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The intrusion detection system is responsible for revealing different intrusion activities, including the denial of service, man-in-middle, Mirai, Scan, and other types of intrusion activities. It is used in many applications, including the smart home Internet of Things networks, where security risks threaten the privacy of individuals. In this context, many works were proposed for detecting and classifying the different types of attacks. However, many challenges are identified for this type of problem, such as the large amount of data available, the imbalanced nature of the data, and the quality of detection and classification outcomes. This paper aims to address these challenges by proposing an approach that considers a metaheuristic-based random weight neural network to detect intrusion activities and classify the different types and subtypes of activities. The following points summarize the contribution of this paper. First, the automatic tuning of the neural network parameters where the weights, biases, regularization value, the number of neurons, and the type of activation function are optimized by different metaheuristic algorithms to produce high-quality results. Second, the proposed approach adopts a clustering with reduction technique to tackle the challenge of processing large volumes of data. Third, oversampling the dataset is also embedded in the proposed approach to avoid a biased classification of the majority class. The experiments are conducted based on a large dataset with more than half a million instances. The results show that the proposed approach outperforms the other classification approaches in geometric mean (G-Mean) and has promising results. [ABSTRACT FROM AUTHOR]

Topology control is a significant research problem when it comes to designing energy-efficient wireless sensor networks (WSNs). It is a metric for evaluating network service quality. To ensure the quality of network services, it is critical to provide energy-efficient sensor node scheduling in order to extend the network's lifespan. In this paper, we propose an energy-aware scheduling protocol (ESP)-based hybrid metaheuristic technique for minimizing the amount of energy consumption and ensuring sufficient coverage for the monitored area while maximizing the network lifespan for WSNs. The ESP consists of two phases: distributed clustering and sensor node scheduling. The clustering phase implements the DBSCAN clustering algorithm with slide modification to cluster the sensor nodes in the area of interest into clusters of nodes, and the efficient periodic cluster head election approach is proposed based on criteria such as remaining energy, number of neighbors, and distance for each node in the cluster. This clustering is only performed at the beginning to group the nodes into clusters. Then, only the cluster head election will be done every period. In the scheduling phase, we model the scheduling optimization problem using three objectives: reducing the number of uncovered zones, minimizing the number of active sensors, and selecting the active sensor nodes with the maximum remaining energy. This scheduling optimization model is solved using a hybrid metaheuristic algorithm. When the genetic algorithm's operators were added to the regular Cuckoo algorithm, the balance between exploration and exploitation abilities was improved even more. The algorithms were able to search a larger area. Extensive simulation experiments were conducted using the OMNeT++ network simulator. The results show that the proposed ESP protocol introduces better performance in terms of coverage ratio, active sensor ratio, energy consumption, network lifespan, and execution time compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Software is playing a growing role in many safety-critical applications, and software systems dependability is a major concern. Predicting faulty modules of software before the testing phase is one method for enhancing software reliability. The ability to predict and identify the faulty modules of software can lower software testing costs. Machine learning algorithms can be used to solve software fault prediction problem. Identifying the faulty modules of software with the maximum accuracy, precision, and performance are the main objectives of this study. A hybrid method combining the autoencoder and the K-means algorithm is utilized in this paper to develop a software fault predictor. The autoencoder algorithm, as a preprocessor, is used to select the effective attributes of the training dataset and consequently to reduce its size. Using an autoencoder with the K-means clustering method results in lower clustering error and time. Tests conducted on the standard NASA PROMIS data sets demonstrate that by removing the inefficient elements from the training data set, the proposed fault predictor has increased accuracy (96%) and precision (93%). The recall criteria provided by the proposed method is about 87%. Also, reducing the time necessary to create the software fault predictor is the other merit of this study. [ABSTRACT FROM AUTHOR]

One of the most important composite indicators (CIs) to assess the development of countries or regions is the human development index (HDI) which is used by the United Nations (UN) to rank countries. HDI has three dimensions including healthy life, population education, and standard of living. A total of four different sub-indicators are defined for these three dimensions. The UN evaluates and ranks all countries using a simple arithmetic or geometric average of the sub-indicators and then categorizes the countries into four different groups based on their HDI scores. To measure the HDI, the benefit-of-the-doubt (BOD) model is used by researchers instead of the geometric mean. The conventional BOD model has some main drawbacks. The first is not accounting for data uncertainty, and the second is evaluating countries using different weights for the same sub-indicators. Furthermore, BOD model is not capable of predicting countries' future HDI scores. To overcome these deficiencies, this paper proposes a common weight fuzzy BOD (CWFBOD) model to measure the HDI scores. First, to take into account the uncertainty, data are considered fuzzy numbers, and a fuzzy BOD model (FBOD) is introduced. Then, to find a set of common weights for the three dimensions of HDI, the proposed FBOD model is transformed into a multiple-objective CWFBOD model. To convert and solve the multiple objective CWFBOD model to a single objective model, a fuzzy theory approach is used. In addition, of predicting the future HDI scores of countries, an artificial neural network (ANN) is designed and trained, where the original data on sub-indicators health, education, and income are considered as the features, and the HDI scores generated by CWBOD are assumed as the target of ANN. Finally, this study applies the fuzzy C-Means clustering technique to cluster all countries into four different clusters based on the HDI scores generated by FBOD and CWFBOD models. To illustrate the ability of the proposed methodology, the HDI scores of 190 countries during the period of 2015–2021 have been estimated and predicted. The results show that the proposed integrated methodology can be effectively used to estimate and predict the HDI scores as well as to cluster countries. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

Recently, the analysis of heterogeneous networks has become more popular due to the growing number of social networks. These networks are capable of covering a variety of nodes and edges. The members of these networks usually have metadata whose analysis can lead to the discovery of knowledge. One way to analyze such data is clustered where high-quality clustering requires effective similarity calculation. Most of the existing clustering methods do not pay attention to the use of metadata or the characteristics of network members. On the other hand, they are only able to process small and medium-sized networks due to the amount of memory and execution speed. This paper presents a hybrid approach for heterogeneous network clustering to overcome these problems. The structural similarity in this approach is calculated by the graph embedding method, which we call learning-based. Attribute similarity is calculated by a scoring method that we call similarity-based. In the experimental study, we compared the proposed method with collaborative approaches based on similarity on the real-world networks. The experimental findings demonstrate the superiority of the proposed method in terms of entropy, memory consumption, execution time, and density in certain cases. [ABSTRACT FROM AUTHOR]

Design of effective algorithm for reliable and energy optimized secure routing protocol (SRP) for wireless sensor networks (WSNs) is a demanding design issue now. To handle this problem, we propose a trust and encryption-based SRP based on trust modelling with intrusion detection, elliptic curve cryptography (ECC), clustering, fuzzy rules and ant colony optimization (ACO)-oriented SRP for WSN routing. In this paper, an extended convolutional neural networks with Schrodinger equation and particle swarm optimization is proposed for developing and intrusion detection-based trust modelling. Moreover, a new node authentication scheme and an encryption-based secure routing protocol are also proposed in this work for increasing the security. This proposed secure protocol known as trust and ECC encryption-based ACO-SRP (TECC-ACO-SRP) performs authentication and trust analysis on the nodes using intrusion detection, and then, the data are communicated after data encryption using ECC encryption technique. This proposed system combines dominant set clustering with fuzzy rules to make clusters with similar type of nodes as members and then selects cluster heads (CHs) for every cluster. This SRP ensures improved security, reduced delay and energy usage with higher packet delivery ratio than other existing SRPs. [ABSTRACT FROM AUTHOR]