionally, the address architecture of the new protocol has been explained, and changes in the formats and headers have also been drscussed. Data fieLds have been described, where a...

In Vehicular Ad hoc Networks (VANETs), broadcasted beacon messages are critical as most safety applications rely on them. However, the design of a secure broadcast authentication faces many challenges. Specifically, vehicles are vulnerable to active attacks and the revealing of location. In this paper, we propose a lightweight authentication scheme using Timed Efficient Stream Loss-Tolerant Authentication (TESLA) scheme and Bloom Filters that not only prevents active attacks but also adds a privacy-preserving feature to make the scheme have better performance. Simulation results indicate the scheme outperforms the existing schemes in terms of verification time and the level of anonymity.

Bloom filter is a space-efficient randomized data structure for group membership query. It is widely used in networking applications which involve the packet header/content inspection. To provide fast membership query operation, this data structure resides in the main memory in most of its applications. Each membership query consists hashing for a set of memory addresses and memory accesses at these locations. In this paper, we propose a new design of Bloom filter in which every two memory addresses are squeezed into one I/O block of the main memory. With the burst-type data I/O capability in the contemporary DRAM design, the total number of memory I/O's involved in the membership query is reduced by half. Therefore, the average query delay can be reduced significantly. The cost of using this new design is a negligible increment of false positive rate as shown by both analysis and simulation.

The continuous growth in the Internet's size, the amount of data traffic, and the complexity of processing this traffic gives rise to new challenges in building high-performance network devices. One of the most fundamental tasks performed by these devices is searching the network data for predefined keys. Address lookup, packet classification, and deep packet inspection are some of the operations which involve table lookups and searching. These operations are typically part of the packet forwarding mechanism, and can create a performance bottleneck. Therefore, fast and resource efficient algorithms are required. One of the most commonly used techniques for such searching operations is the Ternary Content Addressable Memory (TCAM). While TCAM can offer very fast search speeds, it is costly and consumes a large amount of power. Hence, designing cost-effective, power-efficient, and high-speed search techniques has received a great deal of attention in the research and industrial community. In this thesis, we propose a generic search technique based on Bloom filters. A Bloom filter is a randomized data structure used to represent a set of bit-strings compactly and support set membership queries. We demonstrate techniques to convert the search process into table lookups. The resulting table data structures are kept in the off-chip memory and their Bloom filter representations are kept in the on-chip memory. An item needs to be looked up in the off-chip table only when it is found in the on-chip Bloom filters. By filtering the off-chip memory accesses in this fashion, the search operations can be significantly accelerated. Our approach involves a unique combination of algorithmic and architectural techniques that outperform some of the current techniques in terms of cost-effectiveness, speed, and power-efficiency.

Modern Network Intrusion Detection Systems (NIDS) inspect the network packet payload to check if it conforms to the security policies of the given network. This process, of-ten referred to as deep packet inspection, involves detection of predefined signature strings or keywords starting at an arbitrary location in the payload. String matching is a computationally intensive task and can become a potential bottleneck without high-speed processing. Since the conventional software-implemented string matching algorithms have not kept pace with the increasing network speeds, special purpose hardware solutions have been introduced. In this paper we show how Bloom filters can be used effectively to perform string matching for thousands of strings at wire speed. We describe how Bloom filters can be implemented feasibly on commodity FPGA. Our analysis shows that this approach for string matching is more effective than the current FPGA-based solutions which use Deterministic or Non-deterministic Finite Automata (DFA or NFA). Fi-nally, we give the details of our implementation of string matching technique on Xilinx XCV 2000E FPGA.

While the problem of general packet classification has received a great deal of attention from researchers over the last ten years, there is still no really satisfactory solution. Ternary Content Addressable Memory (TCAM), although widely used in practice, is both expensive and consumes a lot of power. Algorithmic solutions, which rely on commodity memory chips, are relatively inexpensive and power-efficient, but have not been able to match the generality and performance of TCAMs. In this paper we propose a new approach to packet classification, which combines architectural and algorithmic techniques. Our starting point is the well-known crossproducting algorithm, which is fast but has significant memory overhead due to the extra rules needed to represent the crossproducts. We show how to modify the crossproduct method in a way that drastically reduces the memory required, without compromising on performance. We avoid unnecessary accesses to off-chip memory by filtering off-chip accesses using on-chip Bloom filters. For packets that match p rules in a rule set, our algorithm requires just 4 + p + ǫ independent memory accesses on average, to return all matching rules, where ǫ á 1 is a small constant that depends on the false positive rate of the Bloom filters. Each memory access is just 256 bits, making it practical to classify small packets at OC-192 link rates using two commodity SRAM chips. For rule set sizes ranging from a few hundred to several thousand filters, the average rule set expansion factor attributable to the algorithm is just 1.2. The memory consumption per rule is 36 bytes in the average case.

This paper presents a novel method for efficient image retrieval, based on a simple and effective hashing of CNN features and the use of an indexing structure based on Bloom filters. These filters are used as gatekeepers for the database of image features, allowing to avoid to perform a query if the query features are not stored in the database and speeding up the query process, without affecting retrieval performance. Thanks to the limited memory requirements the system is suitable for mobile applications and distributed databases, associating each filter to a distributed portion of the database. Experimental validation has been performed on three standard image retrieval datasets, outperforming state-of-the-art hashing methods in terms of precision, while the proposed indexing method obtains a 2× speedup.

In hardware transactional memory, signatures have been proposed to keep track of memory locations accessed in a transaction to help conflict detection. Generally, signatures are implemented as Bloom filters that suffer from aliasing, that is, they can give rise to false conflicts. Such conflicts are more likely as signature fills (saturation), and they can lead a parallel application to perform worse than its serial version. Irrevocability is analyzed to address the signature saturation problem. When a transaction reaches a saturation threshold, the transaction enters an irrevocable state that prevents it from being aborted. Hence, such a transaction keeps running while the others are either stalled or allowed to run concurrently. We propose an analytical model that shows this is a good solution to overcome a high contention scenario. In addition, experimental evaluation shows the benefits in performance and power consumption of the proposed irrevocability mechanisms. Different saturation metrics are considered and a fixed threshold is found that yields maximum performance for the benchmarks evaluated.

Signatures have been proposed in transactional memory systems to represent read and write sets and to decouple transaction conflict detection from private caches or to accelerate it. Generally, signatures are implemented as Bloom filters that allow unbounded read/write sets to be summarized in bounded space at the cost of false conflict detection. It is known that this behavior has great impact in parallel performance. In this work, a scalability study of state-of-the-art signature designs is presented, for different orthogonal transactional characteristics, including contention, length, concurrency and spatial locality. This study was accomplished using the Stanford EigenBench benchmark. This benchmark was modified to support spatial locality analysis using a Zipf address distribution. Experimental evaluation on a hardware transactional memory simulator shows the impact of those parameters in the behavior of state-of-the-art signatures.

Transactional Memory (TM) systems must track memory accesses made by concurrent transactions in order to detect conflicts. Many TM implementations use signatures for this purpose, which summarize reads and writes in fixed-size bit registers at the cost of false positives (detection of nonexisting conflicts). Signatures are commonly implemented as two separate same-sized Bloom filters, one for reads and other for writes. In contrast, transactions frequently exhibit read and write sets of uneven cardinality. This mismatch between data sets and filter storage introduces inefficiencies in the use of signatures that have some impact on performance. This paper presents different signature designs as alternatives to the common scheme to deal with the asymmetry in transactional data sets in an effective way. Basically, we analyze two classes of new signatures, called multiset and reconfigurable asymmetric signatures. The first class uses only one Bloom filter to track both read and write sets, while the second class uses Bloom filters of configurable size for reads and writes. The main focus of this paper is a thorough study of these alternative signature designs, including a statistical analysis of false positives and an experimental evaluation, providing performance results and hardware area, time and energy requirements.

Motivation Detection of maximal exact matches (MEMs) between two long sequences is a fundamental problem in pairwise reference-query genome comparisons. To efficiently compare larger and larger genomes, reducing the number of indexed k-mers as well as the number of query k-mers has been adopted as a mainstream approach which saves the computational resources by avoiding a significant number of unnecessary matches. Results Under this framework, we proposed a new method to detect all MEMs from a pair of genomes. The method first performs a fixed sampling of k-mers on the query sequence, and adds these selected k-mers to a Bloom filter. Then all the k-mers of the reference sequence are tested by the Bloom filter. If a k-mer passes the test, it is inserted into a hash table for indexing. Compared with the existing methods, much less number of query k-mers are generated and much less k-mers are inserted into the index to avoid unnecessary matches, leading to an efficient matching process...

Counting Bloom Filters (CBFs) are widely employed in many applications for fast membership queries. CBF works on dynamic sets rather than a static set via item insertions and deletions. CBF allows false positive, but not false negative. The Bh-Counting Bloom Filter (Bh-CBF) and Variable Increment Counting Bloom Filter (VI-CBF) are introduced to reduce the false positive probability, but they suffer from memory overhead and hardware complexity. In this paper, we proposed a multilevel optimization approach named as Veracious Bh-Counting Bloom Filter (VBh-CBF) and Veracious Variable increment Counting Bloom Filter (VVI-CBF) by partitioning the counter vector into multiple levels to reduce the False Positive Probability (FPP) and to limit the memory requirement. The experiment result shows that the false positive probability and total memory size are reduced by 65.4%, 67.74% and 20.26%, 41.29% respectively compared to basic Bh-CBF and VI-CBF.

Penggunaan teknologi sonic bloom untuk meningkatkan produktivitas beberapa varietas tanaman kedelai (Glycine max (L.) Merril) The use of sonic bloom technology to increase the productivity of several soybean plant varieties (Glycine Max (L.) Merril

The theoretical minimum storage needed to represent a set of size N drawn from a universe of size M is about N·(log[sub]2[/sub](M/N) + 1.4472) bits (assuming neither N nor M/N is very small). I review the technique of `quotienting' which is used to approach this minimum, and look at the actual memory costs achieved by practical designs. Instead of somehow implementing and exploiting 1.4472 bits of steering information, most practical schemes use two bits (or more). In the conclusion I mention a scheme to reduce the overhead cost from two bits to a single trit.

Wireless Sensor Networking continues to evolve as one of the most challenging research areas. Considering the insecure nature of these networks and the fact that sensor nodes are distributed in a hostile environment, having a wellimplemented security scheme is absolutely essential. Bearing in mind the important security services like authentication and access control, we have proposed a novel security framework for these networks. The new framework is based on Kerberos authentication and access control system. The Kerberos has been adopted for WSNs by utilizing Bloom Filter data structure and Elliptic Curve cryptography. In the proposed scheme, Bloom Filter data structure is used in a novel way; we have used this data structure to get rid of Public Key's certificates. By combining Bloom Filter data structure and Elliptic Curve cryptography, we achieved a very light robust security framework that offers Authentication, Access Control, and key sharing services. The analysis results showed that our scheme provides more security services and is more robust in the presence of attacks compared to the previous schemes. In contrast, simulation results indicated that our system had significant improvements over the other schemes in many aspects such as power and time expenditure.

Wireless Sensor Networking continues to evolve as one of the most challenging research areas. Considering the insecure nature of these networks and the fact that sensor nodes are distributed in a hostile environment, having a wellimplemented security scheme is absolutely essential. Bearing in mind the important security services like authentication and access control, we have proposed a novel security framework for these networks. The new framework is based on Kerberos authentication and access control system. The Kerberos has been adopted for WSNs by utilizing Bloom Filter data structure and Elliptic Curve cryptography. In the proposed scheme, Bloom Filter data structure is used in a novel way; we have used this data structure to get rid of Public Key's certificates. By combining Bloom Filter data structure and Elliptic Curve cryptography, we achieved a very light robust security framework that offers Authentication, Access Control, and key sharing services. The analysis results showed that our scheme provides more security services and is more robust in the presence of attacks compared to the previous schemes. In contrast, simulation results indicated that our system had significant improvements over the other schemes in many aspects such as power and time expenditure.

The goal of query optimization in query federation over linked data is to minimize the response time and the completion time. Communication time has the highest impact on them both. Static query optimization can end up with inefficient execution plans due to unpredictable data arrival rates and missing statistics. This study is an extension of adaptive join operator which always begins with symmetric hash join to minimize the response time, and can change the join method to bind join to minimize the completion time. The authors extend adaptive join operator with bind-bloom join to further reduce the communication time and, consequently, to minimize the completion time. They compare the new operator with symmetric hash join, bind join, bind-bloom join, and adaptive join operator with respect to the response time and the completion time. Performance evaluation shows that the extended operator provides optimal response time and further reduces the completion time. Moreover, it has the ...

Malicious URL detection is an emerging research area due to continuous modernization of various systems, for instance, Edge Computing. In this article, we present a novel malicious URL detection technique, called deepBF (deep learning and Bloom Filter). deepBF is presented in two-fold. Firstly, we propose a self-adjusted Bloom Filter using 2-dimensional Bloom Filter. We experimentally decide the best non-cryptography string hash function. Then, we derive a modified non-cryptography string hash function from the selected hash function for deepBF by introducing biases in the hashing method and compared among the string hash functions. The modified string hash function is compared to other variants of diverse non-cryptography string hash functions. It is also compared with various filters, particularly, counting Bloom Filter, Kirsch et al., and Cuckoo Filter using various test cases. The test cases unearth weakness and strength of the filters. Secondly, we propose a malicious URL detection mechanism using deepBF. We apply the evolutionary convolutional neural network to identify the malicious URLs. The evolutionary convolutional neural network is trained and tested with malicious URL datasets. The output is tested in deepBF for accuracy. We have achieved many conclusions from our experimental evaluation and results and are able to reach various conclusive decisions which are presented in the article.

Heterogeneous systems that integrate a multicore CPU and a GPU on the same die are ubiquitous. On these systems, both the CPU and GPU share the same physical memory as opposed to using separate memory dies. Although integration eliminates the need to copy data between the CPU and the GPU, arranging transparent memory sharing between the two devices can carry large overheads. Memory on CPU/GPU systems is typically managed by a software framework such as OpenCL or CUDA, which includes a runtime library, and communicates with a GPU driver. These frameworks offer a range of memory management methods that vary in ease of use, consistency guarantees and performance. In this study, we analyze some of the common memory management methods of the most widely used software frameworks for heterogeneous systems: CUDA, OpenCL 1.2, OpenCL 2.0, and HSA, on NVIDIA and AMD hardware. We focus on performance/functionality trade-offs, with the goal of exposing their performance impact and simplifying th...

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps toward elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depend on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely. With ABySS 1.0, we originally showed that assembling the human genome using short 50-bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its redesign, which departs from MPI and instead implements algorithms that employ a Bloom filter, a probabilistic data structure, to represent a de Bruijn graph and reduce memory requirements. We benchmarked ABySS 2.0 human genome assembly using a Genome in a Bottle data set of 250-bp Illumina paired-end and 6-kbp matepair libraries from a single individual. Our assembly yielded a NG50 (NGA50) scaffold contiguity of 3.5 (3.0) Mbp using <35 GB of RAM. This is a modest memory requirement by today's standards and is often available on a single computer. We also investigate the use of BioNano Genomics and 10x Genomics' Chromium data to further improve the scaffold NG50 (NGA50) of this assembly to 42 (15) Mbp.

The recent file storage applications built on top of peer-to-peer distributed hash tables lack search capabilities. We believe that search is an important part of any document publication system. To that end, we have designed and analyzed a distributed search engine based on a distributed hash table. Our simulation results predict that our search engine can answer an average query in under one second, using under one kilobyte of bandwidth.

Dragon Nest is one of Massively Multiplayer Online Role-playing Game (MMORPG online games. It has become the most popular online game played by people around the world. This work observes two examples of the MMORPG online games: the Dragon Nest INA and the Legend DN II. The purpose is to analyze the traffic data of the Dragon Nest to find and discern the patterns of behavior of the Dragon Nest INA and the Legend DN II using Deep Packet Inspection (DPI).  A dataset is constructed by capturing traffic data from the testbed environment. Then feature extraction, feature selection, and visualization are performed during the experiments. Experiment results shows the traffic data of the Dragon Nest INA is higher than the Legend DN II. It is because of the difference in the number of entries in the game. Then, the Bloom filter method is used as a tool to check the existence of a pattern of the Dragon Nest in the dataset. The false positive rate of matching is 0.399576%.

In log files, not all information/events are recorded and it is thus impossible to trace the paths of secret leaking based on log files alone. In this paper, we utilize user-relationshipgraphs, or social networks, to compensate for the required information. User-relationship-graphs are constructed from several flow-net data structures over a longer period so that we can avoid missing embedded threats such as hostile codes. We call this approach virtual flow-net.

We introduce the first software-based attestation approach with provable security properties, and argue for its importance as a component in a new Anti-Virus paradigm. Our new method is practical and efficient. It enables detection of any malware (that does not commit suicide to remain undetected)-even if the infection occurred before our security measure was loaded. Our new approach works independently of computing platform, and is eminently suited to address the threat of mobile malware, for which the current Anti-Virus paradigm is poorly suited. Our approach is based on memory-printing of client devices. Memory-printing is a novel and lightweight cryptographic construction whose core property is that it takes notably longer to compute a function if given less RAM than for which it was configured. This makes it impossible for a malware agent to remain active (e.g., in RAM) without being detected, when the function is configured to use all space that should be free after all active applications are swapped out. Our approach is based on inherent timing differences for random access of RAM, flash, and other storage; and the time to communicate with external devices.

In recent work, the authors introduced a data structure with the same functionality as a counting Bloom filter (CBF) based on fingerprints and the d-left hashing technique. This paper describes dynamic bit reassignment, an approach that allows the size of the fingerprint to flexibly change with the load in each hash bucket, thereby reducing the probability of a false positive. This technique allows us to not only improve our d-left counting Bloom filter, but also to construct a data structure with the same functionality as a Bloom filter, including the ability to handle insertions online, that yields fewer false positives for sufficiently large filters. Our results show that our d-left Bloom filter data structure begins achieving smaller false positive rates than the standard construction at 16 bits per element. We explain the technique, describe why it is amenable to hardware implementation, and provide experimental results.

The objective of this research increases efficiency of web cache memory. The Adaptive Web Caching with Interpolation and Web Usage Patterns (AWC-IWUP) simulation is investigated to solve for decision making about web objects replacement based on web usage and dynamic decision. More than 1,000,000 data sets of web usage between January 2013 and October 2013 from Walailak University are used for testing in this simulation. The AWC-IWUP simulation combines many mathematical statistics theories as follows: Expected value algorithm, Cubic spline technique, MarSpline technique, First Order Condition (FOC) and Bayesian algorithm. The experimental results of this research can conclude that the AWC-IWUP simulation is proposed replacement technique that the AWC-IWUP solves replacement problem of the web cache memory better than the LRU technique. The AWC-IWUP technique increases the hit ratios higher than the LRU technique approximately 25%.

With the emergence of IoT devices, data aggregation in the area of smart grids can be implemented based on IoT networks. However, the communication and computation resources of IoT devices are limited so it is not possible to apply conventional Internet protocols directly. On the other hand, gathering data from smart meters in the advanced metering infrastructure faces challenges such as privacy-preserving and heavy-loaded authentication and aggregation schemes. In this paper, we propose an improved lightweight, secure, and privacypreserving scheme for aggregating data of smart meters in largescale IoT-based smart grids. The proposed scheme adopts lightweight operations of cryptography such as exclusive-OR, hash, and concatenation functions. In comparison with the schemes in the literature, the analysis and simulation results show that the proposed scheme satisfies the same security levels, while at the same time burdens lower computation and communication overheads. This observation makes the proposed scheme more suitable to be employed in large-scale and IoT-based smart grids for data aggregation.

The retrieval problem is the problem of associating data with keys in a set. Formally, the data structure must store a function f : U → {0, 1} r that has specified values on the elements of a given set S ⊆ U , |S| = n, but may have any value on elements outside S. All known methods (e. g. those based on perfect hash functions), induce a space overhead of Θ(n) bits over the optimum, regardless of the evaluation time. We show that for any k, query time O(k) can be achieved using space that is within a factor 1 + e −k of optimal, asymptotically for large n. The time to construct the data structure is O(n), expected. If we allow logarithmic evaluation time, the additive overhead can be reduced to O(log log n) bits whp. A general reduction transfers the results on retrieval into analogous results on approximate membership, a problem traditionally addressed using Bloom filters. Thus we obtain space bounds arbitrarily close to the lower bound for this problem as well. The evaluation procedures of our data structures are extremely simple. For the results stated above we assume free access to fully random hash functions. This assumption can be justified using space o(n) to simulate full randomness on a RAM.

The assembly of DNA sequences de novo is fundamental to genomics research. It is the first of many steps towards elucidating and characterizing whole genomes. Downstream applications, including analysis of genomic variation between species, between or within individuals critically depends on robustly assembled sequences. In the span of a single decade, the sequence throughput of leading DNA sequencing instruments has increased drastically, and coupled with established and planned large-scale, personalized medicine initiatives to sequence genomes in the thousands and even millions, the development of efficient, scalable and accurate bioinformatics tools for producing high-quality reference draft genomes is timely.With ABySS 1.0, we originally showed that assembling the human genome using short 50 bp sequencing reads was possible by aggregating the half terabyte of compute memory needed over several computers using a standardized message-passing system (MPI). We present here its re-de...

Searchable encryption techniques are intelligent tools that attempt to solve the challenge of querying stored data on untrusted cloud servers while preserving data confidentiality. This paper proposes simple, powerful, and practical Searchable Symmetric Encryption (SSE) scheme which capable of executing SQL (Structural Query Language) queries over encrypted cloud databases. Our scheme improves the efficiency of the search over encrypted data by using the SCKHA (Symmetric Cipher based on Key Hashing Algorithm) algorithm instead of using the bloom filter technique which causes the problem of the false positives. We built a complicated index structure for the encrypted data by combining the encryption of the data unit using Advanced Encryption Standard (AES) scheme with the encryption of SCKHA scheme for the same data to ensure data security. We also proposed a complete framework for implementing our construction, validating its practicality to achieve exact and partial search over encrypted SQL databases. Furthermore, we performed experiments on adult data sets and analyzed the experimental results in terms of computation time for index generation, trapdoor generation and average search. Experiments showed that the taken time for generating the trapdoor and the keyword search in our scheme is faster than other schemes. We also analyzed the security of our scheme and evaluated its performance experimentally and theoretically. Finally, we provide features for evaluating single, conjunctive, and disjunctive queries.

As more critical and private information and assets moves to cloud computing environment (CCE), security risks, compliance and data integrity complications and legal issues arise. To maximize the value of cloud computing and meanwhile, to avoid the risks associated with their cloud based implementations. Enterprises need an approach, processes, procedures' and technology to manage and control thousands of critical data, services and process elements in the CCE. In other words, cloud computing needs a unified .yet independent model which is efficient .has high cloud computing value and governance layer that marks out a path that will guide the enterprise into the cloud in a controlled and secure manner.

Objectives: This study introduces MetaBIDx, a computational method designed to enhance species prediction in metagenomic environments. The method addresses the challenge of accurate species identification in complex microbiomes, which is due to the large number of generated reads and the ever-expanding number of bacterial genomes. Bacterial identification is essential for disease diagnosis and tracing outbreaks associated with microbial infections. Methods: MetaBIDx utilizes a modified Bloom filter for efficient indexing of reference genomes and incorporates a novel strategy for reducing false positives by clustering species based on their genomic coverages by identified reads. The approach was evaluated and compared with several well-established tools across various datasets. Precision, recall, and F1-score were used to quantify the accuracy of species prediction. Results: MetaBIDx demonstrated superior performance compared to other tools, especially in terms of precision and F1-score. The application of clustering based on approximate coverages significantly improved precision in species identification, effectively minimizing false positives. We further demonstrated that other methods can also benefit from our approach to removing false positives by clustering species based on approximate coverages. With a novel approach to reducing false positives and the effective use of a modified Bloom filter to index species, MetaBIDx represents an advancement in metagenomic analysis. The findings suggest that the proposed approach could also benefit other metagenomic tools, indicating its potential for broader application in

Jedna od najvecih sigurnosnih prijetnji na globalnoj mreži je nedostupnost resursa uzrokovana napadom uskracivanja usluge. Velik broj istraživanja se temelji na mogucnostima unaprijeđenja metoda otkrivanja i prevencije napada uskracivanja usluga na strani poslužitelja žrtve ili na strani izvora napada. Bloomov filter je podatkovno ucinkovita struktura koja se koristi u podrucju prepoznavanja uzoraka. Bloomov filter se koristi u mrežnim aplikacijama za detaljnu provjeru sadržaja zaglavlja paketa tražeci unaprijed ocekivanje nizove znakova s ciljem otkrivanja pripadnosti. U sustavima za otkrivanje kompromitiranih aktivnosti efikasnost algoritama za raspoznavanje uzoraka je izuzetno važna za ispravno otkrivanje pripadnih parova paketa. Složenost algoritma znacajno utjece na rezultate otkrivanja. U ovom radu izrađena je napredna metoda pronalaska pripadnih parova paketa primjenjiva za SACK2 algoritam za otkrivanje napada. Teorijskom analizom određena je vjerojatnost pojave greske u vidu...

Distributed Denial of Service (DDoS) attacks are a serious threat to Internet security. A lot of research effort focuses on having detection and prevention methods on the victim server side or source side. The Bloom filter is a space-efficient data structure used to support pattern matching problems. The filter is utilised in network applications for deep packet inspection of headers and contents and also looks for predefined strings to detect irregularities. In intrusion detection systems, the accuracy of pattern matching algorithms is crucial for dependable detection of matching pairs, and its complexity usually poses a critical performance bottleneck. In this paper, we will propose a novel Dual Counting Bloom Filter (DCBF) data structure to decrease false detection of matching packets applicable for the SACK 2 algorithm. A theoretical evaluation will determine the false rate probability of detection and requirements for increased memory. The proposed approach significantly reduces the false rate compared to previously published results. The results indicate that the increased complexity of the DCBF does not affect efficient implementation of hardware for embedded systems that are resource constrained. The experimental evaluation was performed using extensive simulations based on real Internet traces of a wide area network link, and it was subsequently proved that DCBF significantly reduces the false rate.

Many clients like to use the Web to discover product details in the form of online reviews. The reviews are provided by other clients and specialists. Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information facilities. Collaborative filtering methods are vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users. The collaborative filtering method has assumption that people having same tastes choose the same items. The conventional collaborative filtering system has drawbacks as sparse data problem & lack of scalability. A new recommender system is required to deal with the sparse data problem & produce high quality recommendations in large scale mobile environment. MapReduce is a programming model which is widely used for large-scale data analysis. The described algorithm of recommendation mechanism for mobile commerce is user based collaborative filtering using MapReduce which reduces scalability problem in conventional CF system. One of the essential operations for the data analysis is join operation. But MapReduce is not very competent to execute the join operation as it always uses all records in the datasets where only small fraction of datasets are applicable for the join operation. This problem can be reduced by applying bloomjoin algorithm. The bloom filters are constructed and used to filter out redundant intermediate records. The proposed algorithm using bloom filter will reduce the number of intermediate results and will improve the join performance.

Cloud computing is a one of the network-based computing that provides services to computer and other cloud devices. Secure cloud data storage is a significant when using the cloud services provided by the service provider in cloud environment. Many research works have been developed to provide secure wireless communication in cloud. However, integrity verification of stored data on cloud was not addressed in an efficient manner. In order to overcome above limitations, bloom hashing based Schnorr signature (BH-SS) technique is proposed. In BH-SS technique, at first, the cloud users send the request to cloud server and it response the user requested data. Next, bloom hash function is used to increase the integrity of cloud data storage. Bloom hash function is an efficient data structure used for storing the data and its hashed value. Bloom hash function stores the various cloud user data in it bits of array by generating hash value for each cloud data. Each data has the diverse hash value in bit array. This helps for BH-SS technique to improve the data integrity and dynamic data storage in cloud environment. Lastly, BH-SS technique employed Schnorr signature to ensure confidentiality of stored data with the aid of producing private signing key and public verification key. Therefore, proposed BH-SS technique improves the data integrity rate and reduces execution time. The BH-SS technique conducts experimental works on the parameters such as data integrity, space complexity, execution time and data confidentiality rate. The experimental results observe that the BH-SS technique is able to enhance the cloud data integrity and also reduces the execution time of cloud storage and space complexity when compared to state-of-the-art-works.

The problem of Network Traffic Classification (NTC) has attracted significant amount of interest in the research community, offering a wide range of solutions at various levels. The core challenge is in addressing high amounts of traffic diversity found in today's networks. The problem becomes more challenging if a quick detection is required as in the case of identifying malicious network behavior or new applications like peer-to-peer traffic that have potential to quickly throttle the network bandwidth or cause significant damage. Recently, Traffic Dispersion Graphs (TDGs) have been introduced as a viable candidate for NTC. The TDGs work by forming a network wide communication graphs that embed characteristic patterns of underlying network applications. However, these patterns need to be quickly evaluated for mounting real-time response against them. This paper addresses these concerns and presents a novel solution for real-time analysis of Traffic Dispersion Metrics (TDMs) in the TDGs. We evaluate the dispersion metrics of interest and present a dedicated solution on an FPGA for their analysis. We also present analytical measures and empirically evaluate operating effectiveness of our design. The mapped design on Virtex-5 device can process 7.4 million packets/second for a TDG comprising of 10k flows at very high accuracies of over 96%.

DESCRIPTION Information dissemination as the main objective of setting up the Internet has seen different folds to improve its course. Information centric networking (ICN) has been introduced with the aim of curtailing some future challenges posed at the traditional Internet in the nearest future. ICN advantage of caching chunks of information on path and off-path of the network stands the paradigm out as an alternative shift from host centric network to name centric. ICN caching approach can thereby significantly reduce amount of times a host is visited. Bloom Filter with its advantage of fast searching and false positivity characteristics are seen as form of message retrieval practice to improve interest serving on the network. This paper analyzed the advantages of vending and adopting Bloom Filters and Muxing as research directions to minimize excessive bandwidth consumption, lesser delays, prompt delivery of information, higher throughput and the ability to share information fro...

Recent work on searchable symmetric encryption (SSE) has focused on increasing its expressiveness. A notable example is the OXT construction (Cash et al., CRYPTO '13) which is the first SSE scheme to support conjunctive keyword queries with sub-linear search complexity. While OXT efficiently supports disjunctive and boolean queries that can be expressed in searchable normal form, it can only handle arbitrary disjunctive and boolean queries in linear time. This motivates the problem of designing expressive SSE schemes with worst-case sub-linear search; that is, schemes that remain highly efficient for any keyword query. In this work, we address this problem and propose non-interactive highly efficient SSE schemes that handle arbitrary disjunctive and boolean queries with worst-case sub-linear search and optimal communication complexity. Our main construction, called IEX, makes black-box use of an underlying single keyword SSE scheme which we can instantiate in various ways. Our first instantiation, IEX-2Lev, makes use of the recent 2Lev construction (Cash et al., NDSS '14) and is optimized for search at the expense of storage overhead. Our second instantiation, IEX-ZMF, relies on a new single keyword SSE scheme we introduce called ZMF and is optimized for storage overhead at the expense of efficiency (while still achieving asymptotically sub-linear search). Our ZMF construction is the first adaptively-secure highly compact SSE scheme and may be of independent interest. At a very high level, it can be viewed as an encrypted version of a new Bloom filter variant we refer to as a Matryoshka filter. In addition, we show how to extend IEX to be dynamic and forward-secure. To evaluate the practicality of our schemes, we designed and implemented a new encrypted search framework called Clusion. Our experimental results demonstrate the practicality of IEX and of its instantiations with respect to either search (for IEX-2Lev) and storage overhead (for IEX-ZMF).

The task of maintaining database Integrity is always complex and hectic because there's tremendous amount of data in the database, the business analysts or database administrators can also make errors that lead to integrity violations, so the tools or applications which are handling these things should be empowered with high efficiency techniques. If there weren't any database integrity detection applications then it would be difficult for a client to trust the 3rd party service providers. Many methodologies and tools exists which are maintaining the integrity with proper accuracy but most of them do not reveal any details about the cause also these methodologies suffer from time and space complexities, so to enhance the process of maintaining database integrity proposed methodology put forwards an idea of detecting tampering attack on databases using tiled bitmap technique which is powered with avalanche effect of the hash codes of data tuples in regular intervals. System not only detects all the details of forensics and also it clearly restores the original database. To enhance the process of maintaining database integrity proposed methodology put forwards an idea of detecting tampering attack on databases using tiled bitmap technique which is powered with avalanche effect of the hash codes of data tuples in regular intervals. System not only detects all the details of forensics and also it clearly restores the original database.

Many large storage systems use approximatemembership-query (AMQ) data structures to deal with the massive amounts of data that they process. An AMQ data structure is a dictionary that trades off space for a false positive rate on membership queries. It is designed to fit into small, fast storage, and it is used to avoid I/Os on slow storage. The Bloom filter is a well-known example of an AMQ data structure. Bloom filters, however, do not scale outside of main memory. This paper describes the Cascade Filter TM, an AMQ data structure that scales beyond main memory, supporting over half a million insertions/deletions per second and over 500 lookups per second on a commodity flashbased SSD. 1

Radio Frequency Identification (RFID) is a technology for automatic object identification that has been implemented in several real-life applications. In this work, we expand a novel relevant application of RFID tags for grocery stores, which aims to check the adequacy of food items with respect to the shoppers' personal preferences. Unlike similar works, we focus on shoppers' privacy and running time efficiency. For this aim, we propose a novel private set intersection (PSI) protocol to be used in matching the shoppers' personal preferences with the set of each item's adequate profiles that are held by the back-end server of the store. We provide a standard security proof against curious stores and malicious customers. For efficiency concern, we build our protocol without cryptographic operations, and we achieve a linear asymptotic complexity of O(v + c) for communications and store-side computations, where v and c are the numbers of profiles in the store's back-end server and the shopper's list of preferences respectively. Moreover, experimental results and comparisons with state-of-the art solutions reveal the scalability of our novel PSI protocol for big market stores.