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This community service activity was carried out to overcome the problem of open and uncontrolled internet networks at SMK Islamiyah Serua. This condition poses security risks as well as inefficient bandwidth usage. The solution offered is the implementation of a network gateway system with a user-friendly web-based login portal, so that internet access can be limited only to educators through an authentication mechanism. The implementation method includes needs analysis, system design, implementation using MikroTik RouterOS and HTML/CSS/JavaScript-based web portals, system trials, and socialization to schools. The results showed a significant improvement in network security, bandwidth usage efficiency, and ease of access for teachers. In addition, this activity also increases educators' understanding of the importance of network security in supporting the learning process. The implementation of this system provides sustainable benefits for schools, both from a technical and educational perspective, because it is able to create a safer and more controlled digital environment. Thus, this community service activity not only solves technical problems, but also contributes to improving the quality of educational services at SMK Islamiyah Serua.

ARP spoofing attacks are a serious threat to network security, particularly in vulnerable Internet of Things (IoT) environments. This final project aims to detect ARP spoofing attacks on IoT net-works using a combination of Random Forest (RF) and Robust PCA methods. RF is chosen for its classification capabilities and handling of non-linear data, while Robust PCA is used for di-mensionality reduction and handling outliers in the data. The dataset used is "MITMArpSpoof-ing.pcap.csv," which contains network traffic data. The data is processed by performing prepro-cessing, feature scaling, and converting labels to binary (0 for benign, 1 for ARP spoofing). Subsequently, Robust PCA is applied to reduce data dimensions, and then the data is trained using the RF model. The test results show that the RF model with Robust PCA achieves an accu-racy of 96.02% in detecting ARP spoofing attacks. This method has proven effective in identify-ing and classifying ARP spoofing attacks on IoT networks.

The digital era has transformed the financial sector through the integration of FinTech, making it more susceptible to increasingly complex cyber threats. As these risks rise, there has been a significant increase in academic research to better understand the cybersecurity challenges within the financial sector. This study aims to explore the development of cybersecurity research globally within this field. By utilizing bibliometrics, the research analyzes literature data collected from the Scopus database over the last five years. The analysis was conducted using VOSviewer and RStudio to identify dominant clusters, with cybersecurity and network security as the central themes linking various sub-fields, including artificial intelligence, cyberattacks, and phishing. The findings reveal areas of extensive research and highlight gaps that require further exploration. This study provides valuable insights for researchers and professionals in the cybersecurity field, offering a roadmap for future investigations and the identification of underexplored areas that need attention. Ultimately, this research contributes to advancing knowledge in the financial sector’s cybersecurity landscape and assists in shaping future research directions.

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.  

The development of the internet in Indonesia has opened up numerous opportunities, including in the field of online commerce. One widely used feature in online transactions is the "Paylater" system, as implemented by Shopee through its Shopee Paylater service. This feature allows users to purchase goods now and pay for them later, with relatively low interest rates. This study aims to examine the regulations and implementation of Shopee Paylater, as well as the legal protection available to consumers in cases of default or issues with the service. The study refers to the Indonesian Civil Code, Law No. 8 of 1999 on Consumer Protection, and the Financial Services Authority Regulation (POJK) No. 77/POJK.01/2016 on Information Technology-Based Lending Services. The research findings indicate that Shopee Paylater users are bound by a standard agreement with PT. Commerce Finance during the purchasing process. Shopee is responsible for protecting consumers in accordance with applicable regulations, but breaches related to network security can pose risks to consumers. In addition, while the service facilitates easier access to goods and services for users, it is crucial that consumers fully understand the terms and conditions of the service, including the consequences of late payments. The study also emphasizes the need for clearer and more comprehensive consumer protection regulations, especially in light of the rapid growth of digital financial services. This research highlights the importance of adequate legal protection for users of Paylater services to ensure their rights are safeguarded in the evolving digital economy.

The increasing vulnerabilities of modern enterprise network security systems highlight the necessity of adopting a more comprehensive and adaptive security approach than traditional VPN infrastructures. This study explores the design and implementation of Zero Trust Network Access (ZTNA) using the Cloudflare platform at PT FHI, focusing on addressing the weaknesses caused by uncontrolled remote access and legacy security models. The research adopts an experimental methodology based on the Network Development Life Cycle (NDLC), which includes systematic phases such as needs analysis, architectural design, system implementation, testing, and performance evaluation. Data were collected through structured interviews with IT managers and security specialists, direct observation of network logs, and comprehensive testing involving three different categories of end users. The findings demonstrate significant improvements in role-based access control, granular authentication, and the elimination of unrestricted access to internal resources. Additionally, real-time monitoring and alerting features available through the Cloudflare dashboard enhance visibility and responsiveness to potential threats. The implemented system successfully applies the “never trust, always verify” principle by blocking unauthorized access attempts, enforcing continuous validation, and producing detailed audit logs. Performance evaluation results confirm stable connections, acceptable latency for critical business applications, and an overall improvement in network security posture while maintaining operational productivity across departments..

To securely access servers remotely, an important protocol in the modern era is Secure Shell (SSH). However, brute force attacks, which are automated attempts to guess repeated username and password combinations, are often the main target of this service. The purpose of this research is to evaluate the vulnerability of SSH services to brute force attacks and evaluate how effective Fail2Ban is as a mitigation tool. This method uses simulated experiments with two virtual machines; Kali Linux is used as the attacker with the Hydra tool and Ubuntu Server is used as the target. The results show that SSH is highly vulnerable to brute force attacks if not protected. Hundreds of login failures from the same IP without restrictions show this. After using Fail2Ban, the system can automatically find and block the attacker IP after three unsuccessful logins. With easy configuration and fast threat detection, Fail2Ban has been proven to improve the security of SSH services. This study suggests using Fail2Ban to improve the defense of SSH services against cyberattacks, especially brute force attacks.

This study aims to identify evidence of Address Resolution Protocol (ARP) Spoofing attacks in the form of the attacker's and victim's Media Access Control (MAC) addresses, as well as the time of the attack. With the increasing use of computer networks, ARP Spoofing attacks have become a serious threat that can lead to data theft, communication interception, and service disruption. ARP Spoofing can serve as a means to launch more complex attacks, such as Denial of Service (DoS) and Man in the Middle (MITM), which can cripple network systems and steal sensitive information. This study utilizes the Wireshark tool to monitor network traffic, specifically ARP, and applies the National Institute of Justice (NIJ) method as a framework for forensic investigation. The NIJ method was chosen because it provides a systematic approach to identifying, collecting, analyzing, and reporting digital evidence, enabling enhanced attack mitigation and supporting legal aspects in network security investigations. The specific approaches used in this forensic analysis include log analysis, packet capture and analysis using Wireshark, and traffic correlation to identify attack patterns based on time and involved devices. The attack simulation was conducted on Personal Computer (PC) 1 and a routerboard, where communication between these two devices was redirected through the attacker. Based on the test results, it was found that every device in the network experienced ARP Spoofing attacks, which could be detected and analyzed using the NIJ method. The contribution of this study is to provide a more systematic approach to forensic network investigations using the NIJ method, which not only aids in attack detection but also establishes a strong foundation for mitigation actions and legal enforcement in computer network security.

Cybersecurity is continuously challenged by increasingly sophisticated and dynamic cyber-attacks, necessitating advanced adaptive defense mechanisms. Deep Reinforcement Learning (DRL) has emerged as a promising approach, offering significant advantages over traditional intrusion detection methods through real-time adaptability and self-learning capabilities. This paper presents an advanced adaptive cybersecurity framework utilizing five prominent DRL algorithms: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Twin Delayed DDPG (TD3), Soft Actor-Critic (SAC), and Asynchronous Advantage Actor-Critic (A3C). The effectiveness of these algorithms is evaluated within complex, realistic simulation environments using live-streaming data, emphasizing key metrics such as accuracy (AUC-ROC), response latency, and network throughput. Experimental results demonstrate that the SAC algorithm consistently achieves superior detection accuracy (95% AUC-ROC) and minimal disruption to network performance compared to other approaches. Additionally, A3C provides the fastest response times suitable for real-time defense scenarios. This comprehensive comparative analysis addresses critical research gaps by integrating both traditional and novel DRL techniques and validates their potential to substantially improve cybersecurity defense strategies in realistic operational settings.

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges, necessitating robust attack detection mechanisms. This study presents a comprehensive comparative analysis of ten supervised learning algorithms for IoT attack detection and classification, addressing the critical challenge of balancing detection accuracy with practical deployment constraints. Using the CICIoT2023 dataset, encompassing data from 105 IoT devices and 33 attack types, we evaluate Naive Bayes, Artificial Neural Networks (ANN), Logistic Regression (LR), k-NN, XGBoost, Random Forest (RF), LightGBM, GRU, LSTM, and CNN algorithms based on some performance metrics. The comparative test results show superior performance to the traditional ensemble approach, with RF achieving 99.29% accuracy and leading precision (82.30%), followed closely by XGBoost with 99.26% accuracy and 79.60% precision. Deep learning approaches also demonstrate strong capabilities, with CNN achieving 98.33% accuracy and 71.18% precision, though these metrics indicate ongoing challenges with class imbalance. The analysis of confusion matrices reveals varying success across different attack types, with some algorithms showing perfect detection rates for certain attacks while struggling with others. The study highlights a crucial distinction in IoT security: while high precision remains important, the potentially catastrophic impact of missed attacks necessitates equal attention to recall metrics, as evidenced by the varying recall rates across algorithms (RF: 72.19%, XGBoost: 71.69%, CNN: 64.72%). These findings provide vital insights for developing balanced, context-aware intrusion detection systems for IoT environments, emphasizing the need to consider performance metrics and practical deployment constraints.

This research proposes a time-based dynamic port knocking technique on Mikrotik routers to enhance SSH access security. The system uses an algorithm for port calculation that changes according to the server’s time, requiring a different knocking sequence for each access session. The technique involves three consecutive knocking stages, where each stage must be passed in order for SSH access to be granted. The results show that this method is effective as a two-factor authentication mechanism, limiting access only to clients who understand the port calculation algorithm. Moreover, this system helps prevent brute-force attacks more efficiently by changing the port every minute, making it more difficult for unauthorized parties to launch an attack. Therefore, this method offers a more dynamic, adaptive, and flexible SSH security solution that can effectively address modern cybersecurity threats.

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.

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.  

WiFi networks are very crucial in today's corporate industry. Because the WiFi network contains the internet which acts as a data transfer and communication route to support the company's operations. PT. Bukit Baiduri Energi (PT. BBE) as a company operating in the coal mining sector relies heavily on the internet to carry out company operations. Every day there is always an exchange of data and communication from employees to monitor the process of carrying out existing tasks and work, in responding to problems faced during the internship at PT. Bukit Baiduri Energi, our team takes the right steps to find the optimal solution. The first step taken was to identify the company's needs and formulate the idea of ​​creating a web company profile as a Mikrotik Hotspot captive portal. The website development process involves utilizing HTML, CSS, and JavaScript to create an attractive appearance and ensure functionality. The results of the project during the internship were to create a web company profile as a captive portal for PT Mikrotik Hotspot. Bukit Baiduri Energy. During the internship period at PT BBE, our team succeeded in completing the captive login hotspot project on Mikrotik devices by changing the design of the hotspot login portal and adding a brief company profile page. This project provides important benefits for companies in improving the security of their Wi-Fi networks and introducing the company to users through profile pages. The result is a positive step in supporting the growth and image of companies in the coal mining sector.

Internet connections that are not always stable and data security is not maintained from various threats from outside attacks in the form of hackers, crackers, viruses, and so on are the main problems for every client. Using the pfSense analysis method, this study aims to address the above bandwidth management and network security issues. The pfSense analysis that the researchers carried out included an analysis of total bandwidth usage, data security, and information for each client on the P3DW Internet network (Center for Regional Revenue Management) Bandung III Soekarno-Hatta City. The results of the analysis concluded that pfSense can work as a firewall that functions as a bandwidth limiter and network security. The level of client satisfaction increases with the infrastructure designs being tested. With a web GUI that makes it easy for users to configure, the basic system used is FreeBSD which is open-source, making pfSense able to be an alternative choice for building a firewall that is reliable, flexible, and economical.