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All Journal KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Enrichment: Journal of Multidisciplinary Research and Development
Rimbawa, Danang
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Humanities Computer Science & IT Economics, Econometrics & Finance Engineering Health Professions Social Sciences

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Studi Pustaka: Optimalisasi Deteksi Malware melalui Integrasi Pembelajaran Mesin Heuristik dan Big Data untuk Keamanan Siber Supriyadi, Devi; Wahyudi, Bisyron; Rimbawa, Danang
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 14 No 1 (2025): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v14i1.15595

Abstract

The increasingly complex and dynamic threat of malware drives the need for a more adaptive detection strategy than conventional signature-based methods. This study aims to evaluate the effectiveness of machine learning, heuristics, and big data approaches in detecting modern malware. The main problem raised is the limitation of traditional methods in identifying new malware variants, especially those that use obfuscation techniques such as polymorphism and metamorphism. Using a systematic literature study approach to the 2016-2024 literature from various reputable sources, this study compares the performance of each approach based on accuracy, efficiency, and resistance to adversarial attacks. The results of the analysis show that deep learning models such as the Convolutional Neural Network (CNN) have the highest detection accuracy, while heuristic methods excel in initial detection efficiency, and big data provides advantages in the scalability of real-time detection systems. This study concludes that the hybrid integration of these three approaches has the potential to create a malware detection system that is more adaptive and resilient to cyberattacks, although further empirical validation is still needed for real-world implementation.
Addressing SIM Card and IMEI Security Vulnerabilities in Preventing Illegal Online Activities by Using Elliptic Curve Cryptography Setyowati, Danny; Rimbawa, Danang
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 4 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i4.453

Abstract

The telecommunication system in Indonesia faces serious challenges due to the misuse of SIM card data and devices with illegal IMEIs, as seen in the case of mass registration of prepaid cards using fake or stolen identities in 2018. This problem is further exacerbated by the circulation of black market (BM) phones that use unregistered IMEI to avoid network blocking. As a result, these security loopholes are used to support illegal activities such as account registration on online gambling platforms. This study proposes the application of Elliptic Curve Cryptography (ECC) as a solution to improve SIM card data security and IMEI validation. ECC provides efficient and secure encryption methods to protect data, verify device authentication, and block illegal activities through telecommunication systems. The main contribution of this research is the development of ECC-based systems that can prevent the misuse of SIM card and device data, support the validation of legitimate devices, and tighten control over network access. The evaluation shows that ECC technology can be applied effectively in improving telecommunication security in Indonesia.
Co-Authors Setyowati, Danny Supriyadi, Devi Wahyudi, Bisyron