Comparative analysis of malicious Android-based software detection with trending metaheuristic algorithms

Abstract

Today, Android malware threats and attacks are rapidly increasing due to their use and popularity. Therefore, the need for systems effectively detecting malware is also increasing day by day. This study proposes the use of various trending metaheuristic algorithms for optimal feature selection (FS) in the detection of Android malware. For this purpose, the ten most prominent recent metaheuristic algorithms (RMAs) for feature selection such as Artificial Bee Colony Algorithm (ABC), Firefly Algorithm (FA), Grey Wolf Optimisation (GWO), Ant Lion Optimisation (ALO), Crow Search Algorithm (CSA), Sine Cosine Algorithm (SCA), Whale Optimisation Algorithm (WOA), Salp Swarm Algorithm (SSA), Harris Hawk Optimization (HHO) and Butterfly Optimization Algorithm (BOA) were used for feature selection in this study. The efficiency of these algorithms is evaluated with five different machine learning (ML) methods on two well-known datasets of Android applications (Drebin215 and Malgenome-215). The results obtained are also compared with five well-known and widely used conventional metaheuristic algorithms (CMAs) for solving this problem. Extensive experimental results show that incorporating RMA into Android malware detection is a valuable approach.

Keywords

• Android
• Feature selection
• Machine learning
• Malicious software
• Recent metaheuristic algorithms

Kötü amaçlı Android tabanlı yazılım tespitinin trend meta-sezgisel algoritmalar ile karşılaştırılmalı analizi

Öz

Günümüzde Android kötü amaçlı yazılım tehdit ve saldırıları, kullanımları ve popülerlikleri nedeniyle hızla artmaktadır. Bu nedenle, kötü amaçlı yazılımları etkili bir şekilde tespit edebilecek sistemlere olan ihtiyaç da gün geçtikçe artmaktadır. Bu çalışma, Android kötü amaçlı yazılımların tespitinde optimum özellik seçimi (FS) için trend olan çeşitli meta-sezgisel algoritmaların sarmalama yöntemi ile kullanılmasını önermektedir. Bu amaçla, bu çalışmada Yapay Arı Kolonisi Algoritması (ABC), Ateş Böceği Algoritması (FA), Gri Kurt Optimizasyonu (GWO), Karınca Aslanı Optimizasyonu (ALO), Karga Arama Algoritması (CSA), Sinüs Kosinüs Algoritması (SCA), Balina Optimizasyon Algoritması (WOA), Salp Sürü Algoritması (SSA), Harris Şahin Optimizasyonu (HHO) ve Kelebek Optimizasyonu Algoritması (BOA) gibi özellik seçiminde en öne çıkan on güncel meta-sezgisel algoritma (RMA) kullanılmıştır. Bu algoritmaların verimliliği, Android uygulamalarının iyi bilinen iki veri kümesi (Drebin-215 ve Malgenome215) üzerinde beş farklı makine öğrenmesi (ML) yöntemi ile değerlendirilmiştir. Ayrıca, elde edilen sonuçlar bu problemin çözümünde yaygın olarak kullanılan ve iyi bilinen beş geleneksel metasezgisel algoritma (CMAs) ile de karşılaştırılmıştır. Kapsamlı deneysel sonuçlar, RMA’nın Android kötü amaçlı yazılım tespitine dahil edilmesinin değerli bir yaklaşım olduğunu göstermektedir.

Anahtar Kelimeler

• Android
• Özellik seçimi
• Makine öğrenmesi
• Kötü amaçlı yazılım
• Güncel meta-sezgisel algoritmala

References

1. [1] Tahtaci B, Canbay B. “Android malware detection using machine learning”. In 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, 15-17 October 2020.
2. [2] Kalash M, Rochan M, Mohammed N, Bruce ND, Wang Y, Iqbal F. “Malware classification with deep convolutional neural networks”. In 2018 9th IFIP international conference on new technologies, mobility and security (NTMS), Paris, France, 26-28 February 2018.
3. [3] Masum M, Shahriar H. “Droid-NNet: Deep learning neural network for android malware detection”. In 2019 IEEE International Conference on Big Data(Big Data), Los Angeles, CA, USA, 9-12 December 2019.
4. [4] Lee J, Jang H, Ha S, Yoon Y. “Android malware detection using machine learning with feature selection based on the genetic algorithm”. Mathematics, 9(21), 2813, 117334-117352, 2021.
5. [5] Wang L, Gao Y, Gao S, Yong X. “A new feature selection method based on a self-variant genetic algorithm applied to android malware detection”. Symmetry, 13(7), 1290, 2021.
6. [6] Ay Ş, Ekinci E, Garip Z. “A comparative analysis of meta-heuristic optimization algorithms for feature selection on ML-based classification of heart-related diseases”. The Journal of Supercomputing, 79, 11797-11826,2023.
7. [7] Şahin CB, Diri B. "Robust feature selection with LSTM recurrent neural networks for artificial immune recognition system". IEEE Access, (7), 24165-24178, 2019,
8. [8] Şahin CB. “Learning optimized patterns of software vulnerabilities with the clock-work memory mechanism”. Avrupa Bilim ve Teknoloji Dergisi, (41), 156-165, 2022.

9. [9] Goldberg DE, Holland, JH. “Machine learning”. Machine Learning, 3(23), 95-99, 1988.
10. [10] Laarhoven PJM, Aarts EHL. Simulated Annealing. Theory and Applications. Editors: Manin YI, Rinnooy Kan AHG, Rota G-C. Mathematics and Its Applications, 7-15, Philips Research Laboratories, Eindhoven, The Netherlands Springer Science+Business Media Dordrecht Press,1987.
11. [11] Dorigo M, Birattari M, Stutzle T. “Ant colony optimization”. IEEE Computational Intelligence Magazine, 1(4), 28-39, 2006.
12. [12] Storn R, Price K. “Differential Evolution–a simple and efficient heuristic for global optimization over continuous spaces”. Journal of Global Optimization, (11), 341-359, 1997.
13. [13] Kennedy J, Eberhart R. “Particle swarm optimization”. In Proceedings of ICNN'95-International Conference on Neural Networks, IEEE, Perth, WA, Australia, 27-01 December 1995.
14. [14] Karaboga D. “An idea based on honey bee swarm for numerical optimization”. Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, (200), 1-10, 2005.
15. [15] Yang XS. “Firefly algorithm, stochastic test functions and design optimisation”. International Journal of Bio-İnspired Computation, 2(2), 78-84, 2010.
16. [16] Mirjalili S, Mirjalili SM, Lewis A. “Grey wolf optimizer”. Advances in Engineering Software, 69, 46-61, 2014.
17. [17] Mirjalili S, Gandomi A H, Mirjalili SZ, Saremi S, Faris H, Mirjalili SM. “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems”. Advances in Engineering Software, 114, 163-191, 2017.
18. [18] Akalın F, Yumusak N. “Classification of acute leukaemias with a hybrid use of feature selection algorithms and deep learning-based architectures”. Pamukkale University Journal of Engineering Sciences, 29(3), 256-263, 2022.
19. [19] Beştaş MŞ, Dinler ÖB. “Detection of android based applications with traditional metaheuristic algorithms”. International Journal of Pure and Applied Sciences, 9(2), 381-392, 2023.
20. [20] Naick S, Bethapudi P, Reddy SPR. "Malware detection in Android mobile devices by applying Swarm Intelligence Optimization and machine learning for API Calls". International Journal of Intelligent Systems and Applications in Engineering, 10(3), 67-74, 2022.
21. [21] Sharma RM. “AMD-FIWDA: Android malware detection using feature importance Water Drop Algorithm”. NeuroQuantology, 20(15), 5005-5018 2022.
22. [22] Varma PRK, Mallidi SKR, Jhansi SJ, Dinne PL. “Bat optimization algorithm for wrapper-based feature selection and performance improvement of android malware detection”. IET Netw, 10, 131–140, 2021.
23. [23] Chakravarthy SJ. “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, 2021.
24. [24] Bhagwat S, Gupta GP. “Android malware detection using hybrid meta-heuristic feature selection and ensemble learning techniques”. In International Conference on Advances in Computing and Data Sciences, 6th International Conference, ICACDS 2022, Kurnool, India, 22-23 April, 2022.
25. [25] Elkabbash ET, Mostafa RR, Barakat SI. “Android malware classification based on random vector functional link and artificial Jellyfish Search optimizer”. PLoS ONE, 16(11), 1-22, 2021.
26. [26] Alzubi OA, Alzubi JA, Al-Zoubi AM, Hassonah MA, Kose U. “An efficient malware detection approach with feature weighting based on Harris Hawks optimization”. Cluster Computing, 25, 2369-2387, 2022.
27. [27] Sulaimon SA, Adebayo OS, Bashir SA, Ismaila I. “Android malware classification using whale optimization algorithm”. i-manager’s Journal on Mobile Applications and Technologies, 5(2), 37-45, 2018.
28. [28] Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens CERT. “Drebin: effective and explainable detection of android malware in your pocket”. In Ndss, (14), 23-26, 2014.
29. [29] HCRL. “Hacking and Countermeasure Research Lab”. https://ocslab.hksecurity.net/andro-autopsy (11.05. 2021).
30. [30] Zhou Y, Jiang X. “Dissecting android malware: characterization and evolution”. In 2012 IEEE symposium on security and privacy, IEEE, San Francisco, CA, USA, 20-23 May 2012.
31. [31] Deci. “Lesson 1.4 Data Shuffling”. https://deci.ai/course/data-shuffling/ (11.05.2021).
32. [32] Yildiz O, Doğru IA. “Permission-based android malware detection system using feature selection with genetic algorithm”. International Journal of Software Engineering and Knowledge Engineering, 29(02), 245-262, 2019.
33. [33] Dokeroglu T, Deniz A, Kiziloz HE. “A comprehensive survey on recent metaheuristics for feature selection”. Neurocomputing, 494, 269-296, 2022.
34. [34] Gao W, Liu S, Huang L. “A global best artificial bee colony algorithm for global optimization”. Journal of Computational and Applied Mathematics, 236, 2741-2753, 2011.
35. [35] Singh NSP, Nair NK. “Artificial bee colony algorithm for inverter complex wave reduction under line-load variations”. Transactions of the Institute of Measurement and Control, 40(5), 1593-1607, 2018.
36. [36] Jin Y, Sun Y, Ma H. “A developed artificial bee colony algorithm based on cloud model“. Mathematics, 6(4), 61, 1-18, 2018.
37. [37] Mirjalili S. “The ant lion optimizer”. Advances in Engineering Software, 83, 80-98, 2015.
38. [38] Zhou H, Cheng HY, Wei ZL, Zhao X, Tang AD, Xie L. “A hybrid butterfly optimization algorithm for numerical optimization problems”. Computational Intelligence and Neuroscience, 2021, 1-4, 2021.
39. [39] Arora S, Singh S. “Butterfly optimization algorithm: a novel approach for global optimization”. Soft Computing, 23, 715-734, 2019.
40. [40] Çerçevik AE, Avşar Ö. “Doğrusal sismik izolasyon parametrelerinin karga arama algoritması ile optimizasyonu”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 26(3), 440-447, 2020.
41. [41] Şahin CB. “Optimization of software vulnerabilities patterns with the meta-heuristic algorithms”. Türk Doğa ve Fen Dergisi, 11(4), 117-125, 2022.
42. [42] Bairathi D, Gopalani D. “A novel swarm intelligence based optimization method: Harris’ hawk optimization”. 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018) Vellore, India, 6-8 December, 2018.
43. [43] Mirjalili S. “SCA: a sine cosine algorithm for solving optimization problems”. Knowledge-Based Systems, 96, 120-133, 2016.
44. [44] Abualigah L, Diabat A. “Advances in Sine Cosine Algorithm: A comprehensive survey”. Artificial Intelligence Review, 54(4), 2567–2608, 2021.
45. [45] Ibrahim RA, Ewees AA, Oliva D, Elaziz MA, Lu S. “Improved salp swarm algorithm based on particle swarm optimization for feature selection”. Journal of Ambient Intelligence and Humanized Computing, 10, 3155–3169, 2019.
46. [46] Mirjalili S, Lewis A. “The whale optimization algorithm”. Advances in Engineering Software, 95, 51-67, 2016.
47. [47] Nadimi-Shahraki MH, Zamani H, Asghari Varzaneh, ZA, Mirjalili S. “A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations”. Archives Computational Methods in Engineering, 30, 4113–4159, 2023.
48. [48] Alızada, B. “Improved whale optimization algorithm based on π number”. International Scientific and Vocational Studies Journal, 4(1), 21-30, 2020.
49. [49] Cihan P, Kalıpsız O, Gökçe E. “Computeraided diagnosis in neonatal lambs”. Pamukkale Üniversitesi Mühendislik Dergisi, 26(2), 385-391, 2020.
50. [50] Ullah A, Şahin CB, Dinler Ö.B, Khan MH, Aznaoui H. “Heart disease prediction using various machines learning approach”. Journal of Cardiovaskular Disease Research, 3(12), 379-391, 2021.
51. [51] Koşan MA, Yıldız O, Karacan H. “Kimlik avı web sitelerinin tespitinde makine öğrenmesi algoritmalarının karşılaştırmalı analizi”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 24(2), 276-282,2018.
52. [52] Kalaycı TE. “Kimlik hırsızı web sitelerinin sınıflandırılması için makine öğrenmesi yöntemlerinin karşılaştırılması”. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi. 24(5), 870-878, 2018.
53. [53] Khan SN, Khan SU, Aznaoui H, Şahin, CB, Dinler, Ö.B. “Generalization of linear and nonlinear support vector machine in multiple fields: a review”. Computer Science and Information Technologies, 3(4), 226-239, 2023.
54. [54] Cihan P. “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, 2021.
55. [55] Lee J, Jang H, Ha S, Yoon Y. “Android malware detection using machine learning with feature selection based on the Genetic algorithm”. Mathematics, 9, 1-20, 2021.