Deteksi Malware Ransomware Menggunakan Deep Neural Network

Benni Purnama, Eko Arip Winarto, Shairuppdin Shairuppdin, Ibnu Sani Wijaya

Abstract


Malware pada perangkat mobile android menjadi sebuah tantangan yang perlu di perhatikan secara khusus. Mengingat akhir-akhir ini banyak kasus kejahatan dalam teknologi informasi dan komunikasi melalui malware.  Sebuah malware ini bertujuan untuk mencuri, mengenkripsi, dan menghapus data sensitif kemudian mengubah atau membajak data dari sebuah perangkat pengguna. Oleh karena itu, pada penelitian ini bertujuan untuk mendeteksi malware jenis ransomware melalui system operasi android menggunakan metode deep learning. Metode yang diusulkan pada penelitian ini adalah Deep Neural Network (DNN). Dataset CIC-InvesAndMal2019 akan diujikan ke model hasil dari proses training DNN. Hasil pengujian model DNN menunjukkan bahwa DNN berhasil mendeteksi malware ransomware dengan tingkat akurasi mencapai 96.6 %.

Keywords


Malware; android; machine learning; Ransomware; DNN

Full Text:

PDF

References


M. S. Babak Bashari Rad, Mohammad Kazem Hassan Nejad, “Malware Classification And Detection Using Artificial Neural Network A Literature Review,” J. Eng. Sci. Technol., no. 7, pp. 14–23, 2018.

Y. Du, C. Liu, and Z. Su, “Detection and Suppression of Malware Based on Consortium Blockchain,” IOP Conf. Ser. Mater. Sci. Eng., vol. 490, no. 4, 2019, doi: 10.1088/1757-899X/490/4/042031.

H. Soni, P. Arora, and D. Rajeswari, “Malicious Application Detection in Android using Machine Learning,” Proc. 2020 IEEE Int. Conf. Commun. Signal Process. ICCSP 2020, pp. 846–848, 2020, doi: 10.1109/ICCSP48568.2020.9182170.

A. H. El Fiky, A. Elshenawy, and M. A. Madkour, “Detection of Android Malware using Machine Learning,” 2021 Int. Mobile, Intelligent, Ubiquitous Comput. Conf. MIUCC 2021, pp. 9–16, 2021, doi: 10.1109/MIUCC52538.2021.9447661.

H. Haidros Rahima Manzil and S. Manohar Naik, “DynaMalDroid: Dynamic Analysis-Based Detection Framework for Android Malware Using Machine Learning Techniques,” IEEE Int. Conf. Knowl. Eng. Commun. Syst. ICKES 2022, pp. 1–6, 2022, doi: 10.1109/ICKECS56523.2022.10060106.

W. Wang, M. Zhao, and J. Wang, “Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network,” J. Ambient Intell. Humaniz. Comput., vol. 0, no. 0, pp. 1–9, 2018, doi: 10.1007/s12652-018-0803-6.

M. Anshori, F. Mar’i, and F. A. Bachtiar, “Comparison of Machine Learning Methods for Android Malicious Software Classification based on System Call,” Proc. 2019 4th Int. Conf. Sustain. Inf. Eng. Technol. SIET 2019, pp. 343–348, 2019, doi: 10.1109/SIET48054.2019.8985998.

A. K. T. Lee Yam, J. M. R. Ballesta, J. A. H. Lanceta, M. K. T. Mogol, and R. Labanan, “Hybrid Android Malware Detection Model using Machine learning Algorithms,” Proc. - 2022 2nd Int. Conf. Inf. Comput. Res. iCORE 2022, pp. 66–71, 2022, doi: 10.1109/iCORE58172.2022.00032.

D. Arivudainambi, V. K. K. A, S. C. S, and P. Visu, “Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance,” Comput. Commun., vol. 147, no. June, pp. 50–57, 2019, doi: 10.1016/j.comcom.2019.08.003.

R. Wang, J. Zheng, Z. Shi, and Y. Tan, “Detecting Malware Using Graph Embedding and DNN,” Proc. - 2022 Int. Conf. Blockchain Technol. Inf. Secur. ICBCTIS 2022, pp. 28–31, 2022, doi: 10.1109/ICBCTIS55569.2022.00018.

M. Gullu and N. Barisci, “Android Malware Classification with Gray Wolf Optimization Algorithm and Deep Neural Network Hybrid Approach,” 2022 30th Signal Process. Commun. Appl. Conf. SIU 2022, pp. 13–16, 2022, doi: 10.1109/SIU55565.2022.9864822.

B. Vasu and N. Pari, “Combining Multimodal DNN and SigPid technique for detecting Malicious Android Apps,” Proc. 11th Int. Conf. Adv. Comput. ICoAC 2019, pp. 289–294, 2019, doi: 10.1109/ICoAC48765.2019.247134.

M. K. Alzaylaee, S. Y. Yerima, and S. Sezer, “DL-Droid: Deep learning based android malware detection using real devices,” Comput. Secur., vol. 89, p. 101663, 2020, doi: 10.1016/j.cose.2019.101663.

L. Taheri, A. F. A. Kadir, and A. H. Lashkari, “Extensible android malware detection and family classification using network-flows and API-calls,” Proc. - Int. Carnahan Conf. Secur. Technol., vol. 2019-October, no. Cic, 2019, doi: 10.1109/CCST.2019.8888430.

M. Merenda, C. Porcaro, and D. Iero, “Edge Machine Learning for AI-Enabled IoT Devices: A Review,” Sensors, vol. 20, p. 2533, 2020, doi: 10.3390/s20092533.




DOI: http://dx.doi.org/10.26418/jp.v10i1.68492

Refbacks

  • There are currently no refbacks.