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Quality of data services is crucial for operational large-scale internet-of-things (IoT) research data infrastructure, in particular when serving large amounts of distributed users. Effectively detecting runtime anomalies and diagnosing their root cause helps to defend against adversarial attacks, thereby essentially boosting system security and robustness of the IoT infrastructure services. However, conventional anomaly detection methods are inadequate when facing the dynamic complexities of these systems. In contrast, supervised machine learning methods are unable to exploit large amounts of data due to the unavailability of labeled data. This paper leverages popular GAN-based generative models and end-to-end one-class classification to improve unsupervised anomaly detection. A novel heterogeneous BiGAN-based anomaly detection model Heterogeneous Temporal Anomaly-reconstruction GAN (HTA-GAN) is proposed to make better use of a one-class classifier and a novel anomaly scoring function. The Generator-Encoder-Discriminator BiGAN structure can lead to practical anomaly score computation and temporal feature capturing. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on real-world datasets, anomaly benchmarks and synthetic datasets. The results show that HTA-GAN outperforms its competitors and demonstrates better robustness. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet of Things (IoT), network security challenges are becoming more and more complex, and the scale of intrusion attacks against the network is gradually increasing. Therefore, researchers have proposed Intrusion Detection Systems and constantly designed more effective systems to defend against attacks. One issue to consider is using limited computing power to process complex network data efficiently. In this paper, we take the AWID dataset as an example, propose an efficient data processing method to mitigate the interference caused by redundant data and design a lightweight deep learning-based model to analyze and predict the data category. Finally, we achieve an overall accuracy of 99.77% and an accuracy of 97.95% for attacks on the AWID dataset, with a detection rate of 99.98% for the injection attack. Our model has low computational overhead and a fast response time after training, ensuring the feasibility of applying to edge nodes with weak computational power in the IoT. [ABSTRACT FROM AUTHOR]

Federated generative adversarial networks are designed to collaborate across the communication and privacy-constrained edge servers participating in training. However, in the Internet of Things scenario, local updates uploaded by edge servers can lead to the risk of privacy breaches. Gradient-sanitized-based approaches can transmit sanitized sensitive data with strict privacy guarantees, but gradient clipping and perturbation severely degrade convergence performance. In this paper, our proposed algorithm enhances the privacy of terminated raw data through differential privacy before it is transmitted to the edge server. The edge server trains the local generator and discriminator using the perturbed data, which provides privacy guarantees for the gradient attack on the FedGAN without compromising the gradient accuracy. The results of the experimental evaluation show that the algorithm generates images with slightly better quality than that generated by the gradient-sanitized-based approaches while maintaining privacy. [ABSTRACT FROM AUTHOR]

In the Internet of Things, smart devices are expected to correctly capture and process data from environments, regardless of perturbation and adversarial attacks. Therefore, it is important to guarantee the robustness of their intelligent components, e.g. neural networks, to protect the system from environment perturbation and adversarial attacks. In this paper, we propose a formal verification technique for rigorously proving the robustness of neural networks. Our approach leverages a tight liner approximation technique and constraint substitution, by which we transform the robustness verification problem into an efficiently solvable linear programming problem. Unlike existing approaches, our approach can automatically generate adversarial examples when a neural network fails to verify. Besides, it is general and applicable to more complex neural network architectures such as CNN, LeNet and ResNet. We implement the approach in a prototype tool called WiNR and evaluate it on extensive benchmarks, including Fashion MNIST, CIFAR10 and GTSRB. Experimental results show that WiNR can verify neural networks that contain over 10 000 neurons on one input image in a minute with a 6.28% probability of false positive on average. [ABSTRACT FROM AUTHOR]

With the development of 5G, the wireless Internet of Things (IoT) has become possible; how to provide privacy protections for the communication of IoT devices in a more vulnerable wireless transmission environment is a huge challenge. Thus, steganography is introduced as a safe and effective technology. Blockchain systems have been widely used in the area of steganography. Several works attempted to embed covert data into transactions in public blockchain systems such as Bitcoin, Ethereum and Monero. However, most of them merely focus on putting covert data into certain fields in transactions based on cryptographic algorithms. In this paper, a Covert Transaction Recognition (CTR) model is proposed by the Text Convolutional Neural Networks and Back Propagation Neural Networks. When utilizing the covert data-embedded field for recognizing, our CTR model can attain 0.79 precision and 0.83 recall on average for seven covert transaction construction schemes. The precision and recall can increase by at most 43 and 47%, respectively, if other unembedded fields were additionally exploited for recognition. We further propose a Practical Covert Transaction Construction (PCTC) model. This model fixes the contents in the embedded fields of the constructed transactions, and generates the contents in other fields using Generative Adversarial Networks. Experimental results demonstrated that the precision and recall are greatly decreased when identifying the covert transactions generated by our PCTC model. The data underlying this article are available in 'covert-transaction-model', at https://github.com/1997mint/covert-transaction-model. [ABSTRACT FROM AUTHOR]

Many disciplines have adopted component-based principles to avail themselves of the many advantages they bring, especially component reusability. In a short time, the component-based architecture became a renown branch in the IT world and the center of interest of many researchers. Much work has been conducted in this context for the verification of component-based applications (CBAs). However, the main focus has been on the structural aspect of such compositions, while the behavioral aspect has seldom been dealt with. In this paper, our goal is to close this gap and propose a formal approach to verify the behavioral correctness of CBAs. We first define a set of requirements to be satisfied by the structure and the behavior of a CBA, represented by a set of interactions that may occur between their components. Then, we build a formal Event-B model to represent these requirements in a rigorous and non-ambiguous way. The use of the Event-B refinement technique allows us to master the complexity of CBAs by introducing their elements in an incremental manner. The correctness of the development is ensured by establishing a set of proof obligations, under the Rodin platform, and also by animating it with the ProB animator/model checker. The approach is illustrated by a running example. [ABSTRACT FROM AUTHOR]

The use of fingerprint continues to increase constantly with the propagation of biometric authentication technologies. Fingerprint is today the most widely used biometric modality for human verification and identification, due to its practical use and its discriminative structure that allows to different identities to be easily distinguished. Unfortunately, this emergence has been accompanied by a number of problems and challenges related to identity theft and to security issues in general. To address these concerns, many approaches have been proposed in which only few of them were able to reach an acceptable level of both security and performance. In this paper, we propose a new fingerprint template protection scheme that enhances the security of protected system while preserving performance. The proposed approach is a minutiae-based technique that performs fingerprint matching in a transformed space using irreversible minutiae tetrahedrons. Using the original Fingerprint Verification Competition (FVC) protocol, the provided experimental results on FVC2002 DB1, DB2 and DB3 fingerprint databases have shown satisfactory recognition rates. Our results are compared to some existing techniques that use the same protocol of test. We have proved as well that the proposed scheme meets the requirements of revocability, unlinkability and irreversibility. [ABSTRACT FROM AUTHOR]

Background Breast cancer is the most common disease in women, and it is very difficult to detect breast cancer early, so there are fewer reasons to measure women's survival. The purpose of this paper to apply various basic machine learning algorithms on multiple breast cancer datasets and the obtained results combined with an ensemble technique to find whether the datasets getting high accuracy. Methods Five machine learning algorithms such as decision tree, random forest, k-nearest neighbor, support vector clustering (SVC) and logistic regression (LR) are used to achieve high accuracy. In this study, along with five classifiers and voting ensemble methods on three different datasets (Mammographic mass data, Original Wisconsin breast cancer data and Diagnostic Wisconsin breast cancer data) to obtain the predicted accuracy recall, precision and receiver operating characteristic (ROC) curves. The results obtained by three different datasets were compared. Results By applying five classifiers and voting ensemble, the comparison results show that logistic regression, SVC and voting ensemble methods are potentially effective for predicting classification accuracy. The highest value is (Accuracy: 97.13% with ensemble by D2, Precision: 96.90% with ensemble and LR method by D3, Recall: 97.78% with SVC by D3, ROC: 99.93% with LR by D2). Conclusions Machine learning classifiers and ensemble technologies have the potential to identify diseases. The applied method highlights the importance of classifiers and ensemble methods. [ABSTRACT FROM AUTHOR]

Counter-cryptanalysis uses cryptanalytic techniques to detect cryptanalytic attacks. It was introduced by Stevens with a collision detection algorithm that detects whether a message is one of a colliding message pair constructed using a collision attack. Later, Stevens and Shumow improved the collision detection against SHA-1 by using unavoidable conditions. However, there are no results improving collision detection against MD5 due to its weak diffusion properties. In this paper, an improved collision detection algorithm against MD5 is proposed by using the 14-bit sufficient condition combinations. This leads to the dividing the 223 classes into four sets. Each element, belonging to the first two sets, holds the same sufficient condition combination. Our new algorithm can classify 126 classes efficiently. The runtime is 28.6% of the previous collision detection method. [ABSTRACT FROM AUTHOR]

Traditional link prediction methods of social network are vulnerable to the influence of network structure and have poor generalization, and only on a small number of networks and evaluation indicators. To improve the stability and accuracy of link prediction, this paper assembles 15 similarity indexes, introduces the idea of stacking into the link prediction of complex networks, and presents a link prediction method (Logistic-regression LightGBM Stacking Link Prediction, LLSLP). Firstly, social network link prediction is regarded as a binary classification problem. Secondly, the hyper parameters of the basic model are determined by using cross-validation and grid searching; thirdly, Logistic-regression and LightGBM are integrated by stacked generalization; Finally, take 10 different networks as practical examples. The feasibility and effectiveness of the proposed method are verified by comparing 7 evaluation indicators. The experimental results show that: the proposed method is not only more than 98.71% higher than the traditional CN (Common Neighbor) and other models are 10.52% higher on average. In addition, compared with the traditional 15 link prediction algorithms, |$F1- score$| value and |$MCC$| (Matthews Correlation Coefficient) value is increased by 3.2% ~ 9.7% and 5.9% ~ 14% respectively. The proposed method has good accuracy and generalization. It can also be applied to recommendation system. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) is a well-known and eminent recommendation technique to predict the preference of new users by revealing the structures of historical records of the examined users. Even though CF is effectively adapted in several commercial areas, many limitations still exist, particularly in the sparsity of rating data that raises many issues. This paper devises a novel deep learning strategy for CF to recognize user preferences. Here, black hole entropic fuzzy clustering (BHEFC) is devised for clustering item sequences to form groups with similar item sequences. Moreover, cluster centroids are optimized using the tunicate swarm magnetic optimization algorithm (TSMOA), which is devised by combining tunicate swarm algorithm and magnetic optimization algorithm. After grouping similar items together, the group matching is performed based on a deep convolutional neural network (Deep CNN). Subsequently, the visitor sequence and query sequence are compared using Jaro–Winkler distance, which contributes to the best visitor sequence. From this best visitor sequence, the recommended product is acquired. The proposed TSMOA–BHEFC and Deep CNN outperformed other methods with minimal mean absolute error of 0.200, mean absolute percentage error of 0.198 and root mean square error of 0.447, respectively. [ABSTRACT FROM AUTHOR]

Industrial IoT (IIoT) era is evolving rapidly in parallel to the progress in Industry 4.0, which leads factories to increase the engagement with external parties through different communication infrastructures. This brings a larger attack surface and requires the development of new security solutions suitable for IIoT systems. Authentication is a key enabler to prevent the attacks that come from the interactions with the outside world components. Though there are proposed authentication schemes for IoT or machine-to-machine (M2M) applications, they cannot be readily applied for IIoT since manufacturing machines have different features and requirements than generic IT and IoT devices. In this paper, context information is proposed to be used for enhancing the authentication process in the IIoT, where instantaneous electricity consumption measured by smart meters is the main context information used as a dynamic authentication credential. Besides, in our method, existing smart meter infrastructure is utilized for both exchanging credentials over an industrial network and verification of the credentials by trusted components like Supervisory Control and Data Acquisition (SCADA) or Energy Management System (EMS). The proposed method also allows the use of other context information as authentication credentials such as temperature and humidity collected by sensors in the manufacturing environment. [ABSTRACT FROM AUTHOR]

Today, machine learning plays a major role in different branches of the healthcare industry, from prognosis and diagnosis to drug development providing a significant perspective on the medical landscape for disease prevention or treatment and the improvement of human life. Recently, the use of deep neural networks in different machine learning applications has shown a great contribution to the improvement of the accuracy of predictions. In this paper, a novel application of convolutional neural networks on medical prognosis is presented. The proposed method employs a one-dimensional convolutional neural network (1D-CNN) to predict the survivability of breast cancer patients. After further examining the network architecture, a number of 8, 14 and 24 convolutional filters were considered within three layers, respectively, followed by a max-pooling layer after the second and third layers. In addition, regarding the probabilistic nature of the survivability prediction problem, an extra layer was added to the network in order to calculate the probability of the patient survivability. To train the developed 1D-CNN machine, the SEER database as the most reliable repository of cancer survivability was used to retrieve the required training set. After a pre-processing to remove unusable records, a set of 50 000 breast cancer cases including 35 features was prepared for training the machine. Based on the results obtained in this study, the developed machine could reach an accuracy of 85.84%. This accuracy is the highest level of accuracy compared to the previous prediction methods. Furthermore, the mean squared error of the calculated probability was 0.112, which is an acceptable value of error for a probability calculation machine. The output of the developed machine can be used reliably by physicians to make decision about the most appropriate treatment strategy. [ABSTRACT FROM AUTHOR]

Crowdsourcing has become increasingly popular in recent years. In order to achieve the optimal task allocation, one of the most important issues is to select more suitable crowdworkers. By leveraging its pervasiveness, social network can be employed as a novel worker recruitment platform. A robust task allocation scheme over the social network could also consider the word-of-mouth (WoM) mode, in which tasks are delivered from workers to workers. In this paper, we discuss an Non-deterministic Polynomial-Hard (NP-Hard) problem, cost-effective and budget-balanced task allocation (CBTA) problem under the WoM mode in social groups. We propose two heuristic algorithms: CB-greedy and CB-local based on greedy strategy and local search technique, respectively. We also prove that the running time of CB-greedy is |$O(m^2\log m)$|⁠ , whereas CB-local utilizing disjoint-set achieves |$O(mn\alpha (m, n))$|⁠ , where |$m$| is the number of edges indicating interactions of social groups, |$n$| is the number of social groups and |$\alpha $| is the inverse Ackerman function. Extensive experiments validate the efficiency and performance of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Opinion mining from medical forums such as health check-ups is sparking growing interest and a stimulating area for natural language processing. This allows for a better understanding of patient health status and drug reactions while generating new knowledge for health care professionals and drug manufacturers, which helps improve the quality of service and produce more effective treatments. In this paper, the researchers present a framework of opinions classification of drug reviews. The objective of this work is to find the best model for analyzing patients' emotions about drugs. In this sense, the researchers oppose classical text vectorization methods (bag of words, term frequency-inverse document frequency) and word embedding methods (Word2vec, GloVe) for classical opinion mining face to modern machine learning tools with the Convolutional Neural Network (CNN), the Recurrent Neural Networks (Long Short-term Memory and Bidirectional Long Short-Term Memory). Experiments results show that the best model for drug reviews was achieved by CNN based on the Skip-gram model (85% accuracy). Experiments have led to conclude that the performance of a given model will depend on the type of dataset used, on feature representation and better collaboration between classifiers and feature extraction methods. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is the process of identifying human expressions. People vary in their accuracy at recognizing the emotions of others. Use of technology to help people with emotion recognition is a relatively important research area. Various works have been conducted on automating the recognition of facial expressions. The main intent of this paper is to plan for the FER model with the aid of intelligent techniques. The proposed models consist of steps like data collection, face detection, optimized feature extraction and emotion recognition. Initially, the standard benchmark facial emotion dataset is collected, and it is subjected to face detection. The optimized scale-invariant feature transform (OSIFT) is adopted for feature extraction, in which the key points that are giving unique information are optimized by the hybrid meta-heuristic algorithm. Two meta-heuristic algorithms like spotted hyena optimization and beetle swarm optimization (BSO) are merged to form the proposed spotted hyena-based BSO (SH-BSO). Also, the local tri-directional pattern is extracted, which is further combined with optimized SIFT. Here, the proposed SH-BSO is utilized for optimizing the number of hidden neurons of both deep neural network and convolutional neural network in such a way that the recognition accuracy could attain maximum. [ABSTRACT FROM AUTHOR]

For the multiprocessor systems modeled by interconnection networks, one of the important properties is the characterization of fault tolerability. Connectivity, as an important parameter to evaluate fault tolerability, has witnessed research achievements. To make the evaluation more practical, conditional connectivity has been promisingly proposed. As one kind of conditional connectivity, |$h$| -restricted connectivity of a connected graph |$G$|⁠ , denoted by |$\kappa ^h (G)$|⁠ , is defined as the cardinality of the minimum vertex cut set |$F$| such that |$\delta (G-F)\geq h$|⁠. In this paper, we establish a universally |$h$| -restricted connectivity for a class of hypercube-based compound networks, in which the well-known networks, such as hierarchical cubic network |$HCN(n, n)$| and its generalization complete cubic network |$CCN(n)$|⁠ , are involved. [ABSTRACT FROM AUTHOR]

Attributable to the rapid growth of information technology, the Internet of Things (IoT) having strong permeability characteristics, huge usage of action and better comprehensive benefits. However, it encourages the development of IoT technology in the detection of structural engineering. Structural health monitoring (SHM) is responsible for identifying techniques and for prototyping systems performing a state diagnosis of structures. Its aim is to prevent sudden civil infrastructure failure as a result of several invisible sources of damage. This paper devises a novel method, namely bat-antlion Optimization dependent generative adversarial network (BALO-based GAN) for monitoring the states of structural health. Here, IoT nodes sense the signals of each channels and sensed data are transmitted to base station (BS) using Monarch-Earthworm (Monarch-EWA)-enabled secure routing protocol that selects the optimal path for the data transmission. After performing the IoT routing, the state of the structural health is monitored at the BS. For SHM, the input signal acquired from the IoT routing phase is fed to the pre-processing step for improving the signal quality for further processing. Then, the feature extraction is performed using fractional-amplitude modulation spectrogram (fractional AMS) for extracting the best features for improving the classification accuracy. The extracted features are adapted by the GAN, which is trained by BALO. The proposed BALO is newly designed by integrating the Bat algorithm and antlion optimizer. The proposed BALO-based GAN showed improved performance with maximal accuracy of 0.912, maximal sensitivity of 0.911, maximal throughput of 0.972 and maximal specificity of 0.913, respectively. [ABSTRACT FROM AUTHOR]

Deoxys-BC is an internal tweakable block cipher of the authenticated encryption algorithm Deoxys, which is a third-round finalist in the CAESAR competition. In this paper, we study the property of Deoxys-BC, such as the subtweakey difference cancelation and the freedom of the tweak. Combining the differential enumeration technique with these properties, the authors achieve the key-recovery attacks on Deoxys-BC under the meet-in-the-middle attack. As a result, we get an attack on 9-round Deoxys-BC-128-128 by constructing a 6-round meet-in-the-middle distinguisher with |$2^{113}$| plaintext–tweak combinations, |$2^{97}$| Deoxys-BC blocks and |$2^{121.6}$| 9-round Deoxys-BC-128-128 encryptions. We also present an attack on 11-round Deoxys-BC-256-128 for the first time by constructing a 7-round meet-in-the-middle distinguisher with |$2^{113}$| plaintext-tweak combinations, |$2^{226}$| Deoxys-BC blocks and |$2^{251}$| 11-round Deoxys-BC-256-128 encryptions. [ABSTRACT FROM AUTHOR]

A redactable consortium blockchain (RCB) can build a trust layer for industrial internet of things (IIoT) so as to enable IIoT to resist certain powerful attacks resulting in improper block content. The redactability is particularly important for blockchains applied in IIoT with valuable or sensitive activities such as financial IoT or energy-trading IoT. Huang et al. proposed a threshold chameleon hash (TCH) scheme and then constructed an accountable-and-sanitizable chameleon signature scheme based on TCH. These two primitives are further used as fundamental modules to build an RCB, which empowers IIoT devices to operate the blockchain in a controllable way. However, our paper shows that Huang et al. 's RCB suffers from a security problem that weakens the crucial redactability. Specifically, we find out that if a transaction in a given block is legally redacted by all authorized sensors who collectively hold the private redacting key, anyone (without any private information) can further redact this redacted transaction and delete any transaction within this redacted block and, meanwhile, any sensor user with a private signing (not redacting) key can insert a forged transaction into this redacted block. We further address this threat by replacing the TCH module in Huang et al. 's RCB with our designed TCH. [ABSTRACT FROM AUTHOR]

With the help of a multi-signature scheme, we can reduce the cost of storage and bandwidth in case of many signers signing the same message. Therefore, multi-signature schemes can be used in bitcoin to reduce the size of a blockchain. In this paper, we propose a lattice-based multi-signature scheme with the following highlighted features. Our lattice-based multi-signature scheme supports signature compression and public key aggregation. The only existing lattice-based multi-signature scheme by Kansal and Dutta (Africacrypt, 2020) that supports both signature compression and public key aggregation has communication and storage cost |$\widetilde{\mathcal{O}}(n^2)$|⁠ , whereas our communication and storage cost is |$\mathcal{O}(n)$|⁠. Our multi-signature scheme is in the plain public key model where the special registration of the public key is not necessary and it is secure under the rogue key attack. Our multi-signature scheme is secure under the hardness of ring short integer solution problem in the random oracle model. [ABSTRACT FROM AUTHOR]

Many graph products have been applied to generate complex networks with striking properties observed in real-world systems. In this paper, we propose a simple generative model for simplicial networks by iteratively using edge corona product. We present a comprehensive analysis of the structural properties of the network model, including degree distribution, diameter, clustering coefficient, as well as distribution of clique sizes, obtaining explicit expressions for these relevant quantities, which agree with the behaviors found in diverse real networks. Moreover, we obtain exact expressions for all the eigenvalues and their associated multiplicities of the normalized Laplacian matrix, based on which we derive explicit formulas for mixing time, mean hitting time and the number of spanning trees. Thus, as previous models generated by other graph products, our model is also an exactly solvable one, whose structural properties can be analytically treated. More interestingly, the expressions for the spectra of our model are also exactly determined, which is sharp contrast to previous models whose spectra can only be given recursively at most. This advantage makes our model a good test bed and an ideal substrate network for studying dynamical processes, especially those closely related to the spectra of normalized Laplacian matrix, in order to uncover the influences of simplicial structure on these processes. [ABSTRACT FROM AUTHOR]

Reliability evaluation of interconnection networks is of significant importance to the design and maintenance of interconnection networks. The extra connectivity is an important parameter for the reliability evaluation of interconnection networks and is a generalization of the traditional connectivity. Let |$g\geq 0$| be an integer and |$G$| be a connected graph; the |$g$| -extra connectivity of |$G$| is the minimum cardinality of a set of vertices in |$G$|⁠ , if it exists, whose removal disconnects |$G$| and leaves every component with more than |$g$| vertices. Determining the |$g$| -extra connectivity is still an unsolved problem in many interconnection networks. Let |$n$|⁠ , |$k$| be positive integers. Let |$Q_{n,k}\, (1 \leq k \leq n-1)$| denote the |$(n, k)$| -enhanced hypercube. In this paper, we determine the |$g$| -extra connectivity of |$Q_{n,k}$| is |$(n+1)(g+1)-\frac{g(g+3)}{2}$| for |$0\leq g\leq n-k-1$|⁠ , |$4\leq k\leq n-5\,(n\geq 9)$|⁠. Some previous results in [Zhang, M. and Zhou, J. (2015) On g-extra connectivity of folded hypercubes. Theor. Comput. Sci. , 593, 146–153.] and [Sabir, E. Mamut, A. and Vumar, E. (2019) The extra connectivity of the enhanced hypercubes. Theor. Comput. Sci. , 799, 22–31.] are extended. [ABSTRACT FROM AUTHOR]

This paper aims to develop a novel deep learning concept to deal with video inpainting. Initially, motion tracking is performed, which is the process of determining motion vectors that describe the transformation from adjacent frames in a video sequence. Further, the regions or patches of each frame are categorized using the optimized recurrent neural network (RNN), in which the region is split into a smooth and structure region. It is performed using the texture feature called grey-level co-occurrence matrix. The filling of the smooth region is accomplished by replacing with the mean pixels of unmasked region, and the structure region is done by optimized patch matching approach based on scale-invariant feature transform (SIFT). The main objective optimized patch matching is based on the minimized Euclidean distance between the extracted SIFT features of the original patch and reference patch, and the certain patch is inpainted by the optimized patch. Here, the hybridization of two meta-heuristic algorithms like cuckoo search algorithm and multi-verse optimization (MVO) called Cuckoo Search-based MVO is used to optimize the RNN and patch matching. Finally, the experimental results verify the reliability of the proposed algorithm over existing algorithms. [ABSTRACT FROM AUTHOR]

As the backbone of the domain name system (DNS), DNS resolvers are essential to the Internet. Nowadays, the measurement of DNS resolvers, especially open DNS resolvers, has become a research focus. Previous research works show that DNS responses returned from some open DNS resolvers are not expected for clients and the Internet. We call these DNS responses 'unexpected DNS responses'. Research on unexpected DNS responses is beneficial to the research, usage and management of open DNS resolvers. This paper explores unexpected DNS responses returned from open DNS resolvers in terms of identification and classification to better understand the behaviours of open DNS resolvers. First, an identification method is proposed to identify all kinds of DNS responses from each section of DNS messages. Second, a classification method is proposed to classify unexpected DNS responses by their influences on clients and the Internet. Furthermore, an efficient identification and classification method is proposed to simplify the above process. Among about 9 million responding open DNS resolvers in the experiments on the IPv4 address space, about 40% return unexpected DNS responses. Experimental results show that the proposed methods can identify and classify all kinds of DNS responses returned from open DNS resolvers. [ABSTRACT FROM AUTHOR]

Millions of new domain names are registered every day, but a large proportion of them are malicious and usually discovered and blacklisted after the crime has been committed. In order to improve the security of domain name registration, this paper proposes a lightweight detection method based on the AdaBoost to identify malicious domain names, which focuses on proactively detecting malicious domain names by exploring the abnormal WHOIS records. The domain name registries and registrars can adopt the proposed method as the first layer of defense to identify malicious domains on the domain registration stage. Extensive experiments on a large-scale database demonstrate that the proposed approach achieves satisfactory results on various malicious domain names. [ABSTRACT FROM AUTHOR]

This paper investigates the security of counter mode encryption with authentication tag (COMET), one of the 32 second-round candidates in National Institute of Standards and Technology's lightweight cryptography standardization process, against differential cryptanalysis. CHAM-64/128 is a block cipher chosen as one of the underlying block ciphers in COMET for hardware-oriented applications, and a differential characteristic with a high probability for CHAM-64/128 is useful for forgery attacks on COMET. However, we find that the optimal |$\mathbf{39}$| -round differential characteristic for CHAM-64/128 proposed by Roh et al. , which is the longest differential characteristic of CHAM-64/128, is invalid. Then, we propose a new method of distinguishing an |$\mathbf{m}$| -bit block cipher from an |$\mathbf{m}$| -bit random permutation using a differential characteristic with a probability not higher than |$\mathbf{2^{-m}}$|⁠. Using our method, we use two |$\mathbf{39}$| -round differential characteristics with a probability of |$\mathbf{2^{-64}}$| for CHAM-64/128 to distinguish |$\mathbf{39}$| -round-reduced CHAM-64/128 from a |$\mathbf{64}$| -bit random permutation, respectively. Furthermore, we refine the probabilities of two differentials with the same input and output differential masks as the two |$\mathbf{39}$| -round differential characteristics, respectively. Finally, we present the first forgery attacks on COMET with the two differentials without using weak keys. Our forgery attacks follow the nonce-misuse scenario. It should be noticed that this attack does not invalidate the security claims of the designers. [ABSTRACT FROM AUTHOR]

In the era of multimedia technologies and rapidly growing multimedia contents, secured information transfer needs to be ensured through robust cryptography techniques. This paper presents a bio-key-generation scheme in the local environment to transfer multimedia content over vulnerable networks. To encrypt the images, one user has to ensure his/her authentication and further needs to provide a secret key for bio-key generation. Secret key aids to fetch biometric features for key formation and key expansion. Thus, the user receives an expanded key from the template database to perform encryption of a plain image. Once the cipher image is delivered to the network, the user has to repeat the same steps for decryption. This paper experiments the proposed bio-key-generation scheme with advanced encryption standard algorithm. This encryption strategy can also be used to encrypt confidential or personal images in various digital systems such as mobile phones, personal digital gadgets and so on. Various analyses ensure the robustness and the performance of the bio-key-based AES encryption scheme. Entropy, correlation coefficient, number of pixel change rate and unified average changing intensity are evaluated to ensure the effectiveness of the proposed method over other key-based encryption techniques. [ABSTRACT FROM AUTHOR]

Fault-tolerance is an important parameter to measure the performance of a network. However, most works only consider the fault of single vertex and ignore the structure-fault of a network. The generalized hypercube G (m r , m r − 1 , ... , m 1) is one key interconnection network with excellent topological properties. In this paper, we study the H -structure fault-tolerance of G (m r , m r − 1 , ... , m 1) network by studying its H -structure connectivity and H -substructure connectivity for H ∈ { K 1 , M , C 3 , C 4 , K 4 } ⁠. Since the generalized hypercube network can be used to construct some data center networks, such as BCube, HyperX, and FBFLY, the results in this paper can be applied not only to interconnection networks but also to data center networks. [ABSTRACT FROM AUTHOR]

Exploring the environment using multi-robot systems is a fundamental process that most automated applications depend on. This paper presents a hybrid decentralized task assignment approach based on Partially Observable Semi-Markov Decision Processes called HDec-POSMDPs, which are general models for multi-robot coordination and exploration problems in which robots can make their own decisions according to its local data with limited communication between the robot team. In this paper, a variety of multi-robot exploration algorithms and their comparison have been tackled. These algorithms, which have been taken into consideration, are dependent on different parameters. Collectively, there are five metrics maximize the total exploration percentage, minimize overall mission time, reduce the number of hops in the networked robots, reduce the energy consumed by each robot and minimize the number of turns in the path from the start pose cells to the target cells. Therefore, a team of identical mobile robots is used to perform coordination and exploration process in an unknown cell-based environment. The performance of the task depends on the strategy of coordination among the robots involved in the team. Therefore, the proposed approach is implemented, tested and evaluated in MRESim computer simulator, and its performance is compared with different coordinated exploration strategies for different environments and different team sizes. The experimental results demonstrate a good performance of the proposed approach compared to the four existing approaches. [ABSTRACT FROM AUTHOR]

Certificateless public key cryptography (CL-PKC) promises a practical resolution in establishing practical schemes, since it addresses two fundamental issues, namely the necessity of requiring certificate managements in traditional public key infrastructure (PKI) and the key escrow problem in identity-based (ID-based) setting concurrently. Signcryption is an important primitive that provides the goals of both encryption and signature schemes as it is more efficient than encrypting and signing messages consecutively. Since the concept of certificateless signcryption (CL-SC) scheme was put forth by Barbosa and Farshim in 2008, many schemes have been proposed where most of them are provable in the random oracle model (ROM) and only a few number of them are provable in the standard model. Very recently, Luo and Wan (Wireless Personal Communication, 2018) proposed a very efficient CL-SC scheme in the standard model. Furthermore, they claimed that their scheme is not only more efficient than the previously proposed schemes in the standard model, but also it is the only scheme which benefits from known session-specific temporary information security (KSSTIS). Therefore, this scheme would indeed be very practical. The contributions of this paper are 2-fold. First, in contrast to the claim made by Luo and Wan, we show that unfortunately Luo and Wan made a significant error in the construction of their proposed scheme. While their main intention is indeed interesting and useful, the failure of their construction has indeed left a gap in the research literature. Hence, the second contribution of this paper is to fill this gap by proposing a CL-SC scheme with KSSTIS, which is provably secure in the standard model. [ABSTRACT FROM AUTHOR]

The data science technologies of artificial intelligence (AI), Internet of Things (IoT), big data and behavioral/predictive analytics, and blockchain are poised to revolutionize government and create a new generation of GovTech start-ups. The impact from the 'smartification' of public services and the national infrastructure will be much more significant in comparison to any other sector given government's function and importance to every institution and individual. Potential GovTech systems include Chatbots and intelligent assistants for public engagement, Robo-advisors to support civil servants, real-time management of the national infrastructure using IoT and blockchain, automated compliance/regulation, public records securely stored in blockchain distributed ledgers, online judicial and dispute resolution systems, and laws/statutes encoded as blockchain smart contracts. Government is potentially the major 'client' and also 'public champion' for these new data technologies. This review paper uses our simple taxonomy of government services to provide an overview of data science automation being deployed by governments world-wide. The goal of this review paper is to encourage the Computer Science community to engage with government to develop these new systems to transform public services and support the work of civil servants. [ABSTRACT FROM AUTHOR]

Data wrangling (DW) is the subject of growing interest given its potential to improve data quality. DW applies interactive and iterative data profiling, cleaning, transformation, integration and visualization operations to improve the quality of data. Several domain-independent DW tools have been developed to tackle data quality issues across domains. Using generic DW tools requires a time-consuming and costly DW process, often involving advanced IT knowledge beyond the skills set of traffic analysts. In this paper, we propose a conceptual approach to DW for traffic data by creating a domain-specific language for specifying traffic DW tasks and an abstract set of wrangling operators that serve as the target conceptual construct for mapping domain-specific wrangling tasks. The conceptual approach discussed in this paper is tool-independent and platform agnostic and can be mapped into specific implementations of DW functions available in existing scripting languages and tools such as R, Python, Trifacta. Our aim is to enable a typical traffic analyst without expert Data Science knowledge to be able to perform basic DW tasks relevant to his domain. [ABSTRACT FROM AUTHOR]

Homomorphic signature (HS) is a novel primitive that allows an agency to carry out arbitrary (polynomial time) computation f on the signed data m → and accordingly gain a signature σ h for the computation result f (m →) with respect to f on behalf of the data owner (DO). However, since DO lacks control of the agency's behavior, receivers would believe that DO did authenticate the computation result even if the agency misbehaves and applies a function that the DO does not want. To address the problem above, in this paper we introduce a new primitive called authorized function homomorphic signature (AFHS). In AFHS, the agency has to obtain a confidence key sk f from DO in order to evaluate a function f on the data m → and to obtain a signature with which one can check whether the agency acts in accordance with DO's instructions. A black-box construction of AFHS based on HS is given in this paper, and we show that if the underlying primitives are secure, so is our construction under the given security model. Moreover, we provide a somewhat concrete construction that offers stronger security guarantee. [ABSTRACT FROM AUTHOR]

NewHope cryptosystem is one of the second-round submissions of the National Institute of Standards and Technology post-quantum cryptography standardization process, which is a suite of two key encapsulation mechanisms based on the ring-learning with errors (LWE) problem. It has received much attention from the research community due to its small key size and high efficiency. Recently, three key mismatch attacks are proposed against NewHope under the condition of key reuse. They do not solve the ring-LWE instance directly but exploit the leakage of secret information. As far as we know, the best result is given by Okada et al. ((2020) Improving Key Mismatch Attack on NewHope with Fewer Queries. In Proc. of the 25th Australasian Conf. on Information Security and Privacy , Perth, WA, Australia, November 30–December 2, pp. 505–524. Springer Cham, Switzerland), which recovers the whole secret with a success probability of |$97\%$| and |$233,803$| average queries. In this paper, we further improve the key mismatch attack of NewHope by reducing the average queries to |$106,577$| and raising the success probability to |$100\%$|⁠. Moreover, we analyze the key mismatch attack without key reuse for the first time and we propose a combinatorial attack against NewHope1024. The total complexity of the combinatorial attack is |$2^{253}$|⁠ , which is lower than the complexity of primal attack and the claimed security strength of NewHope1024. [ABSTRACT FROM AUTHOR]

Image thresholding is an important and efficient image segmentation technique, which is crucial and essential for image analysis and computer vision. In this paper, we proposed a new image thresholding method based on entropy and parzen window (PW) estimation. First, the probability of each gray-level distribution is approximate by using the PW estimation. Second, by combining the obtained probability information with entropic information of the foreground and background, a new objective function is created. At last, the ideal threshold value is obtained by optimizing the objective function. By comparing with some classical thresholding methods, such as inter class variance method (OTSU), minimum error thresholding method (MET), Kapur's entropy based method (KAPUR) and the recent methods that take spatial information into consideration (2D-D histogram method, GLLV histogram method and Gabor histogram method), the proposed method, experiment on 10 images (one synthetic image, four nondestructive testing images and five real-world images), presents a better performance on the accuracy, robustness and visual effect of segmentation. [ABSTRACT FROM AUTHOR]

Connectivity and diagnosability are two crucial subjects for a network's ability to tolerate and diagnose faulty processors. The |$r$| -component connectivity |$c\kappa _{r}(G)$| of a network |$G$| is the minimum number of vertices whose deletion results in a graph with at least |$r$| components. The |$r$| -component diagnosability |$ct_{r}(G)$| of a network |$G$| is the maximum number of faulty vertices that the system can guarantee to identify under the condition that there exist at least |$r$| fault-free components. This paper first establishes that the |$(r+1)$| -component connectivity of |$k$| -ary |$n$| -cube |$Q^{k}_{n}$| is |$c\kappa _{r+1}(Q^{k}_{n})=-\frac{1}{2}r^{2}+\Big(2n-\frac{1}{2}\Big)r+1$| for |$n\geq 2$|⁠ , |$k\geq 4$| and |$1\leq r\leq n$|⁠. In view of |$c\kappa _{r+1}(Q^{k}_{n})$|⁠ , we prove that the |$(r+1)$| -component diagnosabilities of |$k$| -ary |$n$| -cube |$Q^{k}_{n}$| under the PMC model and MM* model are |$ct_{r+1}(Q^{k}_{n})=-\frac{1}{2}r^{2}+\Big(2n-\frac{3}{2}\Big)r+2n$| for |$n\geq 4$|⁠ , |$k\geq 4$| and |$1\leq r\leq n-1$|⁠. [ABSTRACT FROM AUTHOR]

AEGIS is one of the authenticated encryption with associated data designs selected for the final portfolio of the CAESAR competition. It combines the AES round function and simple Boolean operations to update its large state and extract a keystream to achieve an excellent software performance. The AEGIS family consists of AEGIS-128, AEGIS-256 and AEGIS-128L, which use 5, 6 and 8 parallel AES round functions to process 128, 128 and 256 bits message block per step with slightly different output functions separately. Surprisingly, very few cryptanalytic results on AEGIS have been published so far. This paper presents the first guess-and-determine attacks on AEGIS family. Firstly, we propose a new observation on the structure of AEGIS that the relations of fixed variables remain in the outputs at consecutive steps under some conditions on the AND operations, and the vectorial bitwise AND operation is biased, which is able to derive the additional variables added directly. Secondly, we add several techniques, such as divide and conquer on byte-based columns, reduction by meet in the middle and simplification through constraints on variables, for each AEGIS member. Finally, we conduct guess-and-determine attacks on AEGIS-128, AEGIS-256 and AEGIS-128L and result in a complexity of |$2^{309}$|⁠ , |$2^{437}$| and |$2^{384}$| to |$2^{416}$|⁠ , respectively. Although neither attack threatens the practical security of AEGIS, it has great significance to evaluate the resistance of such structure compared with their large internal state exploited of 640, 768 and 1024 bits. It is also the first internal state recovery attack on AEGIS without nonce reusing, while only distinguishing attacks on AEGIS exist up to now. [ABSTRACT FROM AUTHOR]

This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method. [ABSTRACT FROM AUTHOR]

With industry 4.0, data-based approaches are in vogue. However, extracting the essential features is not a trivial task and greatly influences the final result. There is also a need for specialized system knowledge to monitor the environment and diagnose faults. In this context, the diagnosis of faults is significant, for example, in a vehicle fleet monitoring system, since it is possible to diagnose faults even before the customer is aware of the fault, minimizing the maintenance costs of the modules. In this paper, several models using machine learning (ML) techniques were applied and analyzed during the fault diagnosis process in vehicle fleet tracking modules. Two approaches were proposed: 'With Knowledge' and 'Without Knowledge', to explore the dataset using ML techniques to generate classifiers that can assist in the fault diagnosis process. The approach 'With Knowledge' performs the feature extraction manually, using the ML techniques: random forest, naive Bayes, support vector machine and Multi Layer Perceptron; on the other hand, the approach 'Without Knowledge' performs an automatic feature extraction, through a convolutional neural network. The results showed that the proposed approaches are promising. The best models with manual feature extraction obtained a precision of 99.76% and 99.68% for detection and detection and isolation of faults, respectively, in the provided dataset. The best models performing an automatic feature extraction obtained, respectively, 88.43% and 54.98% for detection and detection and isolation of failures. [ABSTRACT FROM AUTHOR]

Authenticated key exchange protocols are quite important primitives for practical applications, since they enable two parties to generate a shared high entropy secret key. In this paper we mainly focus on the authenticated key exchange (AKE) against auxiliary input leakage. As the major contribution of this work, we present a generic framework for the construction of AKE protocols that are secure against auxiliary input leakage. An instantiation based on the generalized decisional Diffie-Hellman (GDDH) assumption in the standard model is also given to demonstrate the feasibility of our proposed framework. We also give a comparison among the existing leakage resilient AKE protocols with auxiliary inputs. [ABSTRACT FROM AUTHOR]

The ability of Fog computing to admit and process huge volumes of heterogeneous data is the catalyst for the fast expansion of Internet of things (IoT). The critical challenge is secure and differentiated access to the data, given limited computation capability and trustworthiness in typical IoT devices and Fog servers, respectively. This paper designs and develops a new approach for secure, efficient and differentiated data access. Secret sharing is decoupled to allow the Fog servers to assist the IoT devices with attribute-based encryption of data while preventing the Fog servers from tampering with the data and the access structure. The proposed encryption supports direct revocation and can be decoupled among multiple Fog servers for acceleration. Based on the decisional |$q$| -parallel bilinear Diffie–Hellman exponent assumption, we propose a new extended |$q$| -parallel bilinear Diffie–Hellman exponent (E |$q$| -PBDHE) assumption and prove that the proposed approach provides 'indistinguishably chosen-plaintext attacks secure' data access for legitimate data subscribers. As numerically and experimentally verified, the proposed approach is able to reduce the encryption time by 20% at the IoT devices and by 50% at the Fog network using parallel computing as compared to the state of the art. [ABSTRACT FROM AUTHOR]

The Learning Parity with Noise (LPN) problem represents the average-case analogue of the NP-Complete problem "decoding linear codes", and it has been extensively studied in learning theory, coding theory and cryptography with applications to quantum-resistant cryptographic schemes. However, LPN also suffers from large public key size which is the common drawback that hinders code-based cryptography from being practical. In this paper, we study a sparse variant of LPN whose public matrix consists of sparse vectors instead of following uniform distribution. We show a win–win argument that at least one of the following assumption is true: (i) either the hardness of sparse LPN is implied by that of the standard LPN under the same noise rate; (ii) or there exists new black-box constructions of public-key encryption schemes and oblivious transfer protocols from standard LPN. Since the second assumption relies on the infeasible noise regimes for LPN-based public-key cryptography, we believe that the first assumption is more likely to hold, i.e. sparse LPN is as hard as standard LPN. Finally, we give a (heuristic) method to further compress the sparse public matrix by evaluating pseudorandom functions with keys made public, whose security again resorts to the aforementioned win–win technique. [ABSTRACT FROM AUTHOR]

Twitter has become an open space for the users' interactions and discussions on diverse trending topics. One of the issues raised on social media platforms is the misunderstanding of 'freedom of speech', which in turn, leads us to a new social and behavioral attack: cyberbullying. Cyberbully affects both individuals and societies. Despite tough sanctions globally and locally, cyberbullying is still a serious issue, which needs further consideration. Thus, this research aims to address this issue by proposing a framework, based on sentiment analysis, to detect cyberbullying in the tweets stream. The proposed framework in this paper extracts the tweets from Twitter. Then, the preprocessing through tweets tokenization was applied to remove noise from tweets and also symbols and phrases such as http, emoji faces, hash tag symbols, mention symbols and retweet. After data tokenization, the proposed system classifies the tweets, based on extracted keywords from both experts and potential victims, using deep-learning classification algorithm with 70% of dataset samples used for the training purpose, and 30% of the dataset samples used for the testing purpose. The experimental results show the ability of proposed methodology to detect cyberbullying effectively with accuracy 81%. [ABSTRACT FROM AUTHOR]

Accurate diagnosis of lesions bears the highest significance in the early detection of diabetic retinopathy (DR). In this paper, the combination of intelligent methods is developed for segmenting the abnormalities like 'hard exudates, hemorrhages, microaneurysm and soft exudates' to detect the DR. The proposed model involves seven main steps: (a) image pre-processing, (b) optic disk removal (c) blood vessel removal, (d) segmentation of abnormalities, (e) feature extraction, (f) optimal feature selection and (f) classification. The pre-processing of the input retinal fundus image is performed by two operations like contrast enhancement by histogram equalization and filtering by average filtering. For the segmentation of abnormalities, the same Circular Hough Transform followed by Top-hat filtering and Gabor filtering is used. Next, the entropy-scale-invariant feature transform (SIFT), grey level co-occurrence matrices and color morphological features are extracted in feature extraction. The optimally selected features are subjected to the classification part, which uses a modified deep learning algorithm called optimized recurrent neural network (RNN). As the main novelty, the optimal feature selection and optimized RNN depends on an improved meta-heuristic algorithm called fitness oriented improved Jaya algorithm. Hence, the beneficial part of the optimization algorithm improves the feature selection and classification. [ABSTRACT FROM AUTHOR]

The (conditional) matching preclusion number of a graph is the minimum number of edges whose deletion leaves the resulting graph (with no isolated vertices) that has neither perfect matchings nor almost perfect matchings. The (conditional) strong matching preclusion number of a graph is the minimum number of vertices and edges whose deletion makes the resulting graph (with no isolated vertices) without perfect matching or almost perfect matching. The enhanced hypercube |$Q_{n,k}$|   |$(1\leq k\leq n-1)$| is an extension of hypercube. In this paper, we prove that the matching preclusion number of |$Q_{n,k}$| is |$n+1$|   |$(1\leq k\leq n-1)$|⁠ , the strong matching preclusion number of |$Q_{n,k}$| is |$n+1$|   |$(2\leq k\leq n-1)$|⁠ , the conditional matching preclusion number of |$Q_{n,n-1}$| is |$2n-1$|⁠ , the conditional matching preclusion number of |$Q_{n,k}$| is |$2n$|   |$(1\leq k\leq n-2)$| and the conditional strong matching preclusion number of |$Q_{n,n-2}$| is |$2n-3$|   |$(n\geq 4)$|⁠. [ABSTRACT FROM AUTHOR]

In this paper, we further explore the properties of the verifiable random functions in both a black-box and a non-black-box manner. The results are mainly following two parts: |$\bullet $|   Black-Box Barrier: It is set up for an impossibility result of black-box reduction from verifiable random functions to injective one-way functions and indistinguishability obfuscators, where the verifiable random functions are suggested to be domain-invariant (i.e. the support of the distribution of keys and the domain of the evaluation space are independent of the underlying building blocks). Our result illustrates how the non-domain-invariant constructions circumvent the black-box barriers for constructing verifiable random functions and sheds light on why it is so difficult to give a domain-invariant instantiation. |$\bullet $|   Non-Black-Box Construction: On the other hand, the verifiable unpredictable functions are constructed from a given primitive by a non-black-box technique called the hitting-set generator. To show it's a somewhat useful technique for constructing the verifiable unpredictable functions, we further derive a limitation of the black-box barrier by proving the barrier still holds between the given primitive and verifiable unpredictable functions. Our results not only analyse the properties of verifiable random functions theoretically, but also reveal the limitation of indistinguishability obfuscators in a black-box manner, and show the advantages by adopting non-black-box techniques. [ABSTRACT FROM AUTHOR]

Medical data clustering is an important part of medical decision systems as it refines highly sensitive information from the huge medical datasets. Medical data clustering includes processes, like determine random clusters, set data into specified clusters and handle data clusters dynamically. Hence, handling of medical data streams and clustering remains a challenging issue. This paper proposes a technique, namely Rider-based sunflower optimization (RSFO) for medical data clustering. Initially, the significant features are selected using the Tversky index with holoentropy that is established from the input data. The holo-entropy is utilized to analyze the relationship between the attributes and features. Here, the clustering is done by a Black Hole Entropic Fuzzy Clustering (BHEFC) algorithm, where the optimal cluster centroids are selected by the proposed RSFO algorithm. The proposed RSFO is designed by incorporating the Rider optimization algorithm (ROA) and sunflower optimization (SFO). The effectiveness of the proposed BHEFC+RSFO algorithm is analyzed by the Dermatology Data Set, and the proposed method has the maximal accuracy of 94.480%, Jaccard coefficient of 94.224% and Rand coefficient of 91.307%, respectively. [ABSTRACT FROM AUTHOR]

Differential privacy has made a significant progress in numerical data preserving. Compared with numerical data, non-numerical data (e.g. entity object) are also widely applied in intelligent processing tasks. But non-numerical data may reveal more user's privacy. Recently, researchers attempt to take advantage of the exponential mechanism of differential privacy to solve this challenge. Nonetheless, exponential mechanism has a drawback in correlated data protection, which can not achieve expected privacy degree. To remedy this issue, in this paper, an effective correlated non-numerical data release mechanism is proposed by defining the notion of Correlation-Indistinguishability and designing a correlated exponential mechanism to realize Correlation-Indistinguishability in practice. Inspired by the concept of indistinguishability, Correlation-Indistinguishability can guarantee the correlations of the probability distributions between the output distribution and original data the same to an adversary. In addition, we would rather let two Gaussian white samples pass through a designed filter, to realize the definition of Correlation-Indistinguishability, than using independent exponential variables. Experimental evaluation demonstrates that our mechanism outperforms current schemes in terms of security and utility for frequent items mining. [ABSTRACT FROM AUTHOR]

Cognitive Radio network (CRN) depends on opportunistic spectrum access and spectrum sensing for improving the wireless networks' spectrum efficiency. Since throughput maximization can result in high-energy consumption, the spectrum sensing technique should address the energy-throughput trade-off. The spectrum sensing time has to be determined by the considering the residual battery energies of each secondary user (SU). The primary user (PU) interference degrades the throughput of the entire network. Hence, the transmit power level should be determined by considering the PU interference and SU battery energy. This paper proposes the multi-objective brain storm optimization (MBSO) algorithm for handling energy-throughput trade-off in CRN. In this work, the sensing time is adaptively determined based on the residual battery energy of SUs, and the transmit power is determined based on the energy level of the PU signal and the residual battery energy of the SUs. A multi-objective optimization problem is formulated in order to maximize the throughput and minimize the packet error rate (PER) and is solved by applying the MBSO algorithm. Experimental results show that MBSO attains improved throughput, higher residual energy with lesser PER. The spectrum sensing performance is enhanced with higher probability of detection. [ABSTRACT FROM AUTHOR]