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The detection of computer network attacks is becoming increasingly important as the complexity and frequency of cyber-attacks threatening information systems and network infrastructure continue to rise. These attacks may lead to severe consequences, including data breaches, service disruptions, and financial losses. To address these challenges, artificial intelligence techniques have become a major focus in the development of more effective, adaptive, and reliable intrusion detection systems. Among various classification algorithms, the C4.5 decision tree has demonstrated strong performance due to its simplicity, interpretability, and high classification accuracy. This study aims to apply the C4.5 algorithm for network attack detection using a comprehensive dataset that includes multiple categories of attacks and normal network activities. The proposed methodology consists of several stages, including data preprocessing, feature selection, decision tree model construction, and performance evaluation using standard metrics such as accuracy, precision, recall, and F1-score. Data preprocessing is performed to handle missing values, normalize data, and reduce noise, thereby improving the overall quality of the dataset and enhancing classification results. The experimental results indicate that the C4.5 decision tree algorithm effectively classifies network traffic into attack and normal categories with a satisfactory level of accuracy. The model successfully identifies attack-related patterns and highlights significant features that influence detection performance. Further analysis reveals that appropriate feature selection and parameter tuning significantly contribute to improving model reliability and robustness. This research provides a valuable contribution to the development of efficient, accurate, and practical network intrusion detection systems. The proposed approach is expected to strengthen information security frameworks and support proactive defense strategies against increasingly sophisticated cyber threats, thereby enhancing the protection of critical network infrastructures.

This research aims to develop a network security system with AdGuard DNS on Debian Linux and control time-based internet access using the Microtik Routerboard. The main challenges are rising network security threats and the need for efficient internet access management in a variety of environments, including families,, education, and offices. The aim of this study is to improve network security and efficiency by blocking harmful content and advertisements, ining user privacy, and regulating internet access at a specific time. Technologies used include DNS AdGuard on Debian Linux to strengthen protection against network security threats and RouterOS Microtik devices to manage internet access based on time. These technologies include content filtering features, network traffic distribution (load balancing), and the ability to block unwanted sites to enhance network performance. Research results show that the combination of AdGuard DNS and   Microtik Routerboard is an effective solution to enhance network security and efficiency. The system is capable of blocking harmful content and advertising, improving user privacy, and adjusting internet access as needed, thus creating a more secure, controlled, and optimal network.

This study discusses the network security analysis using MikroTik devices in the computer laboratory of STMIK Widuri. The main objective of this research is to evaluate the level of network security in the computer laboratory by implementing appropriate configurations using MikroTik devices. The methods employed include literature review on network security, analysis of network topology in the laboratory, and implementation of security solutions using MikroTik devices. Data collected include security testing results, network performance, and user satisfaction levels. The research findings indicate that the implementation of security solutions with MikroTik effectively enhances network security in the computer laboratory of STMIK Widuri. This study contributes to a practical understanding of using MikroTik in improving network security in campus environments

This study aims to design and build a hotspot network system using userman and proxy server radius at SMK Negeri 5 Luwu to solve problems because users are not limited in using devices to make internet connections in the school environment. Radius servers run a centralized user administration system. This system will certainly simplify the task of an administrator. With this system, users can use hotspots in different places by authenticating to the radius server. This research was conducted at SMK Negeri 5 Luwu. The research method used in this study is Network Development Life Cycle (NDLC). Data collection methods consisting of direct observation methods to the research location, interviews with one of the operators as well as network operators / administrators in the school, literature studies. Testing was conducted using browser, CMD and Speedtest. The results of research with the hotspto using userman and radius server using the same user as the password in user authentication management to improve network security have been successfully implemented internet networks in schools to improve network security, so it is very helpful for network providers in managing and monitoring all users connected to their networks.    

The advancement of digital technology has brought about significant progress across various sectors, while simultaneously introducing new risks, particularly in the realm of cybersecurity. Cybersecurity has become a critical concern for companies, including energy giants like Pertamina. In the context of Indonesia, adherence to cybersecurity within Pertamina must align with relevant legal frameworks, such as the 1945 Constitution (UUD 1945). This research aims to explore optimal approaches to implementing cybersecurity within Pertamina, with a focus on the legal underpinnings of the 1945 Constitution. Key attention is directed towards Article 27, paragraph (3) of the Constitution, which guarantees universal access to education, including information security training for employees—a crucial element in thwarting cyber threats. Furthermore, Article 28, paragraph (1) of the 1945 Constitution, safeguarding freedom of association, can be interpreted to endorse collaboration between Pertamina and external entities, like cybersecurity institutions and regulatory bodies, to bolster cyber defense capabilities. Integration of cutting-edge security technology is paramount, aligning with the principles outlined in Article 33, paragraph (2) of the 1945 Constitution, emphasizing the preservation of natural resources. This extends to safeguarding Pertamina's data and digital infrastructure through measures like encryption technology, robust network security protocols, and effective intrusion detection systems. By heeding the legal foundations provided by the 1945 Constitution, the implementation of cybersecurity at Pertamina must be comprehensive, encompassing employee education, technological upgrades, and collaborative efforts with external stakeholders. Such measures aim to mitigate cyber risks, safeguard operational interests, and shield sensitive information from evolving threats in today's digital landscape. Additionally, Law Number 11 of 2008 plays a crucial role in governing information technology and electronic transactions in Indonesia. This legislation grants legal validity to electronic documents, signatures, and transactions, while also ensuring information security and user privacy. It imposes criminal penalties for illicit activities in the realm of information technology, fostering a secure environment for electronic transactions and upholding user rights in the digital age.

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.