IMPLEMENTATION OF DYNAMIC METHOD FOR MALWARE DETECTION IN EMAIL PHISHING ATTACKS ON LET'S DEFEND
DOI:
https://doi.org/10.59407/jdaics.v1i2.717Keywords:
Cybersecurity, Dynamic analysis, Malware detection, PhishingAbstract
In the rapidly evolving landscape of cybersecurity threats, the need for robust defenses against phishing attacks has become paramount. This study explores the efficacy of malware detection in phishing email attacks using dynamic analysis on the Letsdefend.io platform. Leveraging the insights provided by the Deloitte Center for Controllership regarding the escalating frequency of cyber adversaries targeting organizational data, this research investigates the effectiveness of the Letsdefend.io platform, particularly utilizing the SOC 146 rule, in identifying and mitigating phishing threats. Through a comprehensive analysis process encompassing dynamic malware analysis techniques, such as those employed by VirusTotal and URLHaus, alongside detailed examination of suspicious email attachments using the Mailbox feature, this study aims to provide insights into the evolving tactics of phishing attackers, specifically those utilizing Excel 4.0 Macros. The research methodology involves collecting malware samples for analysis, configuring sandbox environments with tools like Process Monitor and Regshot, and utilizing sophisticated analysis tools like ProcDot to visualize malware behavior. Additionally, the study examines the effectiveness of the Letsdefend.io platform in detecting phishing URLs and malicious domains reported by AnyRun and URLHaus databases. The findings reveal promising results in the detection and identification of phishing threats, shedding light on the potential of dynamic analysis methods in bolstering cybersecurity defenses against evolving phishing techniques. This research contributes to the ongoing efforts to enhance cybersecurity measures and protect organizational assets from the pervasive threat of phishing attacks.
References
Abroshan, H., Devos, J., Poels, G., & Laermans, E. (2018). Phishing attacks root causes. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Vol. 10694 LNCS. Springer International Publishing. https://doi.org/10.1007/978-3-319-76687-4_13
Admass, W. S., Munaye, Y. Y., & Diro, A. A. (2024). Cyber security: State of the art, challenges and future directions. Cyber Security and Applications, 2, 100031. https://doi.org/https://doi.org/10.1016/j.csa.2023.100031
Barrera, D., Naranjo, V., Fuertes, W., & Macas, M. (2024). Literature Review of SMS Phishing Attacks: Lessons, Addresses, and Future Challenges BT - Advanced Research in Technologies, Information, Innovation and Sustainability (T. Guarda, F. Portela, & J. M. Diaz-Nafria (eds.); pp. 191–204). Springer Nature Switzerland.
Belgaum, M. R., Alansari, Z., Jain, R., & Alshaer, J. (2018). A framework for evaluation of cyber security challenges in smart cities. Smart Cities Symposium 2018, 1–6.
Bountakas, P., & Xenakis, C. (2023). HELPHED: Hybrid Ensemble Learning PHishing Email Detection. Journal of Network and Computer Applications, 210, 103545. https://doi.org/https://doi.org/10.1016/j.jnca.2022.103545
Burita, L., Matoulek, P., Halouzka, K., & Kozak, P. (2021). Analysis of phishing emails. AIMS Electronics and Electrical Engineering, 5(1), 93–116. https://doi.org/10.3934/ELECTRENG.2021006
Candraditya Pamungkas, W., & Trimuti Saputra, F. (2020). Analisa Mobile Phishing Dengan Incident Response Plan dan Incident Handling. Jurnal Riset Komputer), 7(4), 2407–389. https://doi.org/10.30865/jurikom.v7i4.2304
Caniago, K., & Sutabri, T. (2023). Tindak Kejahatan Phising Di Sektor Pelayanan Di Universitas Bina Insan Lubuklinggau. Jurnal Riset Sistem Informasi Dan Teknik Informasi, 8(1), 117–125.
Chen, Y., Ding, Z., & Wagner, D. (2023). Continuous learning for android malware detection. 32nd USENIX Security Symposium (USENIX Security 23), 1127–1144.
Chuck Brooks. (2023). Tren & Statistik Keamanan Siber Untuk 2023. Website Forbes.
Dwiyani Permatasari. (2021). Tantangan Cyber Security di Era Revolusi Industri 4.0. Kementrian Keuangan Republik Indonesia.
Fahriza, C. F. (2022). Analisis Ransomware Secara Statis dan Dinamis Untuk Pemetaan Evolusi Ransomware Analisis Ransomware Secara Statis dan Dinamis Untuk Pemetaan Evolusi Ra nsomware.
Gorment, N. Z., Selamat, A., Cheng, L. K., & Krejcar, O. (2023). Machine Learning Algorithm for Malware Detection: Taxonomy, Current Challenges, and Future Directions. IEEE Access, 11, 141045–141089. https://doi.org/10.1109/ACCESS.2023.3256979
Gunes, B., Kayisoglu, G., & Bolat, P. (2021). Cyber security risk assessment for seaports: A case study of a container port. Computers & Security, 103, 102196.
Habibzadeh, H., Nussbaum, B. H., Anjomshoa, F., Kantarci, B., & Soyata, T. (2019). A survey on cybersecurity, data privacy, and policy issues in cyber-physical system deployments in smart cities. Sustainable Cities and Society, 50, 101660.
Hidayat, W., Ramli, H., Ikhram, P. M. B., & ... (2023). Analisa Clustering Phising Untuk Meningkatkan Kesadaran Mahasiswa Terhadap Keamanan Data Pribadi Mahasiswa Universitas Negeri Makassar. Vokatek: Jurnal …, 01, 28–33.
Ilhami, D. A. S. (2022). Data privasi dan keamanan siber pada smart-city: Tinjauan literatur. Jurnal Sains, Nalar, Dan Aplikasi Teknologi Informasi, 2(1), 51–60.
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2023). Towards insighting cybersecurity for healthcare domains: A comprehensive review of recent practices and trends. Cyber Security and Applications, 1, 100016. https://doi.org/https://doi.org/10.1016/j.csa.2023.100016
M., G., & Sethuraman, S. C. (2023). A comprehensive survey on deep learning based malware detection techniques. Computer Science Review, 47, 100529. https://doi.org/https://doi.org/10.1016/j.cosrev.2022.100529
Ma, C. (2021). Smart city and cyber-security; technologies used, leading challenges and future recommendations. Energy Reports, 7, 7999–8012.
Putra, R. C. (2023). Analisis email phising dan karakteristik malware di kementrian komunikasi dan informatika.
Putri, Y. H. Z. M. N. I. (2021). Mitigasi Ancaman Resiko Keamanan Siber Di Masa Pandemi Covid-19. Jurnal ICT: Information Communication & Technology, Vol. 21 No. 1 (2021): JICT-IKMI, Juli 2021, 42–52.
Qamar, T., & Bawany, N. Z. (2020). A cyber security ontology for smart city. International Journal on Information Technologies & Security, 12(3), 63–74.
Ramadhan, I. (2019). Strategi Keamanan Cyber Security di Kawasan Asia Tenggara. Jurnal Asia Pacific Studies, 3(2), 181–192.
Safi, A., & Singh, S. (2023). A systematic literature review on phishing website detection techniques. Journal of King Saud University - Computer and Information Sciences, 35(2), 590–611. https://doi.org/https://doi.org/10.1016/j.jksuci.2023.01.004
Shaukat, K., Luo, S., & Varadharajan, V. (2023). A novel deep learning-based approach for malware detection. Engineering Applications of Artificial Intelligence, 122, 106030. https://doi.org/https://doi.org/10.1016/j.engappai.2023.106030
Silalahi, F. D. (2022). Keamanan Cyber (Cyber Security). Penerbit Yayasan Prima Agus Teknik, 1–285.
Sookhak, M., Tang, H., He, Y., & Yu, F. R. (2018). Security and privacy of smart cities: a survey, research issues and challenges. IEEE Communications Surveys & Tutorials, 21(2), 1718–1743.
Syah, R. (2023). Strategi Kepolisian Dalam Pencegahan Kejahatan Phising Melalui Media Sosial Di Ruang Siber. Jurnal Impresi Indonesia, 2(9), 864–870.
Venkatasubramanian, M., Lashkari, A. H., & Hakak, S. (2023). IoT Malware Analysis Using Federated Learning: A Comprehensive Survey. IEEE Access, 11, 5004–5018. https://doi.org/10.1109/ACCESS.2023.3235389
Vitunskaite, M., He, Y., Brandstetter, T., & Janicke, H. (2019). Smart cities and cyber security: Are we there yet? A comparative study on the role of standards, third party risk management and security ownership. Computers & Security, 83, 313–331.
