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Building a computer network needs significant care in selecting a configuration to ensure its efficacy and efficiency. It involves decisions about network topology, types, and device use. Security measures are essential for protecting networks with internet access from external attacks. A study conducted at an Islamic university in Indonesia indicated the use of a star network architecture and a client-server network model. The servers utilize Debian Linux. The university's security infrastructure includes a server authentication system and the setting up of a Virtual Private Network (VPN). If the server authentication difficulty is caused by a power outage, it is recommended that you use an UPS (uninterruptible power supply) to preserve power stability. Overall, security methods show up to be more effective when firewalls are installed.

In the era of digital communication, access speed and data security are crucial. The study evaluated network topology, focusing on tree topology using simulations with Cisco Packet Tracer. Tests highlighted stability, latency, throughput, and bottleneck potential. Data transmission time information is key in assessing network performance. Test results are important for optimizing performance and identifying areas of improvement in the network. In conclusion, monitoring network performance is an important step for operational efficiency and security.    

The rapid growth of digital technologies has significantly increased the complexity and frequency of cyber threats, making network security a critical concern in modern information systems. Traditional security approaches, such as rule-based and signature-based systems, are often limited in detecting sophisticated and unknown attacks. Therefore, this study proposes an Anomaly-Based Intrusion Detection System (AbIDS) utilizing machine learning and deep learning techniques to enhance detection capabilities. The research adopts a Design Science Research approach, involving stages of problem identification, data collection, preprocessing, model development, system implementation, and evaluation. Several models, including Decision Tree (DT), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), are implemented and compared. The results indicate that deep learning models, particularly LSTM and CNN, outperform traditional machine learning methods in terms of accuracy, precision, recall, and F1-score, while maintaining a lower false positive rate. Additionally, the integration of incremental learning enables the system to adapt to new attack patterns without requiring complete retraining, improving scalability and real-time performance. Despite the promising results, challenges such as computational complexity and false positives remain. Overall, the proposed IDS model demonstrates strong potential as an effective and adaptive solution for enhancing network security in dynamic environments.

This study explores the development and evaluation of an adaptive Intrusion Detection and Response System (IDRS) driven by Reinforcement Learning (RL) for securing 5G networks. The RL-based IDS is designed to overcome the limitations of traditional security systems by dynamically learning from real time network traffic and adapting to emerging cyber threats. Introduction: The rapid growth of 5G networks, with their increased number of connected devices and complex traffic patterns, necessitates advanced security solutions that can detect and respond to evolving cyberattacks. Literature Review: Traditional Intrusion Detection Systems (IDS), including signature based and anomaly based methods, are not equipped to handle the dynamic nature of 5G networks, leading to high false positives and low detection accuracy. In contrast, RL offers significant improvements in adaptability, detection accuracy, and response time. Materials and Method: The study simulates 5G network traffic and develops an RL-based IDS using Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO) techniques. The performance of the RL-based system is compared to traditional IDS systems, focusing on detection accuracy, false positive rates, and response times. Results and Discussion: The RL-driven IDS demonstrated superior performance, achieving higher detection accuracy (95%) and faster response times (30 milliseconds) compared to traditional methods. However, challenges such as computational cost and model interpretability were identified. The study emphasizes the importance of adaptive learning mechanisms and the integration of RL into Zero Trust Architecture (ZTA) to enhance the security of 5G networks.

Telecommunications network planning is a crucial step in building an efficient and reliable communications infrastructure. In an era where connectivity is the backbone for various human activities, both on an individual and business scale, the importance of telecommunications network planning becomes increasingly apparent. In this abstract, we explain why telecommunications network planning is so important. First, careful planning ensures efficient use of resources, from frequency spectrum to hardware. Second, good planning can anticipate demand growth, ensuring that the network can grow in line with needs without sacrificing service quality. Furthermore, careful planning also considers security, privacy and reliability factors, which are crucial in a telecommunications environment that is constantly changing and vulnerable to threats. Thus, this abstract highlights the importance of telecommunications network planning in facing the challenges and opportunities in an increasingly connected world. With proper planning, telecommunications networks can become a strong backbone for social, economic and technological development.

Cyber Physical Systems (CPS) are vital for managing and controlling critical infrastructures, such as industrial control systems, power grids, and transportation networks. These systems integrate digital and physical components, offering numerous benefits for industrial automation. However, the increasing interconnectivity of these systems has introduced new security vulnerabilities, particularly in anomaly detection and system reliability. This research aims to address these challenges by proposing an edge based anomaly detection framework that leverages lightweight deep learning models, specifically designed to operate efficiently on resource constrained edge devices. Literature Review: Previous studies have shown the effectiveness of anomaly detection in CPS, with traditional methods struggling to keep up with the complexity and scale of modern industrial environments. Machine learning and deep learning approaches, particularly hybrid models combining rule based systems and AI, have emerged as effective solutions for real time anomaly detection. Techniques such as model compression, quantization, and pruning are essential for adapting these models to resource limited edge devices while maintaining high detection accuracy and low latency. Materials and Method: The proposed framework integrates deep learning models such as Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) networks, optimized for edge computing environments. The datasets used for training and testing include industrial network traffic and sensor anomaly datasets. Model optimization techniques like pruning and quantization were applied to reduce computational overhead and energy consumption on edge devices. Results and Discussion: The framework demonstrated high detection accuracy (AUC of 0.9720) with ultra low latency (0.0019 seconds training time), making it highly suitable for real time anomaly detection in CPS. Resource efficiency was achieved by optimizing the models for edge devices, reducing energy consumption while maintaining performance. The framework also significantly improved security by identifying anomalies early, preventing potential threats to critical infrastructures. Future directions include exploring federated learning to enhance privacy and data sharing across distributed devices.

This research uses a qualitative approach to understand the efforts of the North Lampung Police in dealing with motorbike theft. Qualitative methods allow for in-depth analysis of strategies, policies and obstacles faced in addressing this problem. Preventive efforts are carried out through integrated security, appeals to the community, as well as routine patrols and raids. Repressive efforts are carried out by taking firm action against perpetrators of crimes, as well as careful investigations and inquiries into cases of theft. However, the North Lampung Police face a number of obstacles in tackling motor vehicle theft crimes. The lack of timely public reports, the difficulty of obtaining sufficient evidence, and illegal motor vehicle trafficking networks are the main challenges. Even though preventive and repressive efforts have been carried out, innovation and better cooperation between the police and the community are still needed to overcome this problem. In conclusion, the North Lampung Police have made various efforts to tackle motor vehicle theft, but are still faced with a number of obstacles. Better coordination is needed between the police, government and community to create a safer and more comfortable environment for all parties.

This Space Security System is a system that utilizes the existing Internet Network to monitor a room remotely or from a different location. With the aim of making it easier to limit employee access rights and monitor the presence of people in the room.So the author made a room security system tool using the Internet of Things (IOT) which was implemented at the Demak Regency Communication and Information Service. The way this system works is only employees who have access rights who can enter the room by attaching an RFID card to the RFID Reader sensor, and the way the PIR sensor works is to monitor human presence, when there is infrared or human emission it will send data to the web if it is in the room has people and the red LED lights up and the buzzer sounds, while the push button button is when the push button button is pressed the solenoid will open. By making this design, it will help improve room security at the Demak Regency Communication and Information Service, especially the Encryption Room

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.

The rapidly evolving landscape of cryptanalysis necessitates an urgent and detailed exploration of the high-degree non-linear functions that govern the relationships between plaintext, key, and encrypted text. Historically, the complexity of these functions has posed formidable challenges to cryptanalysis. However, the advent of deep learning, supported by advanced computational resources, has revolutionized the potential for analyzing encrypted data in its raw form. This is a crucial development, given that the core principle of cryptosystem design is to eliminate discernible patterns, thereby necessitating the analysis of unprocessed encrypted data. Despite its critical importance, the integration of machine learning, and specifically deep learning, into cryptanalysis has been relatively unexplored. Deep learning algorithms stand out from traditional machine learning approaches by directly processing raw data, thus eliminating the need for predefined feature selection or extraction. This research underscores the transformative role of neural networks in aiding cryptanalysts in pinpointing vulnerabilities in ciphers by training these networks with data that accentuates inherent weaknesses alongside corresponding encryption keys. Our study represents an investigation into the feasibility and effectiveness of employing machine learning, deep learning, and innovative random optimization techniques in cryptanalysis. Furthermore, it provides a comprehensive overview of the state-of-the-art advancements in this field over the past few years. The findings of this research are not only pivotal for the field of cryptanalysis but also hold significant implications for the broader realm of data security.

The rapid growth of Internet of Things (IoT) and edge computing technologies has introduced new security challenges due to the distributed, heterogeneous, and dynamic nature of these environments. Conventional static security mechanisms, such as rulebased authentication and fixed trust models, are often inadequate for addressing evolving threats and abnormal behaviors in largescale IoT systems. To overcome these limitations, this study proposes a machine learningbased trust evaluation framework for enhancing security in distributed IoT environments. The proposed approach dynamically assesses the trustworthiness of IoT nodes by analyzing behavioral and interactionbased features collected at the edge layer. Machine learning models are trained to classify nodes into trusted and malicious categories and continuously update trust values in response to changing network conditions. Based on the predicted trust levels, adaptive security decisions are enforced to allow or restrict node participation in data sharing and computation processes. A quantitative experimental evaluation is conducted using simulated distributed IoT scenarios that include both normal and malicious behaviors. The performance of the proposed framework is evaluated using standard metrics such as accuracy, precision, recall, F1score, and detection effectiveness, and is compared against conventional static trust and rulebased security mechanisms. The results demonstrate that the proposed machine learningbased trust evaluation approach achieves significantly higher detection accuracy and robustness while maintaining low computational overhead. Overall, the findings confirm that integrating machine learning into trust management provides an effective and scalable solution for securing distributed IoT systems under dynamic and adversarial conditions.

This research aims to find out and analyze efforts to fulfill political rights for first-time voters with disabilities in the general election in Gorontalo Regency in 2019 and to find out and analyze the factors inhibiting the fulfillment of political rights for first-time voters with disabilities in the general election in Gorontalo Regency in 2019 , based on the title raised in this research, researchers see the weakness of Indonesia's defenses from attacks by things that endanger society, thousands of illegal drugs have been circulating throughout Indonesia. This research uses empirical methods. The data collection process examined by researchers in this research is primary data and secondary data. Data collection techniques that support and are related to this research are interviews, observation and literature study. The results of this research show that the KPU and the Government have ensured the implementation of efforts to respect and promote , protection and fulfillment of the rights of novice voters with disabilities to develop themselves and utilize all abilities according to their talents and interests to enjoy, participate and contribute optimally, safely, freely and with dignity in all aspects of national, state and social life. By socializing and expanding the network of first-time voters with disabilities to play an effective role in the general election system at all stages or parts of its implementation. Increasing the participation of people with disabilities in elections is also not easy. Every citizen has the right to social security necessary for a decent life and for full personal development. Every person with disabilities, elderly people, pregnant women and children have the right to receive special facilities and treatment. Accessibility problems are one of the factors that cause first-time voters with disabilities to not cast their votes optimally, as happened in Gorontalo where the Gorontalo Regency General Election Commission continuously coordinates with first-time voters with disabilities to ensure that these first-time voters with disabilities are still able to vote. exercise their rights when voting.

The Digital Era has transformed the way humans interact and communicate through the development of information technology and social networks. Social media platforms such as Facebook, Instagram, and Twitter have reshaped the landscape of social interactions, enabling individuals to connect with people worldwide quickly and efficiently. This article conducts an in-depth analysis of the significant influence of social networks on social interactions in the digital era. Social networks facilitate the exchange of information, collaboration, and getting to know each other in various forms, ranging from text to visual and audiovisual content. In this context, there are two key elements in social interactions: social contact and communication. The Uses and Gratifications theory highlight the active role of social media users in choosing and utilizing these platforms to fulfill their needs for entertainment, information, personal identity, and social integration. Despite the convenience they offer, the use of social networks also presents significant challenges, such as privacy issues and social media addiction. It is crucial for individuals to develop a healthy self-awareness regarding their social media usage. Additionally, technology companies and policymakers must prioritize protecting users' privacy and creating an online environment that is healthy, safe, and inclusive for everyone. This analysis not only discusses negative impacts such as privacy and data security but also demonstrates the potential of social networks as powerful tools for building bridges between cultures, ideas, and people worldwide. Therefore, collaboration between society, governments, and corporations is essential in creating a positive and healthy online environment for all users.

By using literature studies/literature research, this article discusses how programming algorithms can be applied to the Internet of Things (IoT). The purpose of this article is to explain the function of algorithms in Internet of Things (IoT) programming and several examples. IoT is a concept where various devices and objects can connect and communicate with each other via the internet network. After data is collected through text study, content analysis techniques are used to analyze it. Literature studies show that programming algorithms must be applied to the Internet of Things to ensure efficient collection, analysis and use of data from connected objects. Programming algorithms are widely used for data prediction and analysis, network management, data collection and processing, security, and optimization of communication between IoT objects. Internet of Things developers and researchers should pay attention to the importance of implementing appropriate programming algorithms in their systems because these algorithms enable IoT to optimize the use of resources such as bandwidth, memory, and energy. Efficient algorithms enable smarter data analysis and better data security.

By using literature studies/literature research, this article discusses how programming algorithms can be applied to the Internet of Things (IoT). The purpose of this article is to explain the function of algorithms in Internet of Things (IoT) programming and several examples. IoT is a concept where various devices and objects can connect and communicate with each other via the internet network. After data is collected through text study, content analysis techniques are used to analyze it. Literature studies show that programming algorithms must be applied to the Internet of Things to ensure efficient collection, analysis and use of data from connected objects. Programming algorithms are widely used for data prediction and analysis, network management, data collection and processing, security, and optimization of communication between IoT objects. Internet of Things developers and researchers should pay attention to the importance of implementing appropriate programming algorithms in their systems because these algorithms enable IoT to optimize the use of resources such as bandwidth, memory, and energy. Efficient algorithms enable smarter data analysis and better data security.

Threats and attacks that can result in leaks of personal or sensitive data or reduced business performance have a significant impact on the security of networks and information systems. Insider attacks, eavesdropping, misconfiguration, missing functionality, confusion, man-in- attacks, virus attacks, denial of service attacks are some of the dangers that threaten your network security or threaten the security of your network. Attacks are possible. Influencing information systems. Companies risk losing information appropriate security measures, such as network and information system security. You can implement the right security solutions to predict and prevent various security risks and attacks. Confidentiality, integrity, and availability are three characteristics of security, and to determine which security technology best suits your organization's needs, you must first compare the various threats and attacks with the security technologies in use. Recommended security technologies intrusion detection systems (IDS), antivirus programs, and encryption systems. This is because this technology relies on predicting and protecting networks and information systems in many security elements.

The use of the internet by students as a means of facilitating their activities and survival through social media has become a trend. The integration between the internet and social networks facilitates online product learning, especially in the context of online shopping. Students' consumption patterns, especially fashion products, have increased in line with their desire to follow the trends of today's young people. Fashion and appearance are the main focus for students, viewing it as an important aspect in showing identity and social status. Students' involvement in using the internet, especially for shopping, has changed their lifestyle significantly. Public consumption trends are influenced by students' online shopping behavior, which is not only based on needs, but also for personal pleasure and lifestyle. This can lead to consumer behavior, where individuals develop purchasing habits to satisfy themselves and increase prestige as a form of demonstrating socio-economic status. E-commerce, as a result of the development of Information Technology systems, has become an important means of meeting consumer needs. Factors such as price, level of trust, time freedom, and product variety influence students' online purchasing intentions. The wide variety of products online is an attraction, while trust is the basis for a mutually beneficial relationship between online sellers and buyers. This research uses a qualitative approach to get an in-depth picture of the impact of e-commerce on student consumption behavior. The research results show that student consumer behavior, especially in terms of online shopping, is influenced by factors such as trust, convenience and security. Trust in e-commerce is a key factor in growing students' buying interest.

This research aims to find out how vital information security compliance is in reducing the risk of Data Breach. The method used in this research is Library Research (Library) which is the method used in this writing, and in its use, this method uses books, and journals both in written form and online. Based on the results obtained from this research, it can be concluded that a Data Breach is an incident where sensitive data or important information becomes vulnerable or is accessed by unauthorized parties. To prevent a Data Breach, organizations need to take various security measures, policies, and practices, such as encryption, access management, physical security, network security, security training and awareness, security policies, and monitoring and auditing. Sensitive data protection helps maintain the confidentiality, integrity, and availability of data and protects the reputation and trust of customers and business partners. By implementing good sensitive data protection, organizations can reduce the risk of data breaches which can be financially detrimental and damage the company's image.  

The role of women in building village government is very important, because they contribute to the economic, social and environmental sectors. Women also have a key role in managing natural resources, local economic activities and maintaining environmental sustainability to build strong social networks in village communities. Women’s involvement is an absolute requirement in efforts to realize village development as an independent village. It is impossible for a country to prosper if its women are left behind. The complete and comprehensive development of a country requires the full role of women in all areas of life. Women as citizens or as sources of village development have the same rights, obligations and opportunities as men in all development activities in the country. All life. The role of women has also been accommodated by all national development regulations, such as Law no. 6 of 2014 concerning Villages, which states that women’s involvement is very necessary for village development. With the existence of women in Ciseeng Village, which is the location of this research, most residents here position women as equal to men. This means that women can also hold government positions ranging from BPD, Village Apparatus, to Village Head. In Ciseeng itself, the role of women in all aspects of development is quite pronounced, starting from participating in building village facilities, maintaining village security, PKK in empowering families, and so on.

Information security and data integration are important aspects in managing and maintaining the continuity of web server system operations. The threat of an attack on a web server can have a serious impact on an organization. This is because websites are able to display text, graphic and sound information from anywhere via the internet network. Behind this convenience, there is a risk of cyber security threats in the use of internet-based technology because it can be accessed from anywhere and by anyone who wants to steal sensitive information or take over the system. In this research, the way to overcome this problem is through implementing a SIEM security information system with the wazuh/Teler platform as an IDS which will be installed on the web server to visualize logs and detect threats to network traffic, especially those leading to the web server. The method used in this research is documentation and forensic investigation in researching or analyzing server log data on websites using wazuh and teler.