Wang S, Chen Z, Yan Q, Ji K, Peng L, Yang B, Conti M. Deep and broad URL feature mining for android malware detection. Inf Sci. 2020;513:600–13.

Article  Google Scholar 

Wang Y, Wang Q, Qin X, Chen X, Xin B, Yang R. Dockerwatch: a two-phase hybrid detection of malware using various static features in container cloud. Soft Comput. 2022;1–17.

Masood Z, Majeed K, Samar R, Raja MAZ. Design of epidemic computer virus model with effect of quarantine in the presence of immunity. Fundam Inform. 2018;161(3):249–73.

Article  MathSciNet  Google Scholar 

Salvakkam DB, Saravanan V, Jain PK, Pamula R. Enhanced quantum-secure ensemble intrusion detection techniques for cloud based on deep learning. Cogn Comput. 2023;1–20.

Tong F, Yan Z. A hybrid approach of mobile malware detection in android. J Parallel Distrib Comput. 2017;103:22–31.

Article  Google Scholar 

Martín A, Menéndez HD, Camacho D. MOCDroid: multi-objective evolutionary classifier for android malware detection. Soft Comput. 2017;21(24):7405–15.

Article  Google Scholar 

Xiong P, Wang X, Niu W, Zhu T, Li G. Android malware detection with contrasting permission patterns. China Commun. 2014;11(8):1–14.

Article  Google Scholar 

Chen C-M, Lai G-H, Lin J-M. Identifying threat patterns of android applications. 2017 12th Asia Joint Conference on Information Security (AsiaJCIS). IEEE: 2017. p. 69–74.

Denźux T. 40 years of Dempster-Shafer theory. Int J Approx Reason. 2016;79(C):1–6.

Article  MathSciNet  Google Scholar 

Zhang Q, Xie Q, Wang G. A survey on rough set theory and its applications. CAAI Trans Intell Technol. 2016;1(4):323–33.

Article  Google Scholar 

Colson B, Marcotte P, Savard G. An overview of bilevel optimization. Ann Oper Res. 2007;153(1):235–56.

Article  MathSciNet  Google Scholar 

Wang X, Miikkulainen R. MDEA: malware detection with evolutionary adversarial learning. 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE: 2020. p. 1–8.

Akandwanaho SM, Kooblal M. Intelligent malware detection using a neural network ensemble based on a hybrid search mechanism. Afr J Inf Commun. 2019;24:1–21.

Google Scholar 

Lee J, Jang H, Ha S, Yoon Y. Android malware detection using machine learning with feature selection based on the genetic algorithm. Mathematics. 2021;9(21):2813.

Article  Google Scholar 

Sen S, Aydogan E, Aysan AI. Coevolution of mobile malware and anti-malware. IEEE Trans Inf Forensics Secur. 2018;13(10):2563–74.

Article  Google Scholar 

Jerbi M, Dagdia ZC, Bechikh S, Said LB. On the use of artificial malicious patterns for android malware detection. Comput Sec. 2020;92:101743.

Article  Google Scholar 

Jerbi M, Dagdia ZC, Bechikh S, Said LB. Android malware detection as a bi-level problem. Comput Secur. 2022;121:102825.

Article  Google Scholar 

Jerbi M, Dagdia ZC, Bechikh S, Said LB. Malware evolution and detection based on the variable precision rough set model. In: 2022 17th Conference on computer science and intelligence systems (FedCSIS). IEEE: 2022. p. 253–262.

Bhattacharya A, Goswami RT. A hybrid community based rough set feature selection technique in android malware detection. 2018;249–258

Deepa K, Radhamani G, Vinod P, Shojafar M, Kumar N, Conti M. FeatureAnalytics: an approach to derive relevant attributes for analyzing android malware. CoRR. abs/1809.09035 2018. arXiv:1809.09035

Chen R-C, Cheng K-F, Chen Y-H, Hsieh C-F. Using rough set and support vector machine for network intrusion detection system. 2009 First asian conference on intelligent information and database systems. 2009. p. 465–470. https://doi.org/10.1109/ACIIDS.2009.59.

Sengupta N, Sen J, Sil J, Saha M. Designing of on line intrusion detection system using rough set theory and Q-learning algorithm. Neurocomputing. 2013;111:161–8.

Article  Google Scholar 

Zhang B, Yin J, Tang W, Hao J, Zhang D. Unknown malicious codes detection based on rough set theory and support vector machine. The 2006 IEEE International joint conference on neural network proceedings. IEEE: 2006. p. 2583–2587.

Bhattacharya A, Goswami RT, Mukherjee K. A feature selection technique based on rough set and improvised PSO algorithm (PSORS-FS) for permission based detection of Android malwares. Int J Mach Learn Cybern. 2019;10(7):1893–907.

Article  Google Scholar 

Penmatsa RKV, Vatsavayi VK, Samayamantula SK. Ant colony optimization-based firewall anomaly mitigation engine. SpringerPlus. 2016;5(1):1–32.

Article  Google Scholar 

Nauman M, Azam N, Yao J. A three-way decision making approach to malware analysis using probabilistic rough sets. Inf Sci. 2016;374:193–209.

Article  Google Scholar 

Golmaryami M, Taheri R, Pooranian Z, Shojafar M, Xiao P. Setti: as elf-supervised adversarial malware detection architecture in an IoT environment. ACM Trans Multimed Comput Commun Appl (TOMM). 2022;18(2s):1–21.

Article  Google Scholar 

Kim J-Y, Cho S-B. Obfuscated malware detection using deep generative model based on global/local features. Comput Secur. 2022;112:102501.

Article  Google Scholar 

Hu W, Tan Y. Generating adversarial malware examples for black-box attacks based on GAN. 2017. arXiv:1702.05983

Kang M, Kim H, Lee S, Han S. Resilience against adversarial examples: data-augmentation exploiting generative adversarial networks. KSII Trans Internet Inf Syst. 2021;15(11).

AbuAlghanam O, Alazzam H, Qatawneh M, Aladwan O, Alsharaiah MA, Almaiah MA. Android malware detection system based on ensemble learning. 2023.

Kim J, Ban Y, Ko E, Cho H, Yi JH. Mapas: a practical deep learning-based android malware detection system. Int J Inf Secur. 2022;21(4):725–38.

Article  Google Scholar 

Alkahtani H, Aldhyani TH. Developing cybersecurity systems based on machine learning and deep learning algorithms for protecting food security systems: industrial control systems. Electronics. 2022;11(11):1717.

Article  Google Scholar 

Millar S, McLaughlin N, Rincon JM, Miller P. Multi-view deep learning for zero-day android malware detection. J Inf Secur Appl. 2021;58:102718.

Google Scholar 

Mimura M, Ito R. Applying NLP techniques to malware detection in a practical environment. Int J Inf Secur. 2022;21(2):279–91.

Article  Google Scholar 

Liu Z, Li S, Zhang Y, Yun X, Cheng Z. Efficient malware originated traffic classification by using generative adversarial networks. In: 2020 IEEE Symposium on Computers and Communications (ISCC). 2020. p. 1–7. https://doi.org/10.1109/ISCC50000.2020.9219561.

Sinha A, Malo P, Deb K. A review on bilevel optimization: from classical to evolutionary approaches and applications. IEEE Trans Evol Comput. 2017;22(2):276–95.

Article  Google Scholar 

Willis M-J, Hiden HG, Marenbach P, McKay B, Montague GA. Genetic programming: an introduction and survey of applications. Second international conference on genetic algorithms in engineering systems: innovations and applications. IET: 1997. p. 314–319.

Nanni L, Lumini A. Generalized Needleman-Wunsch algorithm for the recognition of T-cell epitopes. Expert Syst Appl. 2008;35(3):1463–7.

Article  Google Scholar 

Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K, Siemens C. Drebin: effective and explainable detection of android malware in your pocket. Ndss. 2014;14:23–6.

Google Scholar 

Wei F, Li Y, Roy S, Ou X, Zhou W. Deep ground truth analysis of current android malware. International conference on detection of intrusions and malware, and vulnerability assessment. Springer: 2017. p. 252–276.

Rashidi B, Fung C. Xdroid: an android permission control using hidden Markov chain and online learning. Communications and Network Security (CNS), 2016 IEEE Conference on. IEEE: 2016. p. 46–54.

Jeon S, Moon J. Malware-detection method with a convolutional recurrent neural network using opcode sequences. Inf Sci. 2020;535:1–15.

Article  MathSciNet  Google Scholar