Kajian Literatur Solusi Pencegahan Malware Email Berbasis Perangkat Lunak dan Jaringan
Keywords:
Email Malware, Literature Review, Malware Prevention, Network Security, Software SolutionsAbstract
Email has become the backbone of digital communication, yet it simultaneously serves as a primary vector for malware dissemination with devastating impacts. The increasing complexity and volume of malware attacks via email, such as phishing, ransomware, and spyware, inflict significant losses on individuals and organizations, ranging from data breaches and financial losses to operational system disruptions. Therefore, this study aims to comprehensively map and analyze various email malware prevention solutions from two crucial perspectives: the software side and the network side. The methodology employed in this research was a systematic literature review of relevant contemporary scholarly publications. Data analysis was conducted by categorizing and synthesizing findings from selected journals concerning detection and mitigation techniques. The results of this study have demonstrated that email malware prevention necessitates a multi-layered and integrated approach. From the software perspective, techniques such as static and dynamic analysis, behavior-based detection, and the application of machine learning and deep learning algorithms to email content and attachments have proven effective. Meanwhile, from the network side, solutions like Mail Gateway Security, Intrusion Prevention Systems (IPS), firewalls, and network flow analysis play a vital role in blocking threats before they reach endpoints. The synergy between these two approaches is essential for building robust cybersecurity defenses.
References
[1] R. Sumargo dan H. Santoso, "Uncovering Malware Families Using Convolutional Neural Networks (CNN)," Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 7, no. 1, hlm. 97–103, 2024.
[2] U. W. Oluchukwu, A. S. Okwudili, A. D. Chinedu, E. C. Asogwa, dan A. K. Sylvanus, "Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review," European Journal of Theoretical and Applied Sciences, vol. 2, no. 2, hlm. 76–86, 2024.
[3] L. Á. Redondo-Gutierrez et al., "Detecting malware using text documents extracted from spam email through machine learning," dalam Proceedings of the 2022 ACM Document Engineering Symposium (DocEng '22), New York, NY, USA, 2022, hlm. 1–2.
[4] F. Jáñez-Martino et al., "Spam email classification based on cybersecurity potential risk using natural language processing," Knowledge-Based Systems, vol. 310, hlm. 112939, 2025, doi: 10.1016/j.knosys.2024.112939.
[5] S. Talukder dan Z. Talukder, "A Survey on Malware Detection and Analysis Tools," International Journal of Network Security & Its Applications (IJNSA), vol. 12, no. 2, hlm. 43–57, 2020.
[6] B. Konduri et al., "Advancements and Challenges in Email Spam and Malware Filtering Utilizing AI and Machine Learning," International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 4, hlm. 378–381, 2024, doi: 10.17148/IJARCCE.2024.13483.
[7] H. Rajeev dan M. Chakkravarthy, "Detection of Malware using Phishing Alarm," Indian Journal of Artificial Intelligence and Neural Networking (IJAINN), vol. 4, no. 1, hlm. 1–4, 2023, doi: 10.54105/ijainn.A1077.124123.
[8] K. Thakur, M. L. Ali, M. A. Obaidat, dan A. Kamruzzaman, "A Systematic Review on Deep-Learning-Based Phishing Email Detection," Electronics, vol. 12, no. 21, hlm. 4545, 2023, doi: 10.3390/electronics12214545.
[9] O. Senouci dan N. Benaouda, "Enhancing Phishing Detection in Cloud Environments Using RNN-LSTM in a Deep Learning Framework," Journal of Telecommunications and Information Technology, vol. 1, no. 1, hlm. 1–9, 2025, doi: 10.26636/jtit.2025.1.1916.
[10] Q. Abu Al-Haija, A. Odeh, dan H. Qattous, "PDF Malware Detection Based on Optimizable Decision Trees," Electronics, vol. 11, no. 19, hlm. 3142, 2022, doi: 10.3390/electronics11193142.
[11] H. Zhang et al., "A combined feature selection approach for malicious email detection based on a comprehensive email dataset," Cybersecurity, vol. 8, no. 1, hlm. 1–14, 2025, doi: 10.1186/s42400-024-00309-6.
[12] S. E. Adewumi dan U. D. Ani, "Impact of detection accuracy rates on phishing email spikes: Towards more effective mitigation," Information Security Journal: A Global Perspective, vol. 0, no. 0, hlm. 1–18, 2025, doi: 10.1080/19393555.2025.2469519.
[13] S. A. Aklani, H. Haeruddin, dan N. Putri, "Implementasi Mail Gateway Security dalam Meningkatkan Keamanan Email," Journal of Information System Management (JOISM), vol. 5, no. 2, 2024.
[14] T. Rahmawati, N. Karna, S. Y. Shin, dan M. A. P. Putra, "Enhancing Network Security Through Real-Time Threat Detection with Intrusion Prevention System (Case Study on Web Attack)," Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), vol. 10, no. 4, hlm. 1004–1020, 2024, doi: 10.26555/jiteki.v10i4.30380.
[15] J. Busch dan V. Tresp, "NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification," arXiv preprint arXiv:2103.03939, 2021.
[16] M. Naseer et al., "Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data," Sensors, vol. 25, no. 1, hlm. 202, 2025, doi: 10.3390/s25010202.
[17] A. Krajewska dan E. Niewiadomska-Szynkiewicz, "Clustering Network Traffic Using Semi-Supervised Learning," Electronics, vol. 13, no. 1, hlm. 94, 2024, doi: 10.3390/electronics13010094.
[18] M. Aljabri et al., "Intelligent Techniques for Detecting Network Attacks: Review and Research Directions," Sensors, vol. 21, no. 21, hlm. 7070, 2021, doi: 10.3390/s21217070.
[19] Y. E. Suzuki dan S. A. S. Monroy, "Prevention and mitigation measures against phishing emails: a sequential schema model," Security Journal, vol. 35, hlm. 1162–1182, 2022, doi: 10.1057/s41284-021-00318-x.
[20] A. H. Salem et al., "Advancing cybersecurity: a comprehensive review of AI-driven detection techniques," Journal of Big Data, vol. 11, no.1, hlm. 105, 2024, doi: 10.1186/s40537-024-00957-y.
[21] P. Maniriho, A. N. Mahmood, dan M. J. M. Chowdhury, "A study on malicious software behaviour analysis and detection techniques: Taxonomy, current trends and challenges," Future Generation Computer Systems, vol. 130, hlm. 1–18, 2022, doi: 10.1016/j.fgcs.2021.11.018.
Downloads
Published
Issue
Section
License
All materials contained within this journal are protected by Intellectual Property Corporation of Malaysia, Copyright Act 1987 and may not be reproduced, distributed, transmitted, displayed, published, or
broadcast without the prior, express written permission of Centre for Graduate Studies, Universiti Selangor, Malaysia. You may not alter or remove any copyright or other notice from copies of this content.
