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Item type:Publication, Systematic mapping of the literature on secure software development(Institute of Electrical and Electronics Engineers Inc., 2021-03-09)The accelerated growth in exploiting vulnerabilities due to errors or failures in the software development process is a latent concern in the Software Industry. In this sense, this study aims to provide an overview of the Secure Software Development trends to help identify topics that have been extensively studied and those that still need to be. Therefore, in this paper, a systematic mapping review with PICo search strategies was conducted. A total of 867 papers were identified, of which only 528 papers were selected for this review. The main findings correspond to the Software Requirements Security, where the Elicitation and Misuse Cases reported more frequently. In Software Design Security, recurring themes are security in component-based software development, threat model, and security patterns. In the Software Construction Security, the most frequent topics are static code analysis and vulnerability detection. Finally, in Software Testing Security, the most frequent topics are vulnerability scanning and penetration testing. In conclusion, there is a diversity of methodologies, models, and tools with specific objectives in each secure software development stage. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent route planning for effective police patrolling in a Peruvian district(Springer, 2022-01-01)Citizen security directly influences life quality and is a great source of concern in Latin America. A scheduling and vehicle routing model is proposed for the allocation and routing of police resources, increasing visits in locations with a higher crime rate, and strategic standby locations based on real data. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Advances in resource management through the integration of distributed computing approaches(Wiley, 2024-03-08)With a focus on grid computing, cloud computing, and edge computing (EC), this chapter analyzes the integration of distributed computing systems for resource management. These methods present a number of advantages, including increased flexibility, scalability, and analysis and processing of data capabilities, but they also present difficulties, including efficient resource scheduling, management, and monitoring, as well as issues with data security and management. To address these challenges, researchers are developing new techniques and tools for managing and allocating resources across distributed computing environments. These include resource allocation and scheduling algorithms, monitoring and management tools, and security and data management techniques. The chapter also discusses emerging trends and future research directions in the field, including the increasing use of EC and the need for more efficient and effective resource allocation and scheduling algorithms. The integration of distributed computing approaches for resource management has the potential to transform computing infrastructure for organizations. By leveraging the resources of multiple systems and networks, organizations can scale their computing infrastructure, improve data processing and analysis capabilities, and improve overall efficiency and cost savings. Although there are difficulties to be overcome, the advantages of these technologies make them a crucial field for future research and development. the requirement for ongoing study and development in this field to solve problems and enhance methods and tools for resource management in distributed computing settings. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Intelligent Fog Computing Surveillance System for Crime and Vulnerability Identification and Tracing(IGI Global, 2023-01-01)IoT devices generate enormous amounts of data, which deep learning algorithms can learn from more effectively than shallow learning algorithms. The approach for threat detection may ultimately benefit fog computing or fog networking (fogging). The authors present a cutting-edge distributed DL method for detecting cyberattacks and vulnerability injection (CAVID) in this paper. In terms of the evaluation metrics tested in the tests, the DL model performs better than the SL models. They demonstrated a distributed DL-driven fog computing CAVID approach using the open-source NSLKDD dataset. A pre-trained SAE was utilised for feature engineering, whereas Softmax was employed for categorization. They used parametric evaluation for system assessment to evaluate the model in comparison to SL techniques. For scalability, accuracy across several worker nodes was taken into consideration. In addition to the robustness, effectiveness, and optimization of distributed parallel learning among fog nodes for enhancing accuracy, the findings demonstrate DL models exceeding classic ML architectures.