Malware detection for Android application using Aquila optimizer and Hybrid LSTM-SVM classifier

M. Grace; M. Sughasiny

doi:10.4108/eetsis.v9i4.2565

Authors

M. Grace Bharathidasan University
M. Sughasiny Bharathidasan University

DOI:

https://doi.org/10.4108/eetsis.v9i4.2565

Keywords:

Malware detection, Hybrid LSTM-SVM, Aquila optimizer, k-fold Cross validation

Abstract

INTRODUCTION: Android OS is the most recent used smartphone platform in the world that occupies about 80% in share market. In google play store, there are 3.48 million apps available for downloading. Unfortunately, the growth rate of malicious apps in google play store and third party app store has become a big concern, which holds back the development of the Android smartphone ecosystem.

OBJECTIVES: In recent survey, a new malicious app has been introduced for every 10 seconds. These malicious apps are built to accomplish a variety of threats, such as Trojans, worms, exploits, and viruses. To overcome this issue, a new efficient and effective approach of malware detection for android application using Aquila optimizer and Hybrid LSTM-SVM classifier is designed.

METHODS: In this paper, the optimal features are selected from the CSV file based on the prediction accuracy by cross validation using Aquila optimizer and the mean square error (MSE) obtained by the cross validation is consider as the fitness function for the Aquila to select the optimal features.

RESULTS: The extracted optimal features are given to the Hybrid LSTM-SVM classifier for training and testing the features to predict the malware type in the android system.

CONCLUSION: This proposed model is implemented on python 3.8 for performance metrics such as accuracy, precision, execution time, error, etc. The acquired accuracy for the proposed model is 97%, which is greater compared to the existing techniques such as LSTM, SVM, RF and NB. Thus, the proposed model instantly predicts the malware from the android application.

References

Aung WZZ. Permission-based android malware detection. International Journal of Scientific & Technology Research, 2013, 2(3), 228-234.

Tsiatsikas Z, Kambourakis G, Geneiatakis D, Wang H. The devil is in the detail: SDP-driven malformed message attacks and mitigation in SIP ecosystems. IEEE Access, 2018, 7, 2401-2417.

Ye Y, Li T, Adjeroh D, Iyengar SS. A survey on malware detection using data mining techniques. ACM Computing Surveys (CSUR), 2017, 50(3), 1-40.

Li J, Sun L, Yan Q, Li Z, Srisa-An, W. Ye H. Significant permission identification for machine-learning-based android malware detection. IEEE Transactions on Industrial Informatics, 2018, 14(7), 3216-3225.

Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH. Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 2021, 157, 107250.

Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J. LSTM: A search space odyssey. IEEE transactions on neural networks and learning systems, 2016, 28(10), 2222-2232.

Dai J, Chen C, Li, Y. A backdoor attack against LSTM-based text classification systems. IEEE Access, 2019, 7, 138872-138878.

Karim F, Majumdar S, Darabi H, Chen S. LSTM fully convolutional networks for time series classification. IEEE access, 2017, 6, 1662-1669.

Zhang W, Yoshida T, Tang X. Text classification based on multi-word with support vector machine. Knowledge-Based Systems, 2017, 21(8), 879-886

Mitra V, Wang CJ & Banerjee S. Text classification: A least square support vector machine approach. Applied Soft Computing, 2007, 7(3), 908-914.

Koundel D, Ithape S, Khobaragade, V, Jain R. Malware classification using Naïve Bayes classifier for android OS. The International Journal of Engineering and Science, 2014, 3(4), 59-63.

Khammas BM Ransomware Detection Using Random Forest Technique. ICT Express, 2020, 6(4), 325-331.

Ye Y, Chen L, Wang D, Li T, Jiang Q, Zhao M. SBMDS: an interpretable string based malware detection system using SVM ensemble with bagging. Journal in computer virology, 2020, 5(4), 283-293.

Li Y, Xiong K, Chin T, Hu C (2019) A machine learning framework for domain generation algorithm-based malware detection. IEEE Access, 7, 32765-32782.

Lu R. Malware detection with LSTM using opcode language. arXiv preprint arXiv:1906.04593, 2019.

AlRassas AM, Al-qaness MA, Ewees AA, Ren S, Abd Elaziz M, Damaševičius R, Krilavičius T. Optimized ANFIS model using Aquila Optimizer for oil production forecasting. Processes, 2021, 9(7), 1194.

Abualigah L, Yousri D, Abd Elaziz M, Ewees AA, Al-qaness MA, Gandomi AH. Aquila Optimizer: A novel meta-heuristic optimization Algorithm. Computers & Industrial Engineering, 2021, 157, 107250

Lv, Sheng, Zhang H, He H. and Chen B. Microblog rumor detection based on comment sentiment and CNN-LSTM. In Artificial Intelligence in China, Springer, Singapore, 2020, 148-156.

Tharwat, Alaa. Parameter investigation of support vector machine classifier with kernel functions. Knowledge and Information Systems, 2019, 61(3), 1269-1302.

https://www.unb.ca/cic/datasets/andmal2017.html

Kouliaridis V, Kambourakis G, Chatzoglou E, Geneiatakis D, Wang H. Dissecting contact tracing apps in the Android platform. Plos one, 2021, 16(5), e0251867.

Singh R, Zhang Y, Wang H, Miao Y, Ahmed K. Investigation of Social Behaviour Patterns using Location-Based Data–A Melbourne Case Study. EAI Endorsed Transactions on Scalable Information Systems, 2020, 8(31), e2.

Zhang F, Wang Y, Liu S, Wang H. Decision-based evasion attacks on tree ensemble classifiers. World Wide Web, 2020, 23(5), 2957-2977.

Yin J, Tang M, Cao J, Wang H, You M, Lin Y. Vulnerability exploitation time prediction: an integrated framework for dynamic imbalanced learning. World Wide Web, 2022, 25(1), 401-423.