Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

The Internet Protocol.

Two metaheuristic algorithms (WOA and MFO) are used.These algorithms are applied to multilevel thresholding image segmentation.MFO and WOA are better than compared algorithms.MFO is better than WOA for higher number of thresholds. Determining the optimal thresholding for image segmentation has got more attention in recent years since it has many applications. There are several methods used to find the optimal thresholding values such as Otsu and Kapur based methods. These methods are suitable for bi-level thresholding case and they can be easily extended to the multilevel case, however, the process of determining the optimal thresholds in the case of multilevel thresholding is time-consuming. To avoid this problem, this paper examines the ability of two nature inspired algorithms namely: Whale Optimization Algorithm (WOA) and Moth-Flame Optimization (MFO) to determine the optimal multilevel thresholding for image segmentation. The MFO algorithm is inspired from the natural behavior of moths which have a special navigation style at night since they fly using the moonlight, whereas, the WOA algorithm emulates the natural cooperative behaviors of whales. The candidate solutions in the adapted algorithms were created using the image histogram, and then they were updated based on the characteristics of each algorithm. The solutions are assessed using the Otsus fitness function during the optimization operation. The performance of the proposed algorithms has been evaluated using several of benchmark images and has been compared with five different swarm algorithms. The results have been analyzed based on the best fitness values, PSNR, and SSIM measures, as well as time complexity and the ANOVA test. The experimental results showed that the proposed methods outperformed the other swarm algorithms; in addition, the MFO showed better results than WOA, as well as provided a good balance between exploration and exploitation in all images at small and high threshold numbers.

Whale Optimization Algorithm and Moth-Flame Optimization for multilevel thresholding image segmentation

The book is meant to be useful for practitioners in applied data analysis in life sciences, engineering, physics and chemistry; it will also be valuable to PhD ...

Principal Manifolds for Data Visualization and Dimension Reduction

Certificateless cryptography involves a Key Generation Center (KGC) which issues a partial key to a user and the user also independently generates an additional public/secret key pair in such a way that the KGC who knows only the partial key but not the additional secret key is not able to do any cryptographic operation on behalf of the user; and a third party who replaces the public/secret key pair but does not know the partial key cannot do any cryptographic operation as the user either. We call this attack launched by the third party as the key replacement attack. In ACISP 2004, Yum and Lee proposed a generic construction of digital signature schemes under the framework of certificateless cryptography. In this paper, we show that their generic construction is insecure against key replacement attack. In particular, we show that the security requirements of their generic building blocks are insufficient to support some security claim stated in their paper. We then propose a modification of their scheme and show its security in a new and simplified security model. We show that our simplified definition and adversarial model not only capture all the distinct features of certificateless signature but are also more versatile when compared with all the comparable ones. We believe that the model itself is of independent interest.

Key replacement attack against a generic construction of certificateless signature

As the largest botnet on the Internet, the Storm Worm peer-to-peer botnet partly parasitizes on current Overnet peer-to-peer networks. Therefore, distinguishing Storm bots from regular peers is of great meaning. In this paper we propose a novel method for identifying Strom bots using an active crawler that can collect location information from all participants. Due to the flaws in the generation algorithm of the global identifier of the Storm bots, the location information of each Storm bot partly contributes to the aliasing phenomena. After analyzing the properties of aliasing phenomena, we deduce a Storm bot measuring criteria to identify bots. The results show that the size of Storm Worm botnet would estimate over 400,000 during the first two weeks of February 2008 with the counting of online bots between 16,500 and 23,000.

Actively Measuring Bots in Peer-to-Peer Networks

A new approach to data stream clustering with the help of an ensemble of adaptive neuro-fuzzy systems is proposed. The proposed ensemble is formed with adaptive neuro-fuzzy self-organizing Kohonen maps in a parallel processing mode. Their learning procedure is carried out with different parameters that define a nature of cluster borders' blurriness. Clusters' quality is estimated in an online mode with the help of a modified partition coefficient which is calculated in a recurrent form. A final result is chosen by the best neuro-fuzzy self-organizing Kohonen map.

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An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

The techniques of Dynamic Time Warping (DTW) have shown a great efficiency for clustering time series. On the other hand, it may lead to sufficiently high computational loads when it comes to processing long data sequences. For this reason, it may be appropriate to develop an iterative DTW procedure to be capable of shrinking time sequences. And later on, a clustering approach is proposed for the previously reduced data (by means of the iterative DTW). Experimental modeling tests were performed for proving its efficiency.

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Clustering Matrix Sequences Based on the Iterative Dynamic Time Deformation Procedure

An architecture and learning methods for deep neural networks that increase a number of layers and adjust their synaptic weights in an online mode are proposed in the article. The system's architecture is based on nodes of a special type (extended neo-fuzzy neurons) which possess enhanced approximating properties. A main feature of the proposed network is a learning process for each node that is performed sequentially in an online mode.

A deep cascade neural network based on extended neo-fuzzy neurons and its adaptive learning algorithm

Computer worms evolved continually, faster and smarter. Proactive P2P worms with new "gene" propagate over logical P2P overlay networks defined by peer relationship. Observations suggest that the node degrees of an unstructured P2P network are power law distributed thus we model it as a power law undirected graph. We study propagation process of proactive P2P worm using a dynamic epidemic model. Specifically, we adopt discrete-time to conduct recursive analysis and deterministic approximation to describe propagation of proactive P2P worm. Then we carry out extensive simulation studies, which prove that the mathematical model matches simulation results well.

Zhengbing Hu

Papers

Actively Measuring Bots in Peer-to-Peer Networks

An Ensemble of Adaptive Neuro-Fuzzy Kohonen Networks for Online Data Stream Fuzzy Clustering

Clustering Matrix Sequences Based on the Iterative Dynamic Time Deformation Procedure

A deep cascade neural network based on extended neo-fuzzy neurons and its adaptive learning algorithm

Evolutionary Proactive P2P Worm: Propagation Modeling and Simulation