Conference Agenda

The present investigation addresses the growing problem of robberies, assaults and acts of vandalism that have affected the economic activity of small businesses in Carabayllo, generating an environment of fear that has led many to close or operate with insecurity. The majority of residents and entrepreneurs in this area lack the financial resources necessary to implement adequate security systems in their homes and establishments. Therefore, the objective of the study is to design a home automation system based on Arduino for the security of low-income homes in the Carabayllo district, Lima. An applied methodology was used, with a quantitative approach and a study design. The technique used was the survey, using a questionnaire directed at a sample of 40 families in the El Progreso area. The results revealed that 52.5% of respondents consider the ability to control their home remotely extremely important (Scale 5), evidencing a strong demand for solutions of this type. In conclusion, the implementation of a home automation system is presented as a viable and accessible solution to improve security in these homes.

Detecting phishing websites using Machine Learning (ML) techniques is a key approach in modern cybersecurity, with models such as Random Forest reaching accuracy levels close to 99%, followed by Support Vector Machine, Decision Tree and Logistic Regression. However, what is the level of accuracy of ML techniques in this task and what are the key factors affecting their accuracy and effectiveness? The results highlight that the quality and diversity of the training data, together with metrics such as Accuracy, Precision and Recall, are determinants in the performance of the models. In addition, the ability of algorithms to adapt to dynamic attack patterns is crucial. This study, based on a systematic review with the PRISMA statement, analyzed 43 articles selected from more than 4,600 initials, revealing the importance of developing computationally efficient methods that maintain high levels of accuracy to address growing digital threats.

This review article analyses the impact of blockchain technology on the management of the chain of custody of digital evidence in forensic analysis. Fifteen relevant studies were examined to identify the specific procedures used, the improvements introduced by blockchain, the differences with traditional methods, and the metrics used to evaluate its effectiveness. The results highlight that blockchain offers significant advantages in terms of integrity, traceability, reliability, and efficiency, overcoming the limitations of traditional methods based on centralized and manual records. The most prominent procedures include the use of hashing to ensure the immutability of records, precise timestamps to guarantee traceability, and redundancy through distributed nodes to prevent data loss. Blockchain improves transparency and security, allowing real-time access and reducing human errors through automation with smart contracts. Furthermore, metrics such as consensus rate and resilience demonstrated the robustness of the system in adverse scenarios, while challenges related to energy consumption and interoperability underline the need for more sustainable solutions. It is concluded that blockchain is a transformative technology for digital forensics, capable of redefining evidence custody standards. However, it is essential to address technical and economic challenges through the establishment of global standards and interdisciplinary collaboration to maximize its potential in practical and diverse environments. This work contributes to the understanding of blockchain as a key tool to strengthen forensic processes in the management of digital evidence.

This article presents a comprehensive study on the detection of cyberattacks on websites, highlighting the increase in threats such as phishing, SQL injection and DDoS attacks, which compromise data security and user trust. Through a methodology aimed at the review and collection of 21 recent studies, methods based on artificial intelligence were evaluated, such as neural networks and behavioral analysis, which surpass traditional approaches, such as firewalls and IDS, in precision and adaptability. The results underline that the most effective strategies are based on adaptive approaches and emerging technologies, integrating dynamic systems capable of responding to contextual changes and advanced analytical tools to mitigate risks. This review contributes to consolidating an updated and detailed overview, identifying knowledge gaps and establishing a solid foundation for the development of innovative and robust solutions that strengthen security in modern web applications.

This study aims to analyze the use of machine learning for detecting and preventing malware attacks in key technological environments such as IoT, enterprise networks, and critical systems. A comprehensive review of deep learning techniques and models applied in cybersecurity is conducted, evaluating their effectiveness, accuracy, and the limitations they face against emerging threats. The review also aims to identify the most innovative solutions that integrate artificial intelligence to enhance cyber defenses. To carry out this RSL, a methodological approach was followed, which included collecting relevant articles from academic databases, applying inclusion and exclusion criteria to ensure the quality of the selected studies. Key data was extracted and analyzed from the reviewed works, organized by the machine learning techniques used and the specific areas of application. The results showed that deep learning techniques and hybrid models have significantly improved the detection and mitigation of advanced attacks, such as ransomware and APTs. However, important challenges in implementing these technologies were identified, especially in sectors with resource limitations and resistance to organizational change. In conclusion, the use of machine learning has proven to be highly effective in improving cybersecurity, although its widespread adoption still faces barriers such as the lack of trained personnel and adequate infrastructure. This study highlights the need for further research into integrating these technologies with emerging solutions and improving their adaptability across different organizational contexts.

In an era marked by the relentless escalation of cyber threats and the transformative impact of technological advancements, the world faces a critical challenge: the urgent need for unified global cybersecurity policies. Despite the glaring necessity, efforts are hindered by fragmented regulations, stark technological inequalities between nations, and deep-seated sociocultural divergences that obstruct cohesive international frameworks. This study aims to identify the key barriers to harmonizing international cybersecurity regulations and propose strategies for their resolution, focusing on regulatory, technical, and sociocultural dimensions. A systematic review was conducted following PRISMA guidelines, utilizing databases such as Web of Science, Scopus, and IEEE Xplore. The findings reveal significant fragmentation in global cybersecurity practices due to misaligned legal frameworks, insufficient infrastructure in developing nations, and divergent cultural perceptions of privacy and security. Challenges include the technological divide, lack of standardized protocols, and limited collaboration between public and private sectors. In conclusion, effective global cybersecurity governance requires inclusive strategies that bridge regulatory gaps, promote international collaboration, and address technological and cultural disparities.

This review aims to identify and collect current knowledge on how technologies such as artificial intelligence, machine learning, blockchain, IoT, and others can strengthen cybersecurity. Method: the PICO strategy and PRISMA diagram were used, 133 relevant articles were selected and analyzed from the SCOPUS database between the years 2019 to April 2024. The methods of this SLR included rigorous inclusion and exclusion criteria to ensure the relevance and quality of the studies analyzed. Results: within the present work the most widely used emerging technologies and their practical applications in cybersecurity are highlighted, identifying significant trends and patterns in the implementation of these technologies. Conclusions: the main current limitations are pointed out, such as the lack of standardization and the need for further empirical research, and key areas for future research are proposed, including the evaluation of the effectiveness of these technologies in different business contexts; this study provides a comprehensive and updated view that can guide both researchers and practitioners in the implementation of cybersecurity strategies based on emerging technologies, offering a solid foundation for improving defenses against increasingly sophisticated cyber threats.