Design of an efficient Malware Prediction Model using Auto Encoded & Attention-based Recurrent Graph Relationship Analysis

Mahesh T. Dhande; Sanjaykumar Tiwari; Nikhil Rathod

doi:10.54392/irjmt2515

Authors

Mahesh T. Dhande Department of Computer Science & Engineering, Monad University, Hapur U.P -245301, India Author https://orcid.org/0009-0002-0214-9645
Sanjaykumar Tiwari Department of Computer Science & Engineering, Monad University, Hapur U.P -245301, India Author
Nikhil Rathod Department of Mechanical Engineering, Monad University, Hapur U.P - 245301, India Author

DOI:

https://doi.org/10.54392/irjmt2515

Keywords:

Malware Prediction, Auto Encoders, Attention Mechanisms, Recurrent Graph Analysis, Cyber security, Scenarios

Abstract

The threat of modern malware in the world of cyber security has grown and how the need for proper detection and analysis techniques has grown with it. All these conventional approaches are insufficient methods if used to detect new or emerging strains of malware. For this need, the present research develops a novel Malware Prediction Model using Auto Encoders and Attention Mechanisms to advance Malware Pattern Analysis. This new approach goes beyond the conventional wisdom because it decodes complex patterns of malware into identifiable Malware Classes utilizing the unique Recurrent Graph Relationship Analysis. Recurrent Networks perform the complex task of Feature Analysis and simultaneously. Classical approaches mainly conceive pattern matching where signatures are taken and used to look in the system hence cannot detect polymorphic or metamorphic types of viruses. Additionally, these systems have high levels of false positives and poor ability to learn from new types of threats. On the other hand, the coupling of Auto Encoders with Attention Mechanisms in the model under consideration allows the model to gain better insights of malware behavior. Such an integration not only improves the identification of multiform patterns but also changes the approach to growing threats more effectively. The use of this model was benchmarked against two databases: The Malware Memory Analysis and The Kharon Malware Database Samples. Strikingly, the proposed model provided 8.3% more precision, 8.5% more accuracy, 5.9% higher recall, 6.5% better AUC, higher specificity by 9.4%, while slight reduction in delay by 2.9% to other methods.

References

F. Zhong, Z. Chen, M. Xu, G. Zhang, D. Yu, X. Cheng, Malware-on-the-Brain: Illuminating Malware Byte Codes with Images for Malware Classification. IEEE Transactions on Computers, 72(2), (2023) 438-451. https://doi.org/10.1109/TC.2022.3160357

A. bin Asad, R. Mansur, S. Zawad, N. Evan, M.I. Hossain, Analysis of Malware Prediction Based on Infection Rate Using Machine Learning Techniques. IEEE Region 10 Symposium (TENSYMP), IEEE, Bangladesh. https://doi.org/10.1109/TENSYMP50017.2020.9230624

M.F. Abdelwahed, M.M. Kamal, G. Sayed, Detecting Malware Activities with MalpMiner: A Dynamic Analysis Approach. IEEE Access, 11, (2023) 84772-84784. https://doi.org/10.1109/ACCESS.2023.3266562

K. Rana, S. Gupta, G. Kaur, A.L. Yadav, (2024) Malware Detection in Network Traffic using Machine Learning. International Conference on Applied Artificial Intelligence and Computing (ICAAIC), IEEE, India. https://doi.org/10.1109/ICAAIC60222.2024.10575355

O.E. Kural, E. Kiliç, C. Aksaç, Apk2Audio4AndMal: Audio Based Malware Family Detection Framework. IEEE Access, 11, (2023) 27527-27535. https://doi.org/10.1109/ACCESS.2023.3258377

H. Kim, M. Kim, (2024) Malware Detection and Classification System Based on CNN-BiLSTM. Electronics, 13(13), 2539. https://doi.org/10.3390/electronics13132539

K.A. Dhanya, P. Vinod, S.Y. Yerima, A. Bashar, A. David, T. Abhiram, A. Antony, A.K. Shavanas, G. Kumar, Obfuscated Malware Detection in IoT Android Applications Using Markov Images and CNN. IEEE Systems Journal, 17(2), (2023) 2756-2766. https://doi.org/10.1109/JSYST.2023.3238678

Y.H. Chen, S.C. Lin, S.C. Huang, C.L. Lei, C.Y. Huang, Guided Malware Sample Analysis Based on Graph Neural Networks. IEEE Transactions on Information Forensics and Security, 18, (2023) 4128-4143. https://doi.org/10.1109/TIFS.2023.3283913

D.Y.M. Benchadi, B. Batalo, K. Fukui, Efficient Malware Analysis Using Subspace-Based Methods on Representative Image Patterns. IEEE Access, 11, (2023) 102492-102507. https://doi.org/10.1109/ACCESS.2023.3313409

I. Gulatas, H.H. Kilinc, A.H. Zaim, M. A. Aydin, Malware Threat on Edge/Fog Computing Environments From Internet of Things Devices Perspective. IEEE Access, 11, (2023) 33584-33606. https://doi.org/10.1109/ACCESS.2023.3262614

B. Jin, J. Choi, J.B. Hong, H. Kim, On the Effectiveness of Perturbations in Generating Evasive Malware Variants. IEEE Access, 11, (2023) 31062-31074. https://doi.org/10.1109/ACCESS.2023.3262265

J. Jeon, B. Jeong, S. Baek, Y.S. Jeong, Static Multi Feature-Based Malware Detection Using Multi SPP-net in Smart IoT Environments. IEEE Transactions on Information Forensics and Security, 19, (2024) 2487-2500. https://doi.org/10.1109/TIFS.2024.3350379

M. Venkatasubramanian, A.H. Lashkari, S. Hakak, IoT Malware Analysis Using Federated Learning: A Comprehensive Survey. IEEE Access, 11, (2023) 5004-5018. https://doi.org/10.1109/ACCESS.2023.3235389

E.C. Bayazit, O.K. Sahingoz, B. Dogan, Protecting Android Devices from Malware Attacks: A State-of-the-Art Report of Concepts, Modern Learning Models and Challenges. IEEE Access, 11, (2023) 123314-123334. https://doi.org/10.1109/ACCESS.2023.3323396

G.W. Wong, Y.T. Huang, Y.R. Guo, Y. Sun, M.C. Chen, Attention-Based API Locating for Malware Techniques. IEEE Transactions on Information Forensics and Security, 19, (2024) 1199-1212. https://doi.org/10.1109/TIFS.2023.3330337

D.T. Uysal, P.D. Yoo, K. Taha, Data-Driven Malware Detection for 6G Networks: A Survey from the Perspective of Continuous Learning and Explainability via Visualisation. IEEE Open Journal of Vehicular Technology, 4, (2023) 61-71. https://doi.org/10.1109/OJVT.2022.3219898

S. Ali, O. Abusabha, F. Ali, M. Imran, T. Abuhmed, Effective Multitask Deep Learning for IoT Malware Detection and Identification Using Behavioral Traffic Analysis. IEEE Transactions on Network and Service Management, 20(2), (2023) 1199-1209. https://doi.org/10.1109/TNSM.2022.3200741

S. Li, Y. Li, X. Wu, S.A. Otaibi, Z. Tian, Imbalanced Malware Family Classification Using Multimodal Fusion and Weight Self-Learning. in IEEE Transactions on Intelligent Transportation Systems, 24(7), (2023) 7642-7652. https://doi.org/10.1109/TITS.2022.3208891

U. Ahmed, J.C. W. Lin, G. Srivastava, A. Jolfaei, Active Learning Based Adversary Evasion Attacks Defense for Malwares in the Internet of Things. IEEE Systems Journal, 17(2), (2023) 2434-2444. https://doi.org/10.1109/JSYST.2022.3223694

W. Niu, Y. Wang, X. Liu, R. Yan, X. Li, X. Zhang, GCDroid: Android Malware Detection Based on Graph Compression With Reachability Relationship Extraction for IoT Devices. IEEE Internet of Things Journal, 10(13), (2023) 11343-11356. https://doi.org/10.1109/JIOT.2023.3241697

H. Lee, S. Kim, D. Baek, D. Kim, D. Hwang, Robust IoT Malware Detection and Classification Using Opcode Category Features on Machine Learning. IEEE Access, 11, (2023) 18855-18867. https://doi.org/10.1109/JIOT.2023.3241697

M. Torres, R. Álvarez, M. Cazorla, A Malware Detection Approach Based on Feature Engineering and Behavior Analysis, IEEE Access, 11, (2023) 105355-105367. https://doi.org/10.1109/ACCESS.2023.3319093

L. Huang, J. Xue, Y. Wang, D. Qu, J. Chen, N. Zhang, L. Zhang, EAODroid: Android malware detection based on enhanced API order. Chinese Journal of Electronics, 32(5), (2023) 1169-1178. https://doi.org/10.23919/cje.2021.00.451

H. Manthena, J.C. Kimmel, M. Abdelsalam, M. Gupta, Analyzing and Explaining Black-Box Models for Online Malware Detection. IEEE Access, 11, (2023) 25237-25252. https://doi.org/10.1109/ACCESS.2023.3255176

S. Kasarapu, S. Shukla, S.M. PudukotaiDinakarrao, Resource- and Workload-Aware Model Parallelism-Inspired Novel Malware Detection for IoT Devices. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 42(12), (2023) 4618-4628. https://doi.org/10.1109/TCAD.2023.3290128

T.H. Hai, V. Van Thieu, T.T. Duong, H.H. Nguyen, E.N. Huh, A Proposed New Endpoint Detection and Response With Image-Based Malware Detection System. IEEE Access, 11, (2023) 122859-122875. https://doi.org/10.1109/ACCESS.2023.3329112

F. A. Almarshad, M. Zakariah, G. A. Gashgari, E. A. Aldakheel and A. I. A. Alzahrani, Detection of Android Malware Using Machine Learning and Siamese Shot Learning Technique for Security. IEEE Access, 11, (2023) 127697-127714. https://doi.org/10.1109/ACCESS.2023.3331739

T. He, C. Han, R. Isawa, T. Takahashi, S. Kijima, J. Takeuchi, Scalable and Fast Algorithm for Constructing Phylogenetic Trees With Application to IoT Malware Clustering. IEEE Access, 11, (2023) 8240-8253. https://doi.org/10.1109/ACCESS.2023.3238711

Y. He, Y. Liu, L. Wu, Z. Yang, K. Ren, Z. Qin, MsDroid: Identifying Malicious Snippets for Android Malware Detection. IEEE Transactions on Dependable and Secure Computing, 20(3), (2023) 2025-2039. https://doi.org/10.1109/TDSC.2022.3168285

L.d. Costa, V. Moia, A Lightweight and Multi-Stage Approach for Android Malware Detection Using Non-Invasive Machine Learning Techniques. IEEE Access, 11, (2023) 73127-73144. https://doi.org/10.1109/ACCESS.2023.3296606

J. Qiu, Q.L. Han, W. Luo, L. Pan, S. Nepal, J. Zhang, Y. Xiang, Cyber code intelligence for android malware detection. IEEE Transactions on Cybernetics, 53(1), (2022) 617-627. https://doi.org/10.1109/TCYB.2022.3164625

H. Alamro, W. Mtouaa, S. Aljameel, A.S. Salama, M.A. Hamza, A.Y. Othman, Automated Android Malware Detection Using Optimal Ensemble Learning Approach for Cyber security. IEEE Access, 11, (2023) 72509-72517. https://doi.org/10.1109/ACCESS.2023.3294263

Y. Zhang, G. Gui, S. Mao, A Lightweight Malware Traffic Classification Method Based on a Broad Learning Architecture, IEEE Internet of Things Journal, 10(23), (2023) 21131-21132. https://doi.org/10.1109/JIOT.2023.3297210

H. Kheddar, Y. Himeur, A.I. Awad, Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review. In Journal of Network and Computer Applications 220, (2023) 103760. https://doi.org/10.1016/j.jnca.2023.103760

A. Gueriani, H. Kheddar, A.C. Mazari, (2023) Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey. International Conference on Electronics, Energy and Measurement (IC2EM), IEEE, Medea. https://doi.org/10.1109/IC2EM59347.2023.10419560

M. Anusha, M. Karthika, Deep Learning Based Maldroid Stacked Propagate Network for Android Malware Prediction for Security Enhancement. Indian Journal of Science and Technology, 17(45), (2024) 4743-4755. https://doi.org/10.17485/IJST/v17i45.3099

C. Duthie, G.J.W. Kathrine, G. Amala Nikitha, S.B. Xavier, I.J. Jebadurai, (2023) Deep Learning based Malware Analysis, Prediction and Prevention, 4th International Conference on Electronics and Sustainable Communication Systems (ICESC), IEEE, India. https://doi.org/10.1109/ICESC57686.2023.10193068

T. Kalpana, (2023) Malware Prediction and Classification for Android Applications Using Machine Learning Techniques. International Conference on Computer Communication and Informatics (ICCCI), IEEE, India. https://doi.org/10.1109/ICCCI56745.2023.10128513

M. Basak, D.W Kim, M.M. Han, G.Y. Shin, Attention-Based Malware Detection Model by Visualizing Latent Features Through Dynamic Residual Kernel Network. Sensors, 24(24), (2024) 7953. https://doi.org/10.3390/s24247953

M. Basak, M.M. Han, CyberSentinel: A Transparent Defense Framework for Malware Detection in High-Stakes Operational Environments. Sensors, 24(11), (2024) 3406. https://doi.org/10.3390/s24113406