The rise of smart cities brings increased interconnectivity, but also new security vulnerabilities, especially among IoT devices. This study investigates methods for improving cybersecurity in smart cities by implementing IoT device management protocols. We examine approaches such as network segmentation and secure authentication to mitigate common threats, thus providing a safer environment for urban digital infrastructure.

The growth of big data has driven the need for efficient data processing methods, especially in cloud computing environments. This study evaluates distributed computing frameworks like Apache Hadoop and Apache Spark for optimizing big data processing. By analyzing different configurations, we demonstrate how distributed systems can significantly reduce processing time and improve resource utilization, making them ideal for handling complex datasets in cloud environments.

Network intrusion detection is crucial for maintaining the integrity of real-time systems. This paper evaluates various machine learning algorithms, including support vector machines (SVM) and decision trees, for real-time intrusion detection. Through extensive testing on simulated datasets, the study highlights the advantages of automated detection in reducing response times and enhancing network security.

With the rapid expansion of the Industrial Internet of Things (IIoT), integrating devices, machines, and systems to optimize operations and enable data-driven decision-making, ensuring robust security measures is essential. While blockchain has shown the potential to upgrade traditional authentication methods in IIoT environments, vulnerabilities persist. This paper introduces two innovative methods to enhance blockchain-based authentication in IIoT: first, integrating AI-driven anomaly and threat detection into the blockchain authentication scheme; second, implementing Ethereum smart contracts for enhanced authentication with a two-factor authentication (2FA) system and GFE algorithms. By combining AI for anomaly detection with decentralized smart contracts and blockchain-based 2FA, and leveraging GFE algorithms to enhance blockchain capabilities, the proposed scheme aims to significantly fortify security measures. This integration offers a resilient defense against evolving threats, ensuring transparency, adaptability, and heightened security in IIoT applications.

Information Technology (IT) service management plays a critical role in supporting the efficiency and effectiveness of business intelligence processes within modern organizations. This study aims to evaluate IT service governance in Bank Rakyat Indonesia using the Information Technology Infrastructure Library (ITIL) framework. This approach focuses on improving operational efficiency, service management, and aligning IT services with business needs. The research method involves a detailed analysis of service processes and activities based on ITIL stages, ranging from service strategy to continuous improvement. The findings indicate that ITIL implementation significantly enhances IT service management and promotes business intelligence effectiveness within the organization. Recommendations for further development are also provided to improve the quality and speed of IT services in the future.

In recent years, virtual assistants have become an integral part of everyday life, simplifying routine tasks and allowing users to focus on more important matters. This research aiming to design GiggleGate, a virtual desktop assistant integrated with both face and speech recognition technology to enhance authentication security. The objective is to develop an authentication system that not only verifies user identity but also provides a more intuitive experience and seamless interaction. The research employs a development methodology to create and implement the system, which integrates face recognition via OpenCV and speech recognition via a Python library. The findings indicate that the integration of these technologies enhances security and user experience by offering dual-factor authentication. The system is expected to contribute to more secure and accessible virtual assistant applications, offering both a practical and efficient solution for users. The implications of this study suggest that the combination of face and speech recognition can provide an effective means to protect user privacy and improve the overall functionality of desktop assistants.

With quantum computing’s rise, traditional cryptographic methods face new security challenges. This paper evaluates the effectiveness of various quantum algorithms, such as Shor’s and Grover’s algorithms, on current encryption standards. Our findings highlight the vulnerabilities in conventional cryptography and suggest new standards and protocols to maintain data security in a quantum-driven era.

The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.

Social media platforms contain vast amounts of data that can reveal public sentiment on various topics. This research explores the application of deep learning techniques, particularly convolutional neural networks (CNN) and recurrent neural networks (RNN), to analyze sentiment within social media text. The results indicate that these models achieve high accuracy in sentiment classification, making them valuable tools for companies seeking to understand public opinion.

This study aims to compile an Information Technology (IT) Master Plan for SMK Ma'arif 1 Metro using The Open Group Architecture Framework (TOGAF) method. This method is applied to design a structured and integrated information technology infrastructure to support operations and learning in schools. The results of the study indicate that the main business processes in schools, such as student registration, curriculum management, and financial administration, are still carried out manually, making them prone to errors and inefficiencies. Through TOGAF, an information system architecture is designed that includes the development of the Academic Information System (SIAKAD) and other school management applications. In addition, improving network infrastructure and cybersecurity are priorities in the proposed technology architecture. Implementation strategies and change management are also formulated to ensure the success of the digital transformation. This IT Master Plan is expected to improve the efficiency of school operations and the quality of learning, as well as support the achievement of more optimal educational goals in the digital era.