ABSTRACT The increasing complexity of software development processes has heightened the need for robust security measures. Although technical safeguards are essential, the role of human factors in securing software development remains underexplored. This paper presents a novel approach that integrates people's maturity with a fuzzy analytic hierarchy process (Fuzzy‐AHP) decision‐making framework to enhance the security in software development. The framework provides a systematic method for evaluating and prioritizing human factors that influence an organization's security posture, such as team‐expertized communication and adherence to security protocols. Using the decision‐making model allows the project managers and stakeholders to determine the appropriate areas for improvement and develop the right strategies and actions to nurture a secure and mature development culture. The paper identifies 24 human success factors (HSFs) and human security vulnerabilities (HSVs) and 38 practices for addressing these HSFs and HSVs through systematic literature review (SLR) and empirical survey. Furthermore, we discuss the local and global ranks of each HSF and HSV practice and categorize the identified practices into nine categories to determine the ranks and weight of each category. Based on collected data, Fuzzy‐AHP prioritized these practices; the category “C4: Skill development and stakeholder engagement” is ranked highest at rank‐1 and possesses the most significant weight of 0.12435. Similarly, the highest global weight is 0.051506, and the global ranked (rank‐1) HSF and HSV practice is “P15: Hands‐on practice and stakeholder communication.” The proposed approach complements existing technical methods by addressing the human element of security, making it adaptable to diverse organizational environments. Through this integration of people maturity and Fuzzy‐AHP, the paper contributes a new dimension to securing software development, emphasizing the critical role of human factors in achieving comprehensive security.

ABSTRACT Information System Security (ISS) is the primary economic lever for the global economy. It is the cornerstone for value generation, and its absence undeniably affects technology, people, and finances. The emergence of the worldwide information society has introduced fresh economic and legal challenges attributed to the surge in Internet utilization and advancements in the digital economy. Ensuring the security of advancements within information systems has emerged as a primary concern in propelling the evolution of information processes within the software development industry. This study aims to develop and propose a Fuzzy Analytic Hierarchy Process (Fuzzy‐AHP) framework to enhance decision‐making for software maintenance and deployment in ISS. This framework aims to provide a systematic, flexible method for evaluating and prioritizing multiple conflicting criteria under conditions of uncertainty. The study initially adopts an empirical survey to identify software security maintenance and deployment risks and their practices for ISS organizations. Then adopts the Fuzzy‐AHP method to handle the imprecision of expert judgments and organizes decision‐making into a hierarchical structure. The framework is applied to evaluate key criteria related to software maintenance and deployment, including security risks, system performance, operational costs, and compliance requirements. Data from 50 ISS experts were collected and used to validate the framework. The paper identifies 52 security risks in maintenance and deployment (SRMD) processes in ISS and also identified 139 best practices for ensuring security, including regular updates, patch management, and adherence to industry‐standard security protocols. The Fuzzy‐AHP framework effectively structured the decision‐making process by prioritizing criteria and sub‐criteria. The results demonstrated that the framework helps mitigate the subjective biases in expert judgment and provides a more balanced assessment of maintenance and deployment strategies. Prioritizing security risks and compliance emerged as key factors in the decision‐making process. The proposed Fuzzy‐AHP framework provides an innovative and adaptable solution for optimizing ISS organizations' software maintenance and deployment decisions. It addresses the complexity and uncertainty involved in such decisions, offering a transparent and structured approach that improves the accuracy and reliability of outcomes. Future research should focus on empirical validation of the framework in real‐world case studies and expand its application to other industries with similar decision‐making needs.

During the last few years, due to the COVID-19 pandemic, there has been a significant development of eHealth monitoring systems. However, most of the systems to date have been developed specifically for patient monitoring by nurses, physicians, and specialists. To keep attendants informed about the health status of their patients in the hospitals, we are developing an edge-enabled companion-side eHealth monitoring system for smart hospitals based on the Internet of Things (IoT). In most existing edge-enabled eHealth monitoring systems, the utilized edge devices have limited storage capacity and energy resources, resulting in network outages and loss of data packets. Although these challenges lead to life-threatening problems, much less attention has been paid to these shortcomings in the previous work. Therefore, we first deploy edge-enabled health evaluators to receive the medical signals from the biosensors in each unit of time, and evaluate the health status of the patients. Then, each evaluator generates and stores only one health number instead of caching the data from all sensor nodes, which increases the storage efficiency. We also employ a wireless mobile charger (WMC) to charge the batteries of the health evaluators. Unlike previous work, the different attributes of the WMC are individually optimized to achieve different objectives, resulting in improved network performance and efficiency of the WMC. Experimental results show that the performance of the proposed system is better than other solutions by 99% in cloud storage optimization, 83% in edge storage optimization, 23% in end-to-end delay, and 10% in energy efficiency of edge devices.

Abstract Technological advancement makes the world a global village. Security is an evergreen and everlasting area, because of the continuous threat from Hackers and Crackers. The immense use of software systems has modernized human society in every aspect. Thus, it is crucial to devise new processes, techniques, and tools to support teams in the development of secure code from the early stages of the software development process, while potentially reducing the costs and shortening the time to market. Considering the significance of software security, it is important to consider the security practices from the early phase of the software development life cycle (SDLC), that is, requirements engineering (RE). Hence, this study aims to identify and categorize RE practices important to apply for secure software development (SSD) in a geographically distributed development environment. To study the RE practices concerning SSD, we conducted a questionnaire survey with industrial experts in the global software development (GSD) context. Furthermore, the interpretive structure modeling (ISM) approach was applied to evaluate the relationship between the RE security practice core categories. This paper identifies 70 practices and classifies them into 11 fundamental dimensions (categories) to assist GSD organizations in specifying the requirements for SSD. The ISM results show that the “Awareness of Secure Requirement Engineering (SRE)” category has the most decisive influence on the other 10 core categories of the identified RE security practices. With the help of empirical evidence and the ISM approach, this work attempts to identify potential security practices and to give a set of secure RE practices that can be used to improve the security of the software development process.

Security issues are threatened in various types of networks, especially in the Internet of Things (IoT) environment that requires early detection. IoT is the network of real-time devices like home automation systems and can be controlled by open-source android devices, which can be an open ground for attackers. Attackers can access the network credentials, initiate a different kind of security breach, and compromises network control. Therefore, timely detecting the increasing number of sophisticated malware attacks is the challenge to ensure the credibility of network protection. In this regard, we have developed a new malware detection framework, Deep Squeezed-Boosted and Ensemble Learning (DSBEL), comprised of novel Squeezed-Boosted Boundary-Region Split-Transform-Merge (SB-BR-STM) CNN and ensemble learning. The proposed STM block employs multi-path dilated convolutional, Boundary, and regional operations to capture the homogenous and heterogeneous global malicious patterns. Moreover, diverse feature maps are achieved using transfer learning and multi-path-based squeezing and boosting at initial and final levels to learn minute pattern variations. Finally, the boosted discriminative features are extracted from the developed deep SB-BR-STM CNN and provided to the ensemble classifiers (SVM, MLP, and AdabooSTM1) to improve the hybrid learning generalization. The performance analysis of the proposed DSBEL framework and SB-BR-STM CNN against the existing techniques have been evaluated by the IOT_Malware dataset on standard performance measures. Evaluation results show progressive performance as 98.50% accuracy, 97.12% F1-Score, 91.91% MCC, 95.97 % Recall, and 98.42 % Precision. The proposed malware analysis framework is robust and helpful for the timely detection of malicious activity and suggests future strategies.

Nowadays, rumor spreading has gradually evolved into a kind of organized behaviors, accompanied with strong uncertainty and fuzziness. However, existing fuzzy detection techniques for rumors focused their attention on supervised scenarios that require expert samples with labels for training. Thus, they are not able to well handle the unsupervised scenarios where labels are unavailable. To bridge such gap, this article proposed a fuzzy detection system for rumors through explainable adaptive learning. Specifically, its core is a graph embedding-based generative adversarial network (Graph-GAN) model. First of all, it constructs fine-grained feature spaces via graph-level encoding. Furthermore, it introduces continuous adversarial training between a generator and a discriminator for unsupervised decoding. The two-stage scheme not only solves the fuzzy rumor detection under unsupervised scenarios, but also improves robustness of the unsupervised training. Empirically, a set of experiments are carried out based on three real-world datasets. Compared with seven benchmark methods in terms of four metrics, the results of the Graph-GAN reveal a proper performance, which averagely exceeds baselines by 5–10%.

The number of people afflicted with paralysis is increasing worldwide due to stroke, spinal cord injury, polio, and other related diseases. Exoskeletons have emerged as one of the promising technologies to provide assistance and rehabilitation for the paralyzed people. But most of the exoskeletons are limited by its bulkiness, lack of flexibility and stability, instant control and adaptability. To overcome these issues, this article proposes a novel and efficient software-defined network (SDN)-powered humanoid assistive and rehabilitation system. In the proposed system, the signals acquired by the human sensor module are processed with multiple node MCUs and transmitted via the SDN incorporated with universal software radio peripheral (USRP). Using edge computing, the signal from the USRP is sent to the receiver node MCU and is used for controlling the movements of the humanoid that provides assistance to the paralyzed patients. The experimental setup is done for controlling a humanoid hand, and the results show high quality-of-service (QoS) for hand roll-up and roll-down posture. QoS is also evaluated for different electroencephalogram (EEG) signals, and the results show that the SDN-enabled assistive humanoid system is an efficient method for providing instant control in rehabilitation of the paralyzed patients.

Millions of devices are becoming part of Internet of Things/5G. Securing these devices against all potential threats is a huge challenge. The 5G specification goals require rigid and robust security protocol against such threats. Quantum cryptography is a recently emerged term in which we test the robustness of security protocols against quantum computers. Therefore, in this article, we propose a security protocol called quantum key GRID for authentication and key agreement (QKG-AKA) scheme for the dynamic security association. This scheme is efficiently deployed in long term evolution architecture without any significant modifications in the underlying base system. The proposed QKG-AKA mechanism is analyzed for robustness and proven safe against quantum computers. The simulation results and performance analysis show drastic improvement regarding security and key management over existing schemes.