Crisis Management of Android Botnet Detection Using Adaptive Neuro-Fuzzy Inference System

Darkreading (2013) 150% increase in mobile online shopping black Friday through cyber Monday. Available http://www.darkreading.com/mobile/150-increase-in-mobile-online-shopping-b/240164440. Accessed 1 June 2014

CNET (2014) Android nabs 53% of US smartphone activations in Q1. Available http://www.cnet.com/news/android-nabs-53-percent-of-us-smartphone-activations-in-q1. Accessed 1 June 2014

Techcrunch (2013) Android accounted for 79% of all mobile malware in 2012, 96% in Q4 alone, says f-secure. Available http://techcrunch.com/2013/03/07/f-secure-android-accounted-for-79-of-all-mobile-malware-in-2012-96-in-q4-alone/. Accessed 1 Jan 2013

F-Secure (2014) Q1 2014 mobile threat report. Available http://www.f-secure.com/weblog/archives/00002699.html. Accessed 1 June 2014

F-Secure (2014) Backdoor:Android/Dendroid.A. Available http://www.f-secure.com/v-descs/backdoor_android_dendroid_a.shtml. Accessed 1 June 2014

Symantec (2013) Android madware and malware trends. Available http://www.symantec.com/connect/blogs/android-madware-and-malware-trends. Accessed 1 June 2014

Castellano G, Fanelli AM (2000) Variable selection using neural-network models. Neurocomputing 31(1–4):1–13

Dieterle F, Busche S, Gauglitz G (2003) Growing neural networks for a multivariate calibration and variable selection of time-resolved measurements. Anal Chim Acta 490(1–2):71–83

Cibas T, Soulié FF, Gallinari P, Raudys S (1996) Variable selection with neural networks. Neurocomputing 12(2–3):223–248

Andersson FO, Åberg M, Jacobsson SP (2000) Algorithmic approaches for studies of variable influence, contribution and selection in neural networks. Chemom Intell Lab Syst 51(1):61–72

Sofge D (2002) Using genetic algorithm based variable selection to improve neural network models for real-world systems. In: Proceedings of the international conference on machine learning and applications, Las Vegas, pp 16–19

Chan KY, Ling SH, Dillon TS, Nguyen HT (2011) Diagnosis of hypoglycemic episodes using a neural network based rule discovery system. Expert Syst Appl 38(8):9799–9808

Kwong CK, Wong TC, Chan KY (2009) A methodology of generating customer satisfaction models for new product development using a neuro-fuzzy approach. Expert Syst Appl 36(8):11262–11270

Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

Sarat CN, Bijan BM (2019) A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction. Financ Innov 5:38

Elaheh Y, Mehrbakhsh N, Liyana S, Shahla A, Othman I (2019) Development of a SaaS adoption decision-making model using a new hybrid MCDM approach. Int J Inf Technol Decis Mak 18(06):1845–1874

Petković D, Issa M, Pavlović ND, Pavlović NT, Zentner L (2012) Adaptive neuro-fuzzy estimation of conductive silicone rubber mechanical properties. Expert Syst Appl 39(10):9477–9482

Petković D, Ćojbašić Ž (2012) Adaptive neuro-fuzzy estimation of autonomic nervous system parameters effect on heart rate variability. Neural Comput Appl 21(8):2065–2070

Shariati M, Mafipour MS, Mehrabi P, Shariati A, Toghroli A, Trung NT, Salih MN (2020) A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques. Eng Comput, 1–21

Shariati M, Mafipour MS, Haido JH, Yousif ST, Toghroli A, Trung NT, Shariati A (2020) Identification of the most influencing parameters on the properties of corroded concrete beams using an adaptive neuro-fuzzy inference system (ANFIS). Steel Compos Struct 34(1):155

Toghroli A, Mohammadhassani M, Suhatril M, Shariati M, Ibrahim Z (2014) Prediction of shear capacity of channel shear connectors using the ANFIS model. Steel Compos Struct 17(5):623–639

Shariati M, Azar SM, Arjomand MA, Tehrani HS, Daei M, Safa M (2020) Evaluating the impacts of using piles and geosynthetics in reducing the settlement of fine-grained soils under static load. Geomech Eng 20(2):87

Armaghani DJ, Mirzaei F, Shariati M, Trung NT, Shariati M, Trnavac D (2020) Hybrid ANN-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomech Eng 20(3):191

Areed FG, Haikal AY, Mohammed RH (2010) Adaptive neuro-fuzzy control of an induction motor. Ain Shams Eng J 1(1):71–78

Petković D, Issa M, Pavlović ND, Zentner L, Ćojbašić Ž (2012) Adaptive neuro fuzzy controller for adaptive compliant robotic gripper. Expert Syst Appl 39(18):13295–13304

Tian L, Collins C (2005) Adaptive neuro-fuzzy control of a flexible manipulator. Mechatronics 15(10):1305–1320

Aldair AA, Wang WJ (2011) Design an intelligent controller for full vehicle nonlinear active suspension systems. Int J Smart Sensing Intell Syst 4(2):224–243

Dastranj MR, Ebroahimi E, Changizi N, Sameni E (2011) Control DC motorspeed with adaptive neuro-fuzzy control (ANFIS). Aust J Basic Appl Sci 5(10):1499–1504

Manoj SBA (2011) Identification and control of nonlinear systems using soft computing techniques. Int J Model Optim 1(1):24

Yajin Z, Xuxian J (2012) Dissecting android malware: characterization and evolution. In: Proceedings of the 2012 IEEE symposium on security and privacy (SP), San

tPacketCapturePro (2013) tPacketCapture Pro - android apps on Google Play. Available https://play.google.com/store/apps/details?id=jp.co.taosoftware.android.packetcapturepro. Accessed 1 June 2013

tshark (2013) tshark - The Wireshark Network analyzer 1.10.0. Available http://www.wireshark.org/docs/man-pages/tshark.html. Accessed 1 January 2013