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Computers in one of the network companies at PT. Estrada uses the Fortinet operating system. The final result expected through this implementation is to comprehensively see the capabilities of the firewall on Fortinet in overcoming the problem of blocking social media applications and streaming platforms during working hours. Blocking the application in question is the ability to filter web processes such as Facebook, Instagram, YouTube, etc. In the tests carried out, web filtering was able to block applications on social media and streaming platforms, which proves that the performance of web filtering is quite good. In analyzing web filtering performance, use the office hour rule tool by carrying out the rule schedule in the Fortinet network and displaying all the information in detail. The final result obtained in the network application filtering simulation process using Fortinet is that every network sent cannot be entered (blocked) on both social media applications and streaming platforms.

The increasing use of web applications in various sectors has made security a major concern due to the high risk of attacks targeting the application layer. This study aims to analyze the effectiveness of the F5 Web Application Firewall (F5 WAF) in mitigating vulnerabilities in the OWASP Juice Shop application. Testing was conducted using 10 attack scenarios including SQL Injection, Cross-Site Scripting (XSS), Insecure Direct Object Reference (IDOR), and Information Disclosure before and after F5 WAF implementation. The test results showed that all tested attack scenarios were successfully mitigated after F5 WAF implementation. These findings indicate that the F5 WAF is able to provide effective protection against various vulnerabilities at the application layer and improve the security of web applications in the test environment used.

APIs (Application Programming Interfaces) have become a key component in the development of modern digital products and the transformation of cloud-based services. Its ability to provide structured access to data and enable cross-platform integration makes APIs at the core of the enterprise's digital architecture. However, the high level of API openness poses increasingly complex security challenges, including potential data exploitation, injection attacks, credential misuse, and exploitation of business logic loopholes. This article examines the strategic role of APIs in the digital ecosystem, analyzes the operational risks that arise from API exposure, and evaluates the effectiveness of basic defense mechanisms such as API Gateways and Web Application Firewalls (WAFs). The findings of the study show that while both solutions play an important role in controlling access, filtering, and mitigating attacks at the surface layer, they have not been able to provide comprehensive protection against modern API threats that are dynamic, distributed, and often exploit weaknesses at the application and business logic levels. Therefore, a more holistic, layered, and sustainable API security approach is needed, including anomalous behavior detection, API abuse protection, and real-time monitoring to maintain the integrity and reliability of digital services.  

The rapid growth of web technologies and online services has increased the exposure of web applications to cyber threats such as Cross-Site Scripting (XSS) and SQL Injection (SQLi). Conventional rule-based mechanisms, such as Web Application Firewalls (WAFs), often fail to detect emerging attack patterns. To address this, Machine Learning (ML) and Deep Learning (DL) have emerged as adaptive approaches for enhancing web attack detection. This study performs a Systematic Literature Review (SLR) following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to analyze recent ML/DL-based detection methods. Of the 263 retrieved studies, 15 met the inclusion criteria for detailed review. The findings reveal that Random Forest (RF), Support Vector Machine (SVM), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) are the most applied algorithms. At the same time, recent works emphasize Transformer-based and hybrid ML–DL models. These approaches achieved robust performance (accuracy 85–97%, F1-score >90%) but still face challenges in dataset representativeness, class imbalance, and computational cost. This review highlights future research directions in Explainable Artificial Intelligence (XAI), Federated Learning (FL), and adversarial robustness to develop more efficient and trustworthy web attack detection systems.

In the era of information technology advancement, web applications have become a means of seeking information. However, with technological progress, they have become increasingly vulnerable to cyber attacks such as SQL Injection and Cross-Site Scripting (XSS). This research aims to implement the Teler-waf Web Application Firewall (WAF) to protect web applications from such attacks. The research methodology includes the implementation of the Teler-waf WAF, analysis of web application security, and testing the speed of attack detection. The results show that Teler-waf is effective in preventing attacks, and its integration with Telegram bots provides real-time notifications to system administrators, enhancing security responsiveness. This research contributes to strengthening web application security and understanding the role of the Teler-waf WAF in addressing cyber threats.