Sistem Deteksi Intrusi Berbasis Deep Learning untuk Mitigasi Serangan Zero-Day pada Jaringan Komputer
Keywords:
Intrusion Detection System, Deep Learning, Zero-Day Attacks, CNN-LSTM, Network Security, Anomaly Detection.Abstract
Zero-day cyber attacks increasingly pose a risk to computer network security because they cannot be detected by conventional intrusion detection systems (IDS) that only utilize signature patterns. This study aims to design an intrusion detection system that uses deep learning, which can accurately and directly recognize and overcome zero-day attacks. The method applied is an experimental quantitative approach using the NSL-KDD and UNSW-NB15 datasets, through the stages of data processing, feature selection, and normalization. The model was trained with a hybrid CNN-LSTM architecture and tested by measuring accuracy, precision, recall, F1-score, and ROC-AUC. The results show that this system achieved 98.72% accuracy, 98.41% precision, 97.95% recall, and 98.18% F1-score, and was able to reduce the false positive rate to 12% compared to signature-based IDS. This system also has an average response time of 0.84 seconds, making it suitable for use in real-time networks. Therefore, this deep learning-based intrusion detection system is considered successful in detecting zero-day attacks in an adaptive and efficient manner. However, the need for large computational resources is a challenge that needs to be overcome through further development, such as the integration of edge computing or federated learning, to make the system lighter and easier to develop.
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REFERENSI
[1] F. P. Eka Putra, . S., A. Ramadhani, and . M., “Integrasi Teknologi Kuantum dan fiber Optik untuk Meningkatkan Keamanan dan Efisiensi Jaringan Masa Depan,” J. Ilm. Ilk. - Ilmu Komput. Inform., vol. 8, no. 2, pp. 151–163, 2025, doi: 10.47324/ilkominfo.v8i2.342.
[2] Y. Singh and T. Walingo, “Smart Water Quality Monitoring with IoT Wireless Sensor Networks,” 2024. doi: 10.3390/s24092871.
[3] F. P. E. Putra, U. Ubaidi, R. O. F. Kusuma, A. M. Syam, and S. A. Efendy, “Effect Of Distance On Wi-Fi Signal Quality In The Home Environment,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 391–398, 2024, doi: 10.47709/brilliance.v4i1.4319.
[4] F. P. E. Putra, M. Aziz, G. Arifin, A. Rohman, A. Rizki, and A. M. Syam, “Analisis Qos & Qoe,” J. Syntax Admiration, vol. 5, no. 1, pp. 140–145, 2024, doi: 10.46799/jsa.v5i1.973.
[5] A. Rachmawardani et al., “Wireless Sensor Network (WSN) of a flood monitoring system based on the Internet of Things (IoT),” 2023. doi: 10.1051/e3sconf/202346401004.
[6] T. T. Nguyen and F. Mohammadi, “Cyber-Physical Power and Energy Systems with Wireless Sensor Networks: A Systematic Review,” 2023. doi: 10.1007/s42835-023-01482-3.
[7] A. A. Al-Atawi, “Extending the Energy Efficiency of Nodes in an Internet of Things (IoT) System via Robust Clustering Techniques,” 2023. doi: 10.22247/ijcna/2023/223685.
[8] R. Goyal, N. Mittal, L. Gupta, and A. Surana, “Routing Protocols in Wireless Body Area Networks: Architecture, Challenges, and Classification,” 2023. doi: 10.1155/2023/9229297.
[9] P. P. Danieli, N. F. Addeo, F. Lazzari, F. Manganello, and F. Bovera, “Precision Beekeeping Systems: State of the Art, Pros and Cons, and Their Application as Tools for Advancing the Beekeeping Sector,” 2024. doi: 10.3390/ani14010070.
[10] F. Al‐Quayed, Z. Ahmad, and M. Humayun, “A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0,” 2024. doi: 10.1109/ACCESS.2024.3372187.
[11] C. Cao, X. Zhang, C. Song, A. Georgiadis, and G. Goussetis, “A Highly Integrated Multipolarization Wideband Rectenna for Simultaneous Wireless Information and Power Transfer (SWIPT),” 2023. doi: 10.1109/TAP.2023.3306182.
[12] J. Kipongo, T. G. Swart, and E. Esenogho, “Design and Implementation of Intrusion Detection Systems using RPL and AOVD Protocols-based Wireless Sensor Networks,” 2023. doi: 10.24425/ijet.2023.144366.
[13] B. Meenakshi and D. Karunkuzhali, “Enhancing cyber security in WSN using optimized self-attention-based provisional variational auto-encoder generative adversarial network,” 2024. doi: 10.1016/j.csi.2023.103802.
[14] L. Sahoo, S. S. Sen, K. Tiwary, S. Moslem, and T. Senapati, “Improvement of Wireless Sensor Network Lifetime via Intelligent Clustering Under Uncertainty,” 2024. doi: 10.1109/ACCESS.2024.3365490.
[15] M. Alsharif, A. Jahid, A. Kelechi, and R. Kannadasan, “Green IoT: A Review and Future Research Directions,” 2023. doi: 10.3390/sym15030757.
[16] A. Khan and I. Sharma, “Leveraging Artificial Intelligence Methodologies to Improve WSN Security,” 2023. doi: 10.1109/GCAT59970.2023.10353254.
[17] M. Sajid et al., “Enhancing intrusion detection: a hybrid machine and deep learning approach,” 2024. doi: 10.1186/s13677-024-00685-x.
[18] H. Moudoud, Z. A. El Houda, and B. Brik, “Advancing Security and Trust in WSNs: A Federated Multi-Agent Deep Reinforcement Learning Approach,” 2024. doi: 10.1109/TCE.2024.3440178.
[19] M. Khofikur R.A, F. P. Eka Putra, M. W. Ridho G, and V. Huda, “Analisis Kinerja dan Keamanan Protokol PPTP dan L2TP/IPSec VPN pada Jaringan MikroTik,” Infotek J. Inform. dan Teknol., vol. 8, no. 2, pp. 334–344, 2025, doi: 10.29408/jit.v8i2.30230.
[20] F. P. Eka Putra, Amir Hamzah, W. Agel, and R. O. Firmansyah Kusuma, “Impelementasi Sistem Keamanan Jaringan Mikrotik Menggunakan Firewall Filtering dan Port Knocking,” J. Sistim Inf. dan Teknol., pp. 82–87, 2024, doi: 10.60083/jsisfotek.v5i4.329.
[21] T. Alahmad, M. Neményi, and A. Nyéki, “Applying IoT Sensors and Big Data to Improve Precision Crop Production: A Review,” 2023. doi: 10.3390/agronomy13102603.
[22] M. Devika and S. M. Shaby, “Optimizing Wireless Sensor Networks: A Deep Reinforcement Learning-Assisted Butterfly Optimization Algorithm in MOD-LEACH Routing for Enhanced Energy Efficiency,” 2024. doi: 10.22399/ijcesen.708.
[23] R. Alharthi, “Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection,” 2024. doi: 10.3389/fenrg.2024.1491332.
[24] R. Khan, U. Saeed, and I. Koo, “FedLSTM: A Federated Learning Framework for Sensor Fault Detection in Wireless Sensor Networks,” 2024. doi: 10.3390/electronics13244907.
[25] K. Fang, J. Chen, H. Zhu, T. R. Gadekallu, X. Wu, and W. Wang, “Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers,” 2024. doi: 10.1007/s11432-023-3968-9.
[26] S. Qin and X. Guo, “IoT Edge-Computing-Enabled Efficient Localization via Robust Optimal Estimation,” 2023. doi: 10.1109/JIOT.2022.3200095.
[27] R. Anwit, P. K. Jana, and M. S. Obaidat, “Obstacle Adaptive Smooth Path Planning for Mobile Data Collector in the Internet of Things,” 2023. doi: 10.1109/TSUSC.2023.3281886.
[28] S. Madhavi, N. C. Santhosh, S. Rajkumar, and R. Praveen, “Pythagorean Fuzzy Sets-based VIKOR and TOPSIS-based multi-criteria decision-making model for mitigating resource deletion attacks in WSNs,” 2023. doi: 10.3233/jifs-224141.
[29] F. P. Eka Putra, L. Fitriyah, Z. Naimah, and S. A. Rofika, “Evaluasi Kinerja Aplikasi Wireshark Dalam Monitoring Jaringan Kecil Dengan Topologi Star dan Bus,” J. Ilm. Ilk. - Ilmu Komput. Inform., vol. 8, no. 2, pp. 164–176, 2025, doi: 10.47324/ilkominfo.v8i2.343.
[30] M. Matar, T. Xia, K. D. Huguenard, D. Huston, and S. Wshah, “Anomaly Detection in Coastal Wireless Sensors via Efficient Deep Sequential Learning,” 2023. doi: 10.1109/ACCESS.2023.3322370.
[31] A. F. Rachman, F. P. E. Putra, S. Syirofi, and D. Wahid, “Case Study of Computer Network Development for the Internet Of Things (IoT) Industry in an Urban Environment,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 399–407, 2024, doi: 10.47709/brilliance.v4i1.4302.
[32] P. Wasnik and N. Chavhan, “A Review Paper on Designing Intelligent Intrusion Detection System Using Deep Learning,” 2023. doi: 10.1109/ICETET-SIP58143.2023.10151563.
[33] F. P. Eka Putra, A. Muzayyin, and M. U. Mansyur, “ANALISIS KUALITAS LAYANAN ABSENSI BERBASIS FINGER BERDASARKAN Quality of Service,” J. Inform., vol. 24, no. 1, pp. 17–25, 2024, doi: 10.30873/ji.v24i1.3949.
[34] F. P. E. Putra, K. Mufidah, R. M. Ilhamsyah, S. A. Efendy, and S. N. R. Barokah, “Tinjauan Performa RouterOS Mikrotik dalam Jaringan Internet: Analisis Kinerja dan Kelayakan,” Digit. Transform. Technol., vol. 3, no. 2, pp. 903–910, 2024, doi: 10.47709/digitech.v3i2.3446.
[35] M. Matar, T. Xia, K. D. Huguenard, D. Huston, and S. Wshah, “Multi-Head Attention based Bi-LSTM for Anomaly Detection in Multivariate Time-Series of WSN,” 2023. doi: 10.1109/AICAS57966.2023.10168670.
[36] R. Amutha, G. G. Sivasankari, and K. R. Venugopal, “Node clustering and data aggregation in wireless sensor network using sailfish optimization,” 2023. doi: 10.1007/s11042-023-15225-z.
[37] F. P. E. Putra, U. Ubaidi, A. B. Tamam, and R. W. Efendi, “Implementation And Simulation Of Dynamic Arp Inspection In Cisco Packet Tracer For Network Security,” Brill. Res. Artif. Intell., vol. 4, no. 1, pp. 340–347, 2024, doi: 10.47709/brilliance.v4i1.4199.
[38] M. A. Talukder, S. Sharmin, M. A. Uddin, M. M. Islam, and S. Aryal, “MLSTL-WSN: machine learning-based intrusion detection using SMOTETomek in WSNs,” 2024. doi: 10.1007/s10207-024-00833-z.
[39] H. Kang, J. Yoon, S. R. H. Madjid, S. J. Hwang, and C. D. Yoo, “Forget-free Continual Learning with Soft-Winning SubNetworks,” 2023. doi: 10.48550/arXiv.2303.14962.
[40] F. P. E. Putra, D. A. M. Putra, A. Firdaus, and A. Hamzah, “Analisis Kecepatan Dan Kinerja Jaringan 5G (generasi ke 5) Pada Wilayah Perkotaan,” INFORMATICS Educ. Prof. J. Informatics, vol. 8, no. 1, p. 47, 2023, doi: 10.51211/itbi.v8i1.2439.
[41] N. Yang et al., “A Blade-Type Triboelectric-Electromagnetic Hybrid Generator with Double Frequency Up-Conversion Mechanism for Harvesting Breeze Wind Energy,” 2024. doi: 10.1021/acsami.4c04377.
[42] N. Vidhya and C. Meenakshi, “Blockchain-Enabled Secure Data Aggregation Routing (BSDAR) Protocol for IoT-Integrated Next-Generation Sensor Networks for Enhanced Security,” 2025. doi: 10.22399/ijcesen.722.
[43] Y. Bai, B. Xie, R. Zhu, Z. Chang, and R. Jäntti, “Movable Antenna-Equipped UAV for Data Collection in Backscatter Sensor Networks: A Deep Reinforcement Learning-Based Approach,” 2024. doi: 10.1109/ICC52391.2025.11162069.
[44] R. F. Miranda et al., “A Review of Cognitive Hybrid Radio Frequency/Visible Light Communication Systems for Wireless Sensor Networks,” 2023. doi: 10.3390/s23187815.
[45] B. Al-Fuhaidi, Z. Farae, F. Al-Fahaidy, G. Nagi, A. Ghallab, and A. Alameri, “Anomaly-Based Intrusion Detection System in Wireless Sensor Networks Using Machine Learning Algorithms,” 2024. doi: 10.1155/2024/2625922.
[46] N. Dharini, J. Katiravan, S. D. M. Priya, and S. V. A. Sneghaa, “Intrusion Detection in Novel WSN-Leach Dos Attack Dataset using Machine Learning based Boosting Algorithms,” 2023. doi: 10.1016/j.procs.2023.12.064.
[47] A. Oztoprak, R. Hassanpour, A. Ozkan, and K. Oztoprak, “Security Challenges, Mitigation Strategies, and Future Trends in Wireless Sensor Networks: A Review,” 2024. doi: 10.1145/3706583.
[48] R. Arunachalam and E. D. R. Kanmani, “Detection and mitigation of vampire attacks with secure routing in WSN using weighted RNN and optimal path selection,” 2024. doi: 10.1016/j.cose.2024.103991.
[49] S. Madhavi, S. M. Udhaya Sankar, R. Praveen, and N. Jagadish Kumar, “A fuzzy COPRAS-based decision-making framework for mitigating the impact of vampire sensor nodes in wireless sensor nodes (WSNs),” 2023. doi: 10.1007/s41870-023-01219-5.
[50] B. Prince, P. Kumar, and S. K. Singh, “Multi-level clustering and Prediction based energy efficient routing protocol to eliminate Hotspot problem in Wireless Sensor Networks.,” 2025. doi: 10.1038/s41598-024-84596-6.
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Copyright (c) 2025 Ahmad Farizi, Mohammad Khoirun Nizam (Penulis)
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