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Journal : Indonesian Journal of Electrical Engineering and Computer Science

Threat modeling in application security planning citizen service complaints Agus Tedyyana; Fajar Ratnawati; Elgamar Syam; Fajri Profesio Putra
Indonesian Journal of Electrical Engineering and Computer Science Vol 28, No 2: November 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v28.i2.pp1020-1027

Abstract

The mobile-based service complaint application is one way to implement good governance today. Public facilitated to make complaints without going through a complicated process. Security aspects must be considered to protect user privacy. The security design must be considered so that no one is harmed by the application's users damaged in the application's use. This study used threat modeling during the planning stage of developing a citizen service complaint application to obtain information about vulnerabilities. The researcher uses the threat modeling process that the open web application security project (OWASP) organization has formulated as a framework. The researchers took steps to describe application information, determine and rank threats, countermeasures, and mitigation. In the final stage, the spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege (STRIDE) threat modeling methodology is used to analyze and assess mitigation actions against threats in the application. The researcher gets a defense strategy to reduce the danger based on the threat analysis results. Threat modeling in the early phase software development life cycle process is constructive in ensuring that software is developed with adequate security based on threat mitigation from the beginning.
Machine learning for network defense: automated DDoS detection with telegram notification integration Agus Tedyyana; Osman Ghazali; Onno W. Purbo
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1102-1109

Abstract

As the prevalence and sophistication of distributed denial of service (DDoS) attacks escalate, the imperative for advanced defense mechanisms becomes paramount, especially in rapidly growing digital landscapes like Indonesia. This research presents the development of an innovative intrusion detection system (IDS) that harnesses machine learning (ML) algorithms to automate the detection of DDoS attacks in real time. By monitoring TCP streams, the system utilizes ML-enhanced IDS components to identify malicious traffic patterns indicative of DDoS activities. An automatic alert is dispatched to network administrators via Telegram upon detection, ensuring immediate awareness and facilitating swift countermeasures. Additionally, the system embodies a self-improving architecture by retraining its ML model with newly encountered attack data, thus continuously refining its detection capabilities. The system's efficacy, marked by its adaptive learning and proactive notification system, not only contributes to the fortification of network security but also underscores the potential for ML in cybersecurity within Indonesia’s expanding digital domain. The deployment of this system is anticipated to significantly bolster cybersecurity infrastructure by addressing the urgent need for advanced and responsive defense strategies against the evolving landscape of cyber threats.