Phishing websites can have devastating effects on governmental, financial, and social services, as well as on individual privacy. Currently, many phishing detection solutions are evaluated using small datasets and, thus, are prone to sampling issues, such as representing legitimate websites by only high-ranking websites, which could make their evaluation less relevant in practice. Phishing detection solutions which depend only on the URL are attractive, as they can be used in limited systems, such as with firewalls. In this paper, we present a URL-only phishing detection solution based on a convolutional neural network (CNN) model. The proposed CNN takes the URL as the input, rather than using predetermined features such as URL length. For training and evaluation, we have collected over two million URLs in a massive URL phishing detection (MUPD) dataset. We split MUPD into training, validation and testing datasets. The proposed CNN achieves approximately 96% accuracy on the testing dataset; this accuracy is achieved with URL schemes (such as HTTP and HTTPS) removed from the URL. Our proposed solution achieved better accuracy compared to an existing state-of-the-art URL-only model on a published dataset. Finally, the results of our experiment suggest keeping the CNN up-to-date for better results in practice.

This paper aims to compare the scenic images of four ancient Chinese cities including Lijiang, Pingyao, Huizhou and Langzhong, so as to provide specific development strategies for the ancient cities. In this paper, the ancient cities' scenic images are divided into three sub-indexes and eight evaluation dimensions. Based on this, the study first uses Python software to collect tourists' online comments on the four ancient cities. Then, the social network analysis method is used to build a high-frequency keywords matrix of tourist comments and the R language is used to generate a visual network graph. After this, the entropy weight method is used to determine the weights and values of eight evaluation dimensions. Finally, the tourists' overall satisfaction indexes of the four ancient cities are calculated accordingly. The results show that (1) the overall satisfaction of Lijiang is the highest, while that of Huizhou is the lowest; (2) from the weight of each evaluation dimension, it can be seen that tourists care more about the national culture and historical culture; (3) from tourists' satisfaction index on each evaluation dimension of the four ancient cities, we can find that the four ancient cities has their own advantages and disadvantages in tourism development. (4) local tourism-related institutions should strengthen their advantages and improve their deficiencies so as to enhance tourists' overall image of the ancient city.

We propose a novel speaker adaptation method using acoustic model clustering. The similarity of different speakers is defined by the cosine distance between their i-vectors (intermediate vectors), and various efficient clustering algorithms are applied to obtain a number of speaker subsets with different characteristics. The speaker-independent model is then retrained with the training data of the individual speaker subsets grouped by the clustering results, and an unknown speech is recognized by the retrained model of the closest cluster. The proposed method is applied to a large-scale speech recognition system implemented by a hybrid hidden Markov model and deep neural network framework. An experiment was conducted to evaluate the word error rates using Resource Management database. When the proposed speaker adaptation method using i-vector based clustering was applied, the performance, as compared to that of the conventional speaker-independent speech recognition model, was improved relatively by as much as 12.2% for the conventional fully neural network, and by as much as 10.5% for the bidirectional long short-term memory.

5G network optimization problem is a challenging optimization problem in the practical engineering applications. In this paper, to tackle this issue, Pareto fronts-driven Multi-Objective Cuckoo Search (PMOCS) is proposed based on Cuckoo Search. Firstly, the original global search manner is upgraded to a new form, which is aimed to strengthening the convergence. Then, the original local search manner is modified to highlight the diversity. To test the overall performance of PMOCS, PMOCS is test on three test suits against several classical comparison methods. Experimental results demonstrate that PMOCS exhibits outstanding performance. Further experiments on the 5G network optimization problem indicates that PMOCS is promising compared with other methods.

In a cloud computing paradigm, allocation of various virtualized ICT resources is a complex problem due to the presence of heterogeneous application (MapReduce, content delivery and networks web applications) workloads having contentious allocation requirements in terms of ICT resource capacities (resource utilization, execution time, response time, etc.). This task of resource allocation becomes more challenging due to finite available resources and increasing consumer demands. Therefore, many unique models and techniques have been proposed to allocate resources efficiently. However, there is no published research available in this domain that clearly address this research problem and provides research taxonomy for classification of resource allocation techniques including strategic, target resources, optimization, scheduling and power. Hence, the main aim of this paper is to identify open challenges faced by the cloud service provider related to allocation of resource such as servers, storage and networks in cloud computing. More than 70 articles, between year 2007 and 2020, related to resource allocation in cloud computing have been shortlisted through a structured mechanism and are reviewed under clearly defined objectives. Lastly, the evolution of research in resource allocation techniques has also been discussed along with salient future directions in this area.

The purpose of this survey-based study is to make an analysis of search engine users' behaviors on the Search Engine Results Pages (SERPs) based on the three demographic characteristics gender, age, and program studying. In this study, a questionnaire was designed with 12 closed-ended questions. Remaining questions other than the demographic characteristic related ones were about "tab", "advertisement", "spelling suggestion", "related query suggestion", "instant search suggestion", "video result", "image result", "pagination" and the amount of clicking results. The questionnaire was used and the data collected were analyzed with the descriptive statistics as well as the inferential statistics. 84.2% of the study population was reached. Some of the major results are as follows: Most of each demographic characteristic category (i.e. female, male, under-20, 20-24, above-24, English computer engineering, Turkish computer engineering, software engineering) have rarely or more click for tab, spelling suggestion, related query suggestion, instant search suggestion, video result, image result, and pagination. More than 50.0% of female category click advertisement rarely; however, for the others, 50.0% or more never click advertisement. For every demographic characteristic category, between 78.0% and 85.4% click 10 or fewer results. This study would be the first attempt with its complete content and design. Search engine providers and researchers would gain knowledge to user behaviors about the usage of the SERPs based on the demographic characteristics.

Data Security is the most challenging area in Internet communication, where most of the secret sharing schemes are proposed for binary images. But still it lacks in providing security for data communication, especially in image transmission. Traditional visual cryptography scheme generate meaningless diwies and the reconstruction phase leads to quality degradation over the secret image. In this work, an intelligent two dimensional secret share construction scheme is proposed. A secret image is expanded into n diwies with the choice of scheme selection. By Stacking all the qualified diwies to revert the secret image without content loss and less than s^* - 1 shares could not reveal any information about the secret image. The experimental results emphasize that the proposed secret share scheme is highly secured for image transmission.

In this paper, we study the estimation of angle, range and polarization parameters of a bistatic polarization sensitive frequency diverse array multiple-input multiple-output (PSFDA-MIMO) radar system. The application of polarization sensitive array in receiver is explored. A signal model of bistatic PSFDA-MIMO radar system is established. In order to utilize the multi-dimensional structure of array signals, the matched filtering radar data can be represented by a third-order tensor model. A joint estimation of the direction-of-departure (DOD), direction-of-arrival (DOA), range and polarization parameters based on parallel factor (PARAFAC) algorithm is proposed. The proposed algorithm does not need to search spectral peaks and singular value decomposition, and can obtain automatic pairing estimation. The method was compared with the existing methods, and the results show that the performance of the method is better. Therefore, the accuracy of the parameter estimation is further improved.

Phase-generated carrier (PGC) demodulation algorithm is the main demodulation methods in Fiber-optic interferometric sensors (FOISs). The conventional PGC demodulation algorithms are influenced by the carrier phase delay between the interference signal and the carrier signal. In this paper, an automatic carrier phase delay synchronization (CPDS) algorithm based on orthogonal phase-locked technique is proposed. The proposed algorithm can calculate the carrier phase delay value. Then the carrier phase delay can be compensated by adjusting the initial phase of the fundamental carrier and the second-harmonic carrier. The simulation results demonstrate the influence of the carrier phase delay on the demodulation performance. PGC-Arctan demodulation system based on CPDS algorithm is implemented on SoC. The experimental results show that the proposed algorithm is able to obtain and eliminate the carrier phase delay. In comparison to the conventional demodulation algorithm, the signal-to-noise and distortion ratio (SINAD) of the proposed algorithm increases 55.99dB.

With a huge demand for replicated content on the Internet, a new networking paradigm called information-centric networking (ICN) has been introduced for efficient content dissemination. In ICN, named content is distributed over the network cache and it is accessed by name instead of a location identifier. These aspects allow users to retrieve content from any of the nodes having replicas, and consequently 1) network resources are more efficiently utilized by avoiding redundant transmission and 2) more scalable services are provided by distributing server loads. However, in-network caching in ICN brings about a new type of security issues, called content poisoning attacks, where fabricated content is located in the network cache and interferes with the normal behavior of the system. In this paper, we look into the problems of content poisoning in ICN and discuss security architectures against them. In particular, we reconsider the state-of-the-art schemes from the perspective of feasibility, and propose a practical security architecture.

Post-Quantum Cryptography (PQC) is rapidly developing as a stable and reliable quantum-resistant form of cryptography, throughout the industry. Similarly to existing cryptography, however, it does not prevent a third-party from using the secret key when third party obtains the secret key by deception, unauthorized sharing, or unauthorized proxying. The most effective alternative to preventing such illegal use is the utilization of biometrics during the generation of the secret key. In this paper, we propose a biometric-based secret key generation scheme for multivariate quadratic signature schemes, such as Rainbow. This prevents the secret key from being used by an unauthorized third party through biometric recognition. It also generates a shorter secret key by applying Principal Component Analysis (PCA)-based Confidence Interval Analysis (CIA) as a feature extraction method. This scheme's optimized implementation performed well at high speeds.

Objective image quality assessment (IQA) models have been developed by effective features to imitate the characteristics of human visual system (HVS). Actually, HVS is extremely sensitive to color degradation and complex texture changes. In this paper, we firstly reveal that many existing full reference image quality assessment (FR-IQA) methods can hardly measure the image quality with contrast and masking texture changes. To solve this problem, considering texture masking effect, we proposed a novel FR-IQA method, called Texture and Color Quality Index (TCQI). The proposed method considers both in the masking effect texture and color visual perceptual threshold, which adopts three kinds of features to reflect masking texture, color difference and structural information. Furthermore, random forest (RF) is used to address the drawbacks of existing pooling technologies. Compared with other traditional learning-based tools (support vector regression and neural network), RF can achieve the better prediction performance. Experiments conducted on five large-scale databases demonstrate that our approach is highly consistent with subjective perception, outperforms twelve the state-of-the-art IQA models in terms of prediction accuracy and keeps a moderate computational complexity. The cross database validation also validates our approach achieves the ability to maintain high robustness.

Ensemble clustering commonly integrates multiple basic partitions to obtain a more accurate clustering result than a single partition. Specifically, it exists an inevitable problem that the incomplete transformation from the original space to the integrated space. In this paper, a novel ensemble clustering algorithm using a newly reconstructed mapping coefficient (ECRMC) is proposed. In the algorithm, a newly reconstructed mapping coefficient between objects and micro-clusters is designed based on the principle of increasing information entropy to enhance effective information. This can reduce the information loss in the transformation from micro-clusters to the original space. Then the correlation of the micro-clusters is creatively calculated by the Spearman coefficient. Therefore, the revised co-association graph between objects can be built more accurately because the supplementary information can well ensure the completeness of the whole conversion process. Experiment results demonstrate that the ECRMC clustering algorithm has high performance, effectiveness, and feasibility.

With the increasing amount of splicing images, many detection schemes of splicing images are proposed. In this paper, a splicing detection scheme for color image based on the quaternion discrete cosine transform (QDCT) is proposed. Firstly, the proposed quaternion Markov features are extracted in QDCT domain. Secondly, the proposed quaternion Markov features consist of global and local quaternion Markov, which utilize both magnitude and three phases to extract Markov features by using two different ways. In total, 2916-D features are extracted. Finally, the support vector machine (SVM) is used to detect the splicing images. In our experiments, the accuracy of the proposed scheme reaches 99.16% and 97.52% in CASIA TIDE v1.0 and CASIA TIDE v2.0, respectively, which exceeds that of the existing schemes.

With the advancement in the field of image processing, images are being processed using various image processing algorithms. Nowadays, many efficient content-aware image resizing techniques are being used to safeguard the prominent regions and to generate better results that are visually appealing and pleasing while resizing. Advancements in the new display device with varying screen size demands the development of efficient image resizing algorithm. This paper presents a survey on various image retargeting methods, comparison of image retargeting results based on performance, and also exposes the main challenges in image retargeting such as content preservation of important regions, distortion minimization, and improving the efficiency of image retargeting methods. After reviewing literature from researchers it is suggested that the use of the single operator in image retargeting such as scaling, cropping, seam carving, and warping is not sufficient for obtaining satisfactory results, hence it is essential to combine multiple image retargeting operators. This survey is useful for the researchers interested in content-aware image retargeting.

In the stereoscopic or multiview display, the depth video illustrates visual distances between objects and camera. To promote the computational efficiency of depth video encoder, we exploit the intra prediction of depth videos under Versatile Video Coding (VVC) and observe a diverse distribution of intra prediction modes with different coding unit sizes. We propose a hybrid scheme to further boost fast depth video coding. In the first stage, we adaptively predict the HADamard (HAD) costs of intra prediction modes and initialize a candidate list according to the HAD costs. Then, the candidate list is further improved by considering the probability distribution of candidate modes with different CU sizes. Finally, early termination of CU splitting is performed at each CU depth level based on the Bayesian theorem. Our proposed method is incorporated into VVC intra prediction for fast coding of depth videos. Experiments with 7 standard sequences and 4 Quantization parameters (Qps) validate the efficiency of our method.

Beaches have many risk factors that cause various accidents, such as drifting and drowning, these accidents have many risk factors. To analyze them, in this paper, we identify beach risk factors, and define the criteria and correlation for each risk factor. Then, we generate new risk factors based on Fuzzy theory, and define Situation Awareness for each time. Finally, we propose a beach risk assessment and prediction model based on linear regression using the calculated risk result and pre-defined risk factors. We use national public data of the Korea Meteorological Administration (KMA), and the Korea Hydrographic and Oceanographic Agency (KHOA). The results of the experiment showed the prediction accuracy of beach risk to be 0.90%, and the prediction accuracy of drifting and drowning accidents to be 0.89% and 0.86%, respectively. Also, through factor correlation analysis and risk factor assessment, the influence of each of the factors on beach risk can be confirmed. In conclusion, we confirmed that our proposed model can assess and predict beach risks.

End-to-end network delay plays an vital role in distributed services. This delay is used to measure QoS (Quality-of-Service). It would be beneficial to know all node-pair delay information, but unfortunately it is not feasible in practice because the use of active probing will cause a quadratic growth in overhead. Alternatively, using the measured network delay to estimate the unknown network delay is an economical method. In this paper, we adopt the state-of-the-art matrix completion technology to better estimate the network delay from limited measurements. Although the number of measurements required for an exact matrix completion is theoretically bounded, it is practically less helpful. Therefore, we propose an online adaptive sampling algorithm to measure network delay in which statistical leverage scores are used to select potential matrix elements. The basic principle behind is to sample the elements with larger leverage scores to keep the traits of important rows or columns in the matrix. The amount of samples is adaptively decided by a proposed stopping condition. Simulation results based on real delay matrix show that compared with the traditional sampling algorithm, our proposed sampling algorithm can provide better performance (smaller estimation error and less convergence pressure) at a lower cost (fewer samples and shorter processing time).

An IKPCA-ELM-based intrusion detection method is developed to address the problem of the low accuracy and slow speed of intrusion detection caused by redundancies and high dimensions of data in the network. First, in order to reduce the effects of uneven sample distribution and sample attribute differences on the extraction of KPCA features, the sample attribute mean and mean square error are introduced into the Gaussian radial basis function and polynomial kernel function respectively, and the two improved kernel functions are combined to construct a hybrid kernel function. Second, an improved particle swarm optimization (IPSO) algorithm is proposed to determine the optimal hybrid kernel function for improved kernel principal component analysis (IKPCA). Finally, IKPCA is conducted to complete feature extraction, and an extreme learning machine (ELM) is applied to classify common attack type detection. The experimental results demonstrate the effectiveness of the constructed hybrid kernel function. Compared with other intrusion detection methods, IKPCA-ELM not only ensures high accuracy rates, but also reduces the detection time and false alarm rate, especially reducing the false alarm rate of small sample attacks.

Network anomaly detection system plays an essential role in detecting network anomaly and ensuring network security. Anomaly detection system based machine learning has become an increasingly popular solution. However, due to the unbalance and high-dimension characteristics of network traffic, the existing methods unable to achieve the excellent performance of high accuracy and low false alarm rate. To address this problem, a new network anomaly detection method based on data balancing and recursive feature addition is proposed. Firstly, data balancing algorithm based on improved KNN outlier detection is designed to select part respective data on each category. Combination optimization about parameters of improved KNN outlier detection is implemented by genetic algorithm. Next, recursive feature addition algorithm based on correlation analysis is proposed to select effective features, in which a cross contingency test is utilized to analyze correlation and obtain a features subset with a strong correlation. Then, random forests model is as the classification model to detection anomaly. Finally, the proposed algorithm is evaluated on benchmark datasets KDD Cup 1999 and UNSW_NB15. The result illustrates the proposed strategies enhance accuracy and recall, and decrease the false alarm rate. Compared with other algorithms, this algorithm still achieves significant effects, especially recall in the small category.

Based on the teleportation by quantum walk, a quantum secret sharing scheme with credible authentication is proposed. Using the Hash function and quantum local operation, combined with the two-step quantum walks circuit on the line, the identity authentication and the teleportation of the secret information in distribution phase are realized. Participants collaborate honestly to recover secret information based on particle measurement results, preventing untrusted agents and external attacks from obtaining useful information. Due to the application of quantum walk, the sender does not need to prepare the necessary entangled state in advance, simply encodes the information to be sent in the coin state, and applies the conditional shift operator between the coin space and the position space to produce the entangled state necessary for quantum teleportation. Security analysis shows that the protocol can effectively resist intercept/resend attacks, entanglement attacks, participant attacks, and impersonation attacks. In addition, the quantum walk circuit used has been implemented in many different physical systems and experiments, so this quantum secret sharing scheme may be achievable in the future.

In this paper, we study the joint radio and computational resource allocation in the ultra-dense mobile-edge computing networks. In which, the scenario which including both computation offloading and communication service is discussed. That is, some mobile users ask for computation offloading, while the others ask for communication with the minimum communication rate requirements. We formulate the problem as a joint channel assignment, power control and computational resource allocation to minimize the offloading cost of computing offloading, with the precondition that the transmission rate of communication nodes are satisfied. Since the formulated problem is a mixed-integer nonlinear programming (MINLP), which is NP-hard. By leveraging the particular mathematical structure of the problem, i.e., the computational resource allocation variable is independent with other variables in the objective function and constraints, and then the original problem is decomposed into a computational resource allocation subproblem and a joint channel assignment and power allocation subproblem. Since the former is a convex programming, the KKT (Karush-Kuhn-Tucker) conditions can be used to find the closed optimal solution. For the latter, which is still NP-hard, is further decomposed into two subproblems, i.e., the power allocation and the channel assignment, to optimize alternatively. Finally, two heuristic algorithms are proposed, i.e., the Co-channel Equal Power allocation algorithm (CEP) and the Enhanced CEP (ECEP) algorithm to obtain the suboptimal solutions. Numerical results are presented at last to verify the performance of the proposed algorithms.

One drawback of polar codes is that they are not universal, that is, to achieve optimal performance, different polar codes are required for different kinds of channel. This paper proposes a polar code construction scheme for Nakagami-m fading channel. The scheme fully considers the characteristics of Nakagami-m fading channel, and uses the optimized Bhattacharyya parameter bounds. The constructed code is applied to an orthogonal frequency division multiplexing (OFDM) system over Nakagami-m fading channel to prove the performance of polar code. Simulation result shows the proposed codes can get excellent bit error rate (BER) performance with successive cancellation list (SCL) decoding. For example, the designed polar code with cyclic redundancy check (CRC) aided SCL (L = 8) decoding achieves 1.1dB of gain over LDPC at average BER about 10^-5 under 4-quadrature amplitude modulation (4QAM) while the code length is 1024, rate is 0.5.