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All Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Monas: Jurnal Inovasi Aparatur
Maman Firmansyah
Universitas Nusa Mandiri
Computer Science & IT Decision Sciences, Operations Research & Management Education Environmental Science Social Sciences Other

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Knowledge management for information security incident handling at Security Operation Center of Jakarta Provincial Government Maman Firmansyah; Andrie Yuswanto
Monas: Jurnal Inovasi Aparatur Vol. 4 No. 2 (2022): November
Publisher : Badan Pengembangan Sumber Daya Manusia Provinsi DKI Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54849/monas.v4i2.102

Abstract

Information security incidents have increased in number and become more diverse and destructive and disrupt service availability. An incident management system is needed to detect and handle information security incidents quickly, minimize losses, reduce exploited vulnerabilities and restore infrastructure, including services. An incident management system needs to be managed with a Security Operations Center (SOC). The use of tacit knowledge has been shown to help accelerate problem-solving in SOC better than experience by adopting strategies that have been used previously. The application of knowledge management in SOC has become a basic need. An organization's ability to manage existing knowledge is a necessary strength to be able to survive in the face of incessant cyber-attacks. This study aims to examine the process of capturing tacit in SOC so that it can be used to analyze and deal with cyber threats and to lay the foundation for implicit knowledge management in organizations to increase the efficiency of work methods and processes responding to incidents efficiently and systematically.
IMPLEMENTASI METODE ADASYN DALAM DETEKSI URL BERBAHAYA MENGGUNAKAN MACHINE LEARNING: DEMI MENINGKATKAN KEAMANAN SIBER DI ERA DIGITAL Gilang Dwi Setyawan; Andrie Yuswanto; Ahmad Maulid Ridwan; Budi Wibowo; Maman Firmansyah
TEKNOKOM Vol. 6 No. 2 (2023): TEKNOKOM
Publisher : Department of Computer Engineering, Universitas Wiralodra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31943/teknokom.v6i2.153

Abstract

Cybercriminals exploit malicious URLs as a distribution channel to spread harmful software across the internet. They take advantage of vulnerabilities in browsers to install malicious software with the aim of gaining remote access to the victims' computers. Typically, this malicious software aims to gain access to networks, steal sensitive information, and silently monitor targeted computer systems. In this research, a data mining approach known as Classification Based on Association (CBA) is employed to detect malicious URLs using both the URL itself and the features of the presented web pages. The CBA algorithm utilizes a training dataset of URLs as historical data to discover association rules that can be used to create an accurate classifier. By detecting dangerous URLs and malicious software, this contribution can assist organizations and individual users in enhancing the security of their computer systems and networks, thereby protecting sensitive data and reducing the risk of security incidents. The experimental results demonstrate that CBA achieves performance on par with tested classification algorithms, achieving an accuracy of 99% and low rates of false positives and false negatives. Future research could expand its focus to detect malicious URLs and software on mobile devices and embedded systems, as they have become significant targets for cybercriminals.
Co-Authors Ahmad Maulid Ridwan Andrie Yuswanto Gilang Dwi Setyawan