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Detection of Android Based Applications with Traditional Metaheuristic Algorithms

Yıl 2023, , 381 - 392, 31.12.2023
https://doi.org/10.29132/ijpas.1382344

Öz

The widespread use of devices connected to Android systems in various areas of human life has made it an attractive target for bad actors. In this context, the development of mechanisms that can detect Android malware is among the most effective techniques to protect against various attacks. Feature selection is extremely to reduce the size of the dataset and improve computational efficiency while maintaining the accuracy of the performance model. Therefore, in this study, the five most widely used conventional metaheuristic algorithms for feature selection in the literature, such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Ant Colony Optimization (ACO) and Differential Evolution (DE), was used to select features that best represent benign and malicious applications on Android. The efficiency of these algorithms was evaluated on the Drebin-215 and MalGenome-215 dataset using five different machine learning (ML) method including Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB), Random Forest (RF) and Support Vector Machine (SVM). According to the results obtained from the experiments, DE-based feature selection and RF classifier are found to have better accuracy. According to the findings obtained from the experiments, it was seen that DE-based feature selection and RF method had better accuracy rate.

Kaynakça

  • Akinola, O.O., Ezugwu, A.E., Agushaka, J. O., Zitar, R. A. and Abualigah, L. (2022). Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Computing and Applications, 34 (22), 19751–19790.
  • Albakri, A., Alhayan, F., Alturki, N., Ahamed, S. and Shamsudheen, S. (2023). Metaheuristics with deep learning model for cybersecurity and Android malware detection and classification. Applied Sciences, 13 (4), 2172.
  • Arp D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K. and Siemens, C.E.R.T. (2014). Drebin: Effective and explainable detection of android malware in your pocket. In Ndss, (14), 23-26. Available from:http://www.deeplearningbook.org. (Accessed on 1 November 2022).
  • Bhattacharya, A., Goswami, R.T. and Mukherjee, K. (2019). A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. International Journal of Machine Learning and Cybernetics, (10), 1893–1907.
  • Chakravarthy, S. J. (2021). Wrapper-based metaheuristic optimization algorithms for android malware detection: a correlative analysis of firefly, bat & whale optimization. Journal of Hunan University (Natural Sciences), 48 (10), 928-943.
  • Cihan, P. (2021). The machine learning approach for predicting the number of intensive car, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences, (40) 1, 85-94.
  • Cihan, P., Kalıpsız O. and Gökçe, E. (2020). Computer- aided diagnosis in neonatal lambs. Pamukkale Üniversitesi Mühendislik Dergisi, 26 (2), 385-391.
  • Dağlıoğlu, A. and Doğru, I.A. (2020). Android işletim sisteminde kötücül yazılım tespit sistemleri. Dicle Üniversitesi Mühendislik Fakültesi, Mühendislik Dergisi, 2 (11) , 499-511.
  • Dinler, Ö.B. and Şahin, C.B. (2021). Prediction of phishing web sites with deep learning using WEKA environment. European Journal of Science and Technology, (24), 35-41.
  • Dokeroglu, T., Deniz, A. and Kiziloz, H.E. (2022). A comprehensive survey on recent metaheuristics for feature Selection. Neurocomputing, (494), 269-296.
  • Dorigo, M., Birattari, M. and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1 (4), 28-39.
  • Fatima, A., Maurya R., Dutta M.K., Burget R. and Masek, J. (2019). Android malware detection using genetic algorithm based optimized feature selection and machine learning. In Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP),220–223.
  • Firdaus, A., Anuar, N. B., Karim, A. and Razak, M. F. A. (2018). Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Frontiers of Information Technology & Electronic Engineering, 19 (6), 712-736.
  • Goldberg, D. E. and Holland, J. H. (1988). Machine Learning. Machine Learning, 3 (23), 95-99.
  • Hailat, M. M., Otair, M. A., Abualigah, L., Houssein, E. H. and Şahin, C.B. (2021). Improving automated arabic essay questions grading based on microsoft word dictionary. Deep learning approaches for spoken and natural language processing.
  • Islam, R., Sayed, M.I., Saha, S., Hossain, M.J. and Masud, M.A. (2023). Android malware classification using optimum feature selection and ensemble machine learning. Internet of Things and Cyber-Physical Systems, (3), 100-111.
  • Kareem, S. S., Mostafa, R. R., Hashim, F. A. and El-Bakry, H. M. (2022). An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection. Sensors, 22 (4), 1396.
  • Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization. In Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 1942-1948.
  • Khan, S.N., Khan S.U., Aznaoui H., Şahin, C.B. and Dinler, Ö.B. (2023). Generalization of linear and non-linear support vector machine in multiple fields: a review. Computer Science and Information Technologies, 3(4), 226-239
  • Lee, J., Jang, H., Ha, S. and Yoon, Y. (2021). Android malware detection using machine learning with feature selection based on the Genetic algorithm. Mathematics, (9), 2813.
  • Ling, J., Wang, X. and Sun, Y. (2019). Research of android malware detection based on ACO optimized Xgboost parameters approach. In 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT), 364-371.
  • Masum, M. and Shahriar, H. (2019). Droid-NNet: Deep learning neural network for android malware detection. In 2019 IEEE International Conference on Big Data, IEEE, 5789-5793.
  • Meimandi, A., Seyfari, Y. and Lotfi, S. (2020). Android malware detection using feature selection with hybrid genetic algorithm and simulated annealing. In Procee- dings of the 2020 IEEE 5th Conference on In Electrical and Computer Engineering (ETECH) Information and Communication Technology (ICT),1- 7.
  • Niyomubyeyi, O., Sicuaio, T. E., Díaz González, J. I., Pilesjö, P. and Mansourian, A. (2020). A comparative study of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning. Algorithms, 13 (1), 16.
  • Senanayake, J., Kalutarage, H. and Al-Kadri, M. O. (2021). Android mobile malware detection using machine learning: A systematic review. Electronics, 10(13), 1606.
  • Statista Research Department (2023). Statista. Available from: https://www.statista.com/statistics/272698/global-market-share-held-by-mobile-operating-systems-since-2009/. (Accessed on 1 November 2022).
  • Storn, R. and Price, K. (1997). Differential Evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, (11), 341-359.
  • Şahin, B.C. (2022). Learning optimized patterns of software vulnerabilities with the clock-work memory mechanism. European Journal of Science and Technology, (41), 156-165.
  • Tahtacı, B. and Canbay, B. (2020). Android malware detection using machine learning. Innovations in Intelligent Systems and Applications Conference (ASYU), 1-6.
  • Ullah, A., Şahin, B.C., Dinler, Ö.B., Khan, M.H., and Aznaoui, H. (2021). Heart disease prediction using various machines learning approach. Journal of Cardiovaskular Disease Research, 3(12), 379-391.
  • Van Laarhoven, P.J. and Aarts, E.H. (1987). Simulated Annealing. Springer Netherlands, 7-15.
  • Waleed, A. (2019). Hybrid intelligent Android malware detection using evolving support vector machine based on genetic algorithm and particle swarm optimization. IJCSNS International Journal of Computer Science and Network Security, 9 (19), 15-28.
  • Wang, L., Gao, Y., Gao, S. and Yong, X. (2021). A new feature selection method based on a self-variant genetic algorithm applied to android malware detection. Symmetry, 13 (7), 1290.
  • Yerima, S. Y. and Sezer, S. (2018). Droidfusion: A novel multilevel classifier fusion approach for android malware detection. IEEE transactions on cybernetics, 49 (2), 453-466.
  • Yildiz, O. and Doğru, I.A. (2019). Permission-based android malware detection system using feature selection with genetic algorithm. International Journal of Software Engineering and Knowledge Engineering, 29 (2), 245–262.
  • Zhou, Y. and Jiang, X. (2012). Dissecting android malware: characterization and evolution. In 2012 IEEE symposium on security and privacy, IEEE, 95-109. Available from: http://www.deeplearningbook.org
Yıl 2023, , 381 - 392, 31.12.2023
https://doi.org/10.29132/ijpas.1382344

Öz

Kaynakça

  • Akinola, O.O., Ezugwu, A.E., Agushaka, J. O., Zitar, R. A. and Abualigah, L. (2022). Multiclass feature selection with metaheuristic optimization algorithms: a review. Neural Computing and Applications, 34 (22), 19751–19790.
  • Albakri, A., Alhayan, F., Alturki, N., Ahamed, S. and Shamsudheen, S. (2023). Metaheuristics with deep learning model for cybersecurity and Android malware detection and classification. Applied Sciences, 13 (4), 2172.
  • Arp D., Spreitzenbarth, M., Hubner, M., Gascon, H., Rieck, K. and Siemens, C.E.R.T. (2014). Drebin: Effective and explainable detection of android malware in your pocket. In Ndss, (14), 23-26. Available from:http://www.deeplearningbook.org. (Accessed on 1 November 2022).
  • Bhattacharya, A., Goswami, R.T. and Mukherjee, K. (2019). A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. International Journal of Machine Learning and Cybernetics, (10), 1893–1907.
  • Chakravarthy, S. J. (2021). Wrapper-based metaheuristic optimization algorithms for android malware detection: a correlative analysis of firefly, bat & whale optimization. Journal of Hunan University (Natural Sciences), 48 (10), 928-943.
  • Cihan, P. (2021). The machine learning approach for predicting the number of intensive car, intubated patients and death: The COVID-19 pandemic in Turkey. Sigma Journal of Engineering and Natural Sciences, (40) 1, 85-94.
  • Cihan, P., Kalıpsız O. and Gökçe, E. (2020). Computer- aided diagnosis in neonatal lambs. Pamukkale Üniversitesi Mühendislik Dergisi, 26 (2), 385-391.
  • Dağlıoğlu, A. and Doğru, I.A. (2020). Android işletim sisteminde kötücül yazılım tespit sistemleri. Dicle Üniversitesi Mühendislik Fakültesi, Mühendislik Dergisi, 2 (11) , 499-511.
  • Dinler, Ö.B. and Şahin, C.B. (2021). Prediction of phishing web sites with deep learning using WEKA environment. European Journal of Science and Technology, (24), 35-41.
  • Dokeroglu, T., Deniz, A. and Kiziloz, H.E. (2022). A comprehensive survey on recent metaheuristics for feature Selection. Neurocomputing, (494), 269-296.
  • Dorigo, M., Birattari, M. and Stutzle, T. (2006). Ant colony optimization. IEEE Computational Intelligence Magazine, 1 (4), 28-39.
  • Fatima, A., Maurya R., Dutta M.K., Burget R. and Masek, J. (2019). Android malware detection using genetic algorithm based optimized feature selection and machine learning. In Proceedings of the 2019 42nd International Conference on Telecommunications and Signal Processing (TSP),220–223.
  • Firdaus, A., Anuar, N. B., Karim, A. and Razak, M. F. A. (2018). Discovering optimal features using static analysis and a genetic search based method for Android malware detection. Frontiers of Information Technology & Electronic Engineering, 19 (6), 712-736.
  • Goldberg, D. E. and Holland, J. H. (1988). Machine Learning. Machine Learning, 3 (23), 95-99.
  • Hailat, M. M., Otair, M. A., Abualigah, L., Houssein, E. H. and Şahin, C.B. (2021). Improving automated arabic essay questions grading based on microsoft word dictionary. Deep learning approaches for spoken and natural language processing.
  • Islam, R., Sayed, M.I., Saha, S., Hossain, M.J. and Masud, M.A. (2023). Android malware classification using optimum feature selection and ensemble machine learning. Internet of Things and Cyber-Physical Systems, (3), 100-111.
  • Kareem, S. S., Mostafa, R. R., Hashim, F. A. and El-Bakry, H. M. (2022). An effective feature selection model using hybrid metaheuristic algorithms for iot intrusion detection. Sensors, 22 (4), 1396.
  • Kennedy, J. and Eberhart, R. (1995). Particle Swarm Optimization. In Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, 1942-1948.
  • Khan, S.N., Khan S.U., Aznaoui H., Şahin, C.B. and Dinler, Ö.B. (2023). Generalization of linear and non-linear support vector machine in multiple fields: a review. Computer Science and Information Technologies, 3(4), 226-239
  • Lee, J., Jang, H., Ha, S. and Yoon, Y. (2021). Android malware detection using machine learning with feature selection based on the Genetic algorithm. Mathematics, (9), 2813.
  • Ling, J., Wang, X. and Sun, Y. (2019). Research of android malware detection based on ACO optimized Xgboost parameters approach. In 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT), 364-371.
  • Masum, M. and Shahriar, H. (2019). Droid-NNet: Deep learning neural network for android malware detection. In 2019 IEEE International Conference on Big Data, IEEE, 5789-5793.
  • Meimandi, A., Seyfari, Y. and Lotfi, S. (2020). Android malware detection using feature selection with hybrid genetic algorithm and simulated annealing. In Procee- dings of the 2020 IEEE 5th Conference on In Electrical and Computer Engineering (ETECH) Information and Communication Technology (ICT),1- 7.
  • Niyomubyeyi, O., Sicuaio, T. E., Díaz González, J. I., Pilesjö, P. and Mansourian, A. (2020). A comparative study of four metaheuristic algorithms, AMOSA, MOABC, MSPSO, and NSGA-II for evacuation planning. Algorithms, 13 (1), 16.
  • Senanayake, J., Kalutarage, H. and Al-Kadri, M. O. (2021). Android mobile malware detection using machine learning: A systematic review. Electronics, 10(13), 1606.
  • Statista Research Department (2023). Statista. Available from: https://www.statista.com/statistics/272698/global-market-share-held-by-mobile-operating-systems-since-2009/. (Accessed on 1 November 2022).
  • Storn, R. and Price, K. (1997). Differential Evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, (11), 341-359.
  • Şahin, B.C. (2022). Learning optimized patterns of software vulnerabilities with the clock-work memory mechanism. European Journal of Science and Technology, (41), 156-165.
  • Tahtacı, B. and Canbay, B. (2020). Android malware detection using machine learning. Innovations in Intelligent Systems and Applications Conference (ASYU), 1-6.
  • Ullah, A., Şahin, B.C., Dinler, Ö.B., Khan, M.H., and Aznaoui, H. (2021). Heart disease prediction using various machines learning approach. Journal of Cardiovaskular Disease Research, 3(12), 379-391.
  • Van Laarhoven, P.J. and Aarts, E.H. (1987). Simulated Annealing. Springer Netherlands, 7-15.
  • Waleed, A. (2019). Hybrid intelligent Android malware detection using evolving support vector machine based on genetic algorithm and particle swarm optimization. IJCSNS International Journal of Computer Science and Network Security, 9 (19), 15-28.
  • Wang, L., Gao, Y., Gao, S. and Yong, X. (2021). A new feature selection method based on a self-variant genetic algorithm applied to android malware detection. Symmetry, 13 (7), 1290.
  • Yerima, S. Y. and Sezer, S. (2018). Droidfusion: A novel multilevel classifier fusion approach for android malware detection. IEEE transactions on cybernetics, 49 (2), 453-466.
  • Yildiz, O. and Doğru, I.A. (2019). Permission-based android malware detection system using feature selection with genetic algorithm. International Journal of Software Engineering and Knowledge Engineering, 29 (2), 245–262.
  • Zhou, Y. and Jiang, X. (2012). Dissecting android malware: characterization and evolution. In 2012 IEEE symposium on security and privacy, IEEE, 95-109. Available from: http://www.deeplearningbook.org
Toplam 36 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yüksek Performanslı Hesaplama
Bölüm Makaleler
Yazarlar

Mehmet Şirin Beştaş 0000-0001-6561-4378

Özlem Batur Dinler 0000-0002-2955-6761

Erken Görünüm Tarihi 29 Aralık 2023
Yayımlanma Tarihi 31 Aralık 2023
Gönderilme Tarihi 27 Ekim 2023
Kabul Tarihi 8 Aralık 2023
Yayımlandığı Sayı Yıl 2023

Kaynak Göster

APA Beştaş, M. Ş., & Batur Dinler, Ö. (2023). Detection of Android Based Applications with Traditional Metaheuristic Algorithms. International Journal of Pure and Applied Sciences, 9(2), 381-392. https://doi.org/10.29132/ijpas.1382344
AMA Beştaş MŞ, Batur Dinler Ö. Detection of Android Based Applications with Traditional Metaheuristic Algorithms. International Journal of Pure and Applied Sciences. Aralık 2023;9(2):381-392. doi:10.29132/ijpas.1382344
Chicago Beştaş, Mehmet Şirin, ve Özlem Batur Dinler. “Detection of Android Based Applications With Traditional Metaheuristic Algorithms”. International Journal of Pure and Applied Sciences 9, sy. 2 (Aralık 2023): 381-92. https://doi.org/10.29132/ijpas.1382344.
EndNote Beştaş MŞ, Batur Dinler Ö (01 Aralık 2023) Detection of Android Based Applications with Traditional Metaheuristic Algorithms. International Journal of Pure and Applied Sciences 9 2 381–392.
IEEE M. Ş. Beştaş ve Ö. Batur Dinler, “Detection of Android Based Applications with Traditional Metaheuristic Algorithms”, International Journal of Pure and Applied Sciences, c. 9, sy. 2, ss. 381–392, 2023, doi: 10.29132/ijpas.1382344.
ISNAD Beştaş, Mehmet Şirin - Batur Dinler, Özlem. “Detection of Android Based Applications With Traditional Metaheuristic Algorithms”. International Journal of Pure and Applied Sciences 9/2 (Aralık 2023), 381-392. https://doi.org/10.29132/ijpas.1382344.
JAMA Beştaş MŞ, Batur Dinler Ö. Detection of Android Based Applications with Traditional Metaheuristic Algorithms. International Journal of Pure and Applied Sciences. 2023;9:381–392.
MLA Beştaş, Mehmet Şirin ve Özlem Batur Dinler. “Detection of Android Based Applications With Traditional Metaheuristic Algorithms”. International Journal of Pure and Applied Sciences, c. 9, sy. 2, 2023, ss. 381-92, doi:10.29132/ijpas.1382344.
Vancouver Beştaş MŞ, Batur Dinler Ö. Detection of Android Based Applications with Traditional Metaheuristic Algorithms. International Journal of Pure and Applied Sciences. 2023;9(2):381-92.

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