Abstract Software effort estimation is a vital process in the software industry for successfully administering 5Ds of the software development life cycle (SDLC). The 5Ds stand for demand, development, direction, deployment, and designated cost of the software. Software development effort estimation (SDEE) is an effort prediction mechanism to calculate the effort for the development of the software product in order to minimize the challenges in the software field. Academics and practitioners are striving to identify which machine learning estimation technique yields more accurate results based on evaluation metrics, datasets, and other pertinent aspects. The feature selection techniques impact accuracy by selecting the main and relevant features in the dataset and eliminating the redundant and irrelevant features in the dataset. To achieve accurate estimations, the paper utilizes feature selection algorithms, along with various machine learning techniques, which predict the desired effort and the performance of the model has been measured in terms of prediction accuracy, value, relative error, and mean absolute error. The datasets China and Maxwell are trained with the relevant features by applying feature selection algorithms, and estimation techniques are applied to predict the effort. The performance is compared with the regression models and feature selection techniques utilized by many authors previously. The result of the proposed methodology significantly gives the best performance with the combination of feature selection and estimation models than all regression models when applied alone, to both datasets. From the results, it is perceptible that random forest is performing well with the feature selection techniques and obtains the highest prediction accuracy of 99.33% with the China and 89.47% with the Maxwell datasets.

Summary Cloud computing is considered as the most fabulous paradigm to accommodate various kinds of user information. However, the privacy conflicts integrated with computational complexities need more hybridized resource efficiency for hassle‐free accession. For this purpose, this article presents a cloud assisted, secured and privacy preserved protocol based on the elliptical curve cryptography (ECC) and blockchain consortium. Blockchain provides an innovative approach for storing information, establishing trust, and various other transactions in an open platform. There exists no single technology as a panacea to obtain optimum privacy and security in the complex cloud environment that needs several desired characteristics. Hence, an elite integration of multiple cryptographic methods by carefully analyzing the potential harms and pitfalls has to be framed to balance the trade‐off between privacy and security. The proposed technique highlights the user privacy preservation through guaranteeing that user information is safeguarded from unauthorized use or access. In accordance with that the present study enrolled the advantages of the ECC algorithm, vectorization, and blockchain methods to rule out the limitations of state of art methods. This article attempts to provide the most required privacy with optimum key generation, encryption, and decryption time. The present study has obtained optimal outcomes and the maximum percentage reduction w.r.t the existing method in key generation time, encryption time, and decryption time is given as 14.89%, 16.67%, and 12.5%, respectively.

Summary The necessity of cyber‐security has obtained immense importance in day‐to‐day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber‐physical system) model using a dual mutation‐based genetic algorithm, with feature classification through Ada‐Boost and SVM classifier is proposed in this paper. Dual‐mutation based genetic‐algorithm overcomes the issues of conventional techniques including convergence issues and local fine‐tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating‐point representation, every chromosome vector has been coded as a floating‐point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual‐mutation algorithm that handles local fine‐tuned features. The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov‐resampling process. Followed by this, a hybrid approach of ESVM (enhanced support‐vector machine) algorithm with Ada‐Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy‐factor, F1‐score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1‐score (99%) rates, and less execution time (15.33 s).

In the area where privacy is of greater concern, federated learning,a distributed machine learning strategy for preserving privacy,is widely employed in several privacy concern applications. In the meantime, neural architectures became familiar with deep learning approaches for automatic tuning of the architecture of deep neural networks (DNN). While searching with neural architecture and federated learning has experienced several challenges, optimized neural architecture research in federated learning is extensively on demand. DNN faces numerous issues while training such user privacy and ensuring the integrity of the aggregated results obtained from a server. To provide solutions for the above-mentioned issues, enormous federated learning techniques worked towards preserving privacy and were applied in different situations. Still, it is an open challenge that enables users to verify if the cloud server functions appropriately while ensuring users’ privacy while training. Federated Learning Method is a new way to improve the accuracy and precision, since the previous approach failed to opt the solutions. Here, Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL)is proposed to ensure the confidentiality of users’ local gradients while performing federated learning. The parameters such as classification accuracy, running time, Communication overhead, Computation overhead, and transaction speed are considered. The values obtained for these parameters are compared against three standard methods, namely Biparing Method (BM) Homomorphic Cryptosystem (HC), and Multiple Authorities with Attribute-Based Signature scheme (MA-ABS)against proposed Elliptical Curve Cryptography with Blockchain-based Federated Learning (ECC-BFL). As a result, the proposed ECC-BFL achieved 95% of classification accuracy, 65 sec of running time, 76% of communication overhead, 63% of computation overhead, and 92% of transaction speed.

Web Applications are becoming more sophisticated to cater the ever-growing demand of data processing and computing. Fast technological advancements in web engineering not only facilitate data intensive and high-performance computing, but also raise serious concerns on security. Cyber threats are also ramping up at the equal pace and attackers are now more organised and equipped with high-end servers. The Data over Web needs to be more authenticated and reliable. Data Provenance-aware methods are capable of identification of data breaches and manipulation through various attacks. They analyse underlying data for the potential threats to ensure protection against various attacks. Cyber Security Practitioners are witnessing severe issues in securing the Web Data and applications as the security risks are growing rapidly due to the sudden eruption in internet usage due to the pandemic in the last few years. People and organisations are relying more on Internet and web applications than ever before. The efforts for securing the web data on such a massive scale is premature to counter the ever-evolving attack attempts. Nature-inspired Cyber Security (NICS) facilitates the development and implementation of robust defensive mechanisms which are more adaptive and highly tolerant to online malicious programs. These methods are also capable of dealing with the common algorithmic issues like incompleteness and uncertainty of information and to provide a high-level security mechanism by effectively implementing the bio-inspired methodologies like deception, and camouflage etc. This article will attempt to explore the effectiveness of NICS in web data and application security to provide smart security methods.

The sixth-generation (6G) communication networks being highly data-intensive and enabled by the Internet of Everything (IoE) are envisaged to find applications in various domains including smart healthcare, smart industry, and gaming. In this article, a novel hybrid 6G-based cybertwin cooperative architecture is studied and an analytical framework for the secrecy outage analysis is presented. The base station (BS) considered utilizes nonorthogonal-multiple-access for content request transmission to the cybertwin host and the server with a direct communication link present between the BS and the server. A wireless link experiencing Nakagami-$m$fading is considered which is further assisted in the communication network by a wired link which is a power line communication link and experiences Rayleigh fading, for the communication between the cybertwin host and the server. Secrecy error probability expressions are derived for the cybertwin host and the server for the scenario when both the wireless and the wired links are available for communication and only the wireless link is available for communication. Furthermore, the presented analytical results are corroborated with simulation results which demonstrate the optimality of operating the wireless links at lower signal-to-noise ratio (SNR) values. It is observed that for the lower values of Nakagami fading parameter, better secrecy performance is observed with the usage of wired link along with the wireless link, and also the secrecy performance of the cybertwin host and the server is dependent on the system parameters like the fading parameter and the average SNR.

Android malware has become the topmost threat for ubiquitous and useful Android eco-system. Multiple solutions leveraging big data and machine learning capabilities to detect android malware are being constantly developed. Too often, many of these solutions are either limited to the research output or remain isolated and unable to reach to end-users or malware researchers. In this paper, we propose, PACE, a unified solution to offer open and easy implementation access to several machine learning-based Android malware detection techniques that make most of the research in this domain reproducible. The benefits of PACE are offered using three interfaces i.e. through REST API, Web Interface and ADB interface. Multiple interfaces enable users with different expertise such as IT administrator, security practitioners, malware researcher, etc. to avail its offered services. A community-accepted dataset is used for testing of all the techniques to provide a better comparison of performance. A prototype of the proposed platform is introduced and our vision is that it will help malware analysts to tackle challenges and reduce the amount of manual work.

In service to the state of the art, advances are required toward redesigning the framework over which web applications are built.The semantic web lies at the intersection of web and machine understandable meaningful data, turning it into intelligent 'web of data'.The key requirement with any intelligent system has been to find a concrete knowledge representation that can make the inferences within time and space constraints; that is, reasoning effectively and efficiently within the resource constraints posed to the problem at one hand and with insufficient data as well as incomplete knowledge on the other hand.Various Knowledge representation schemes have been proposed in the literature, each having its limitation over the others.Ontology is the key component for semantic web engineering.Ontologies are conceptual knowledge bases providing a systematic and taxonomical description of the concepts and instances under consideration.Conceptual clarity in the computational representation of a concept is vital for holistic thinking and knowledge engineering.In order to meet the needs of an application/enterprise, knowledge should be presented taking care of all possible perspectives; and represented in a hierarchical structure with differing levels of granularity.This paper discusses about bringing all the manifestations of an ontological