Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.

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.

Bloom Filter is a bit array (a one-dimensional storage structure) that provides a compact representation for a set of data, which can be used to answer the membership query in an efficient manner with a small number of false positives. It has a lot of applications in many areas. In this paper, we extend the design of Bloom Filter by using a multi-dimensional matrix to replace the one-dimensional structure with three different implementations, namely OFFF, WOFF, FFF. We refer the extended Bloom Filter as Feng Filter. We show the false positive rates of our method. We compare the false positive rate of OFFF with that of the traditional one-dimensional Bloom Filter and show that under certain condition, OFFF has a lower false positive rate. Traditional Bloom Filter can be regarded as a special case of our Feng Filter.

In the 5G era, centralized mobility management raises the issue of traffic concentration on the mobility anchor. Distributed mobility management is expected to be a solution for this issue, as it moves mobility anchor functions to multiple edge routers. However, it incurs path stretch and redundant traffic on the backhaul links. Although these issues were not considered important in the 3G/4G era, they are expected to be a serious problem in the 5G era. In this paper, we design a routing-based mobility management mechanism to address the above problems. The mechanism integrates distributed routing with Bloom Filters and an anchor-less scheme where edge routers work as mobility anchors. Simulations show that the proposed mechanism achieves a good balance between redundant traffic on the backhaul links and routing overhead.

Current signature-based antivirus solutions have three limitations such as the large volume of signature database, privacy preservation, and computation overheads of signature matching. In this paper, we propose LigeroAV, a light-weight, performance-enhanced antivirus, suitable for pervasive environments such as mobile phones. LigeroAV focuses on detecting MD5 signatures which are more than 90% of signatures. LigeroAV offloads matching computation in the cloud server with up-to-dated signature database while preserving privacy level using the Bloom filter.

We design a new hash table for high-speed networking that reduces main memory accesses even when the ratio of inserted items to the table size is high, at which point previous schemes no longer work. This improvement comes from a new design of a summary, called expanded keys, exploiting recent multiple hash functions and Bloom filter theories.

Internet of Things (IoT) devices, which have different characteristics in mobility and communication patterns from traditional mobile devices such as cellular phones, have come into existence as a new type of mobile devices. A strict mobility management scheme for providing highly mobile devices with seamless access is over-engineered for IoT devices' mobility management. We revisit current mobility management schemes for wireless mobile networks based on identifier/locator separation. In this paper, we focus on IoT communication patterns, and propose a new routing-based mobility scheme for them. Our scheme adopts routing information aggregation scheme using the Bloom Filter as a data structure to store routing information. We clarify the effectiveness of our scheme in IoT environments with a large number of IoT devices, and discuss its deployment issues.

To eliminate CAMs from the load/store queues, several techniques to detect memory access order violation with hash filters composed of RAMs have been proposed. This paper proposes a technique with parallel counting Bloom filters (PCBF). A Bloom filter has extremely low false positive rates owing to multiple hash functions. Although some existing researches claim the use of Bloom filters, none of them make mention to multiple hash functions. This paper also addresses the problem relevant to the variety of access sizes of load/store instructions. The evaluation results show that our technique, with only 2720-bit Bloom filters, achieves a relative IPC of 99.0% while the area and power consumption are greatly reduced to 14.3% and 22.0% compared to a conventional model with CAMs. The filter is much smaller than usual branch predictors.

Content-centric networking (CCN) is an innovative network architecture that is being considered as a successor to the Internet. In recent years, CCN has received increasing attention from all over the world because its novel technologies (e.g., caching, multicast, aggregating requests) and communication based on names that act as addresses for content have the potential to resolve various problems facing the Internet. To implement these technologies, however, requires routers with performance far superior to that offered by today's Internet routers. Although many researchers have proposed various router components, such as caching and name lookup mechanisms, there are few router-level designs incorporating all the necessary components. The design and evaluation of a complete router is the primary contribution of this paper. We provide a concrete hardware design for a router model that uses three basic tables — forwarding information base (FIB), pending interest table (PIT), and content store (CS) — and incorporates two entities that we propose. One of these entities is the name lookup entity, which looks up a name address within a few cycles from content-addressable memory by use of a Bloom filter; the other is the interest count entity, which counts interest packets that require certain content and selects content worth caching. Our contributions are (1) presenting a proper algorithm for looking up and matching name addresses in CCN communication, (2) proposing a method to process CCN packets in a way that achieves high throughput and very low latency, and (3) demonstrating feasible performance and cost on the basis of a concrete hardware design using distributed content-addressable memory.

With the advent of high speed links, online flow measurement for, e.g., flow round trip time (RTT), has become difficult due to the enormous demands placed on computational resources. Most existing measurement methods are designed to count the numbers of flows or sizes of flows, but we address the flow RTT measurement, which is an important QoS metric for network management and cannot be measured with existing measurement methods. We first adapt a standard Bloom Filter (BF) for the flow RTT distribution estimation. However, due to the existence of multipath routing and Syn flooding attacks, the standard BF does not perform well. We further design the double-deletion bloom filter (DDBF) scheme, which alleviates potential hash collisions of the standard BF by explicitly deleting used records and implicitly deleting out-of-date records. Because of these double deletion operations, the DDBF accurately estimates the RTT distribution of TCP flows with limited memory space, even with the appearance of multipath routing and Syn flooding attacks. Theoretical analysis indicates that the DDBF scheme achieves a higher accuracy with a constant and smaller amount of memory compared with the standard BF. In addition, we validate our scheme using real traces and demonstrate significant memory-savings without degrading accuracy.

We propose a new Bloom filter that efficiently filters out non-members. With extra bits assigned and asymmetrically distributed, the new filter reduces hash computations and memory accesses. For an error rate of 10-6, the new filter reduces cost by 31.31% with 4.33% additional space, while the standard method saves offers a 20.42% reduction.

This paper proposes a method for reducing redundant greedy-perimeter transitions in beacon-less geographic routing for wireless sensor networks (WSNs). Our method can be added to existing routing methods. Using a bloom filter, each node can detect a routing loop, and then the node stores the information as “failure history”. In the next forwarding the node can avoid such bad neighbors based on the failure history. Simulation results demonstrate the benefit of our method.

In this paper, we study the problem of efficient processing of conjunctive queries in Peer-to-Peer systems based on Distributed Hash Tables (P2P DHT, for short). The basic idea of our approach is to cache the search result for the queries submitted in the past, and to use them to improve the performance of succeeding query processing. More concretely, we propose to adopt Bloom filters as a concrete implementation of such a result cache rather than a list of items used in many conventional schemes. By taking such an approach, the cache size for each conjunctive query becomes as small as the size of each file index. The performance of the proposed scheme is evaluated by simulation. The result of simulation indicates that the proposed scheme is particularly effective when the size of available memory in each peer is bounded by a small value, and when the number of peers is 100, it reduces the amount of data transmissions of previous schemes by 75%.

Boolean matching is a fundamental problem in FPGA synthesis, but existing Boolean matchers are not scalable to complex PLBs (programmable logic blocks) and large circuits. This paper proposes a filter-based Boolean matching method, F-BM, which accelerates Boolean matching using lookup tables implemented by Bloom filters storing pre-calculated matching results. To show the effectiveness of the proposed F-BM, a post-mapping re-synthesis minimizing area which employs Boolean matching as the kernel has been implemented. Tested on a broad selection of benchmarks, the re-synthesizer using F-BM is 80X faster with 0.5% more area, compared with the one using a SAT-based Boolean matcher.

Bloom filters are widely used for various network applications. Because of the limited size of on-chip memory and the large volume of network traffic, Bloom filters are often required to update their contents incrementally. Two techniques have been used for this purpose: cold cache and double buffering. Cold cache outperforms double buffering in terms of the average cache ratio. However, double buffering works much better than cold cache for the worst-case cache hit ratio. In this paper, we propose a new updating scheme for Bloom filters, which updates the contents of Bloom filter incrementally while the assigned memory space is fully utilized. The proposed scheme works better than cold cache in terms of the average cache hit ratio. At the same time, it outperforms double buffering for the worst-case cache hit ratio.

Using Bloom filters is one of the most popular and efficient lookup methods in P2P networks. A Bloom filter is a representation of data item indices, which achieves small memory requirement by allowing one-sided errors (false positive). In the lookup scheme besed on the Bloom filter, each peer disseminates a Bloom filter representing indices of the data items it owns in advance. Using the information of disseminated Bloom filters as a clue, each query can find a short path to its destination. In this paper, we propose an efficient extension of the Bloom filter, called a Deterministic Decay Bloom Filter (DDBF) and an index dissemination method based on it. While the index dissemination based on a standard Bloom filter suffers performance degradation by containing information of too many data items when its dissemination radius is large, the DDBF can circumvent such degradation by limiting information according to the distance between the filter holder and the items holders, i.e., a DDBF contains less information for faraway items and more information for nearby items. Interestingly, the construction of DDBFs requires no extra cost above that of standard filters. We also show by simulation that our method can achieve better lookup performance than existing ones.

The Bloom Filter (BF), a space-and-time-efficient hash-coding method, is used as one of the fundamental modules in several network processing algorithms and applications such as route lookups, cache hits, packet classification, per-flow state management or network monitoring. BF is a simple space-efficient randomized data structure used to represent a data set in order to support membership queries. However, BF generates false positives, and cannot count the number of distinct elements. A counting Bloom Filter (CBF) can count the number of distinct elements, but CBF needs more space than BF. We propose an alternative data structure of CBF, and we called this structure an Adaptive Bloom Filter (ABF). Although ABF uses the same-sized bit-vector used in BF, the number of hash functions employed by ABF is dynamically changed to record the number of appearances of a each key element. Considering the hash collisions, the multiplicity of a each key element on ABF can be estimated from the number of hash functions used to decode the membership of the each key element. Although ABF can realize the same functionality as CBF, ABF requires the same memory size as BF. We describe the construction of ABF and IABF (Improved ABF), and provide a mathematical analysis and simulation using Zipf's distribution. Finally, we show that ABF can be used for an unpredictable data set such as real network traffic.

Many P2P lookup services based on distributed hash tables (DHT) have appeared recently. These schemes are built upon overlay networks and ignore distance to the target resources. As a result, P2P lookups often suffer from unnecessarily long routes in the underlay network, which we call overlay dilation. This paper proposes a new scheme for resource routing, called hybrid hierarchical overlay routing, dubbed Hyho. We introduce distance-weighted Bloom filters (dwBFs) as a concise representation of routing information for scattered resources in overlay networks. To further reduce the size of Bloom filters, so that they are linear in the number of distinct resources, Hyho splits overlay networks in accordance with DHT, where each subnetwork has a smaller set of resources and spans the entire network thinly. As a result, Hyho constructs a hierarchical overlay network and routes requests accordingly. Simulation results show that Hyho can reduce overlay dilation to one half that yielded by the Chord lookup service.