A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system

References

• Abusnaina, A., A. Khormali, H. Alasmary, J. Park, A. Anwar, and A. Mohaisen. 2019b. “Adversarial Learning Attacks on Graph-based IoT Malware Detection Systems.” 2019 IEEE 39th International Conference on Distributed Computing Systems(ICDCS), 1296–1305. Dallas, TX, USA: IEEE.
   Google Scholar
• Abusnaina, A., H. Alasmary, M. Abuhamad, S. Salem, D. Nyang, and A. Mohaisen. 2019a. “Subgraph-Based Adversarial Examples against Graph-Based IoT Malware Detection Systems.” In Computational Data and Social Networks, edited by A. Tagarelli and H. Tong, 268–281. Vol. 11917. Cham: Springer International Publishing.
   Google Scholar
• Acharya, T., and P.-S. Tsai. 2007. “Computational Foundations of Image Interpolation Algorithms.” Ubiquity 2007 (October): 4. doi:10.1145/1322464.1317488.
   Google Scholar
• Agarwal, V., S. Sharma, and P. Agarwal. 2021. “IoT Based Smart Transport Management and Vehicle-to-vehicle Communication System.” In Computer Networks, Big Data and IoT, edited by Pandian A., Fernando X., Islam S.M.S, 709–716. Singapore: Springer.
   Google Scholar
• Al-kasassbeh, M., M. A. Abbadi, and A. M. Al-Bustanji. 2020. “LightGBM Algorithm for Malware Detection.” In Intelligent Computing, edited by K. Arai, S. Kapoor, and R. Bhatia, 391–403. Vol. 1230. Cham: Springer International Publishing.
   Google Scholar
• Al-Kasassbeh, M., M. Almseidin, K. Alrfou, and S. Kovacs. n.d. “Detection of IoT-botnet Attacks Using Fuzzy Rule Interpolation.” Journal of Intelligent & Fuzzy Systems, 39(1): 421-431.
   Google Scholar
• Al-Kasassbeh, M., S. Mohammed, M. Alauthman, and A. Almomani. 2020. “Feature Selection Using a Machine Learning to Classify a Malware.” In Handbook of Computer Networks and Cyber Security, edited by Gupta B., Perez G., Agrawal D., Gupta D., 889–904. Cham: Springer.
   Google Scholar
• Alieyan, K., A. Almomani, M. Anbar, M. Alauthman, R. Abdullah, and B. B. Gupta. 2021. “Dns Rule-based Schema to Botnet Detection.” Enterprise Information Systems 15 (4): 545–564. doi:10.1080/17517575.2019.1644673.
   Google Scholar
• Anderson, B., D. Quist, J. Neil, C. Storlie, and T. Lane. 2011. “Graph-based Malware Detection Using Dynamic Analysis.” Journal in Computer Virology 7 (4): 247–258. doi:10.1007/s11416-011-0152-x.
   Google Scholar
• Anderson, H. S., A. Kharkar, B. Filar, and P. Roth. 2017. “Evading Machine Learning Malware Detection.” Black Hat.
   Google Scholar
• Andriatsimandefitra, R., and V. V. T. Tong. 2015. “Capturing Android Malware Behaviour Using System Flow Graph.” International Conference on Network and System Security, 534–541. USA: Springer.
   Google Scholar
• Angrishi, K. 2017. “Turning Internet of Things (IoT) into Internet of Vulnerabilities (IoV): IoT Botnets.” arXiv preprint arXiv:1702.03681.
   Google Scholar
• Anthony, O., J. Odeyabinya, and S. Emmanuel. 2018. “Intrusion Detection in Internet of Things (IoT).” International Journal of Advanced Research in Computer Science 9 (1): 504-509.
   Google Scholar
• Antonakakis, M., T. April, M. Bailey, M. Bernhard, E. Bursztein, J. Cochran, … M. Kallitsis. 2017. “Understanding the Mirai Botnet.” 26th {USENIX} security symposium ({USENIX} Security 17), 1093–1110, USA.
   Google Scholar
• Arp, D., M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens. 2014. “Drebin: Effective and Explainable Detection of Android Malware in Your Pocket.” Ndss 14: 23–26.
   Google Scholar
• Arzt, S., S. Rasthofer, C. Fritz, E. Bodden, A. Bartel, J. Klein, P. McDaniel, D. Octeau, and P. McDaniel. 2014. “Flowdroid: Precise Context, Flow, Field, Object-sensitive and Lifecycle-aware Taint Analysis for Android Apps.” ACM SIGPLAN Notices 49 (6): 259–269. doi:10.1145/2666356.2594299.
   Google Scholar
• Azmoodeh, A., A. Dehghantanha, and K. R. Choo. 2019. “Robust Malware Detection for Internet of (Battlefield) Things Devices Using Deep Eigenspace Learning.” IEEE Transactions on Sustainable Computing 4 (1): 88–95, January. doi:10.1109/TSUSC.2018.2809665.
   Google Scholar
• Azmoodeh, A., A. Dehghantanha, M. Conti, and -K.-K. R. Choo. 2018. “Detecting Crypto ransomware in IoT Networks Based on Energy Consumption Footprint.” Journal of Ambient Intelligence and Humanized Computing 9 (4): 1141–1152. doi:10.1007/s12652-017-0558-5.
   Google Scholar
• Backes, M., and M. Nauman. 2017. “LUNA: Quantifying and Leveraging Uncertainty in Android Malware Analysis through Bayesian Machine Learning.” 2017 IEEE European Symposium on Security and Privacy (EuroS&P), 204–217. Paris, France: IEEE.
   Google Scholar
• Benzaid, C., and T. Taleb. 2020. “ZSM Security: Threat Surface and Best Practices.” IEEE Network 34 (3): 124–133. doi:10.1109/MNET.001.1900273.
   Google Scholar
• Bhunia, S. S., and M. Gurusamy. 2017. “Dynamic Attack Detection and Mitigation in IoT Using SDN.” 2017 27th International telecommunication networks and applications conference (ITNAC), 1–6. Melbourne, Australia: IEEE.
   Google Scholar
• Bläsing, T., L. Batyuk, A.-D. Schmidt, S. A. Camtepe, and S. Albayrak. 2010. “An Android Application Sandbox System for Suspicious Software Detection.” 2010 5th International Conference on Malicious and Unwanted Software, 55–62. USA: IEEE.
   Google Scholar
• Canfora, G., E. Medvet, F. Mercaldo, and C. A. Visaggio. 2016. “Acquiring and Analyzing App Metrics for Effective Mobile Malware Detection.” Proceedings of the 2016 ACM on International Workshop on Security And Privacy Analytics, 50–57, USA.
   Google Scholar
• Carlini, N., and D. Wagner. 2017. “Towards Evaluating the Robustness of Neural Networks.” 2017 IEEE Symposium on Security and Privacy (SP), 39–57. USA: IEEE.
   Google Scholar
• Ceron, J. M., K. Steding-Jessen, C. Hoepers, L. Z. Granville, and C. B. Margi. 2019. “Improving IoT Botnet Investigation Using an Adaptive Network Layer.” Sensors 19 (3): 727. doi:10.3390/s19030727.
   Google Scholar
• Chaabouni, N., M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki. thirdquarter 2019. “Network Intrusion Detection for IoT Security Based on Learning Techniques.” IEEE Communications Surveys Tutorials 21 (3): 2671–2701. doi:10.1109/COMST.2019.2896380.
   Google Scholar
• Chan, P. P., and W.-K. Song. 2014. “Static Detection of Android Malware by Using Permissions and API Calls.” 2014 International Conference on Machine Learning and Cybernetics Vol. 1, 82–87. USA: IEEE.
   Google Scholar
• Chen, H., J. Su, L. Qiao, and Q. Xin. 2018. “Malware Collusion Attack against SVM: Issues and Countermeasures.” Applied Sciences 8 (10): 1718. doi:10.3390/app8101718.
   Google Scholar
• Chen, S., M. Xue, Z. Tang, L. Xu, and H. Zhu. 2016. “Stormdroid: A Streaminglized Machine Learning-based System for Detecting Android Malware.” Proceedings of the 11th ACM on Asia Conference on Computer and Communications Security, 377–388.
   Google Scholar
• Chhabra, G. S., V. P. Singh, and M. Singh. 2020. “Cyber Forensics Framework for Big Data Analytics in IoT Environment Using Machine Learning.” Multimedia Tools and Applications 79 (23): 15881–15900. doi:10.1007/s11042-018-6338-1.
   Google Scholar
• Condoluci, M., and T. Mahmoodi. 2018. “Softwarization and Virtualization in 5G Mobile Networks: Benefits, Trends and Challenges.” Computer Networks 146: 65–84. doi:10.1016/j.comnet.2018.09.005.
   Google Scholar
• Cook, D. J., and L. B. Holder, Eds. 2006. Mining Graph Data. John Wiley & Sons.
   Google Scholar
• Dai, S., A. Tongaonkar, X. Wang, A. Nucci, and D. Song. 2013. “Networkprofiler: Towards Automatic Fingerprinting of Android Apps.” 2013 Proceedings IEEE INFOCOM, 809–817. USA: IEEE.
   Google Scholar
• Dang, H., Y. Huang, and E.-C. Chang. 2017. “Evading Classifiers by Morphing in the Dark.” Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 119–133.
   Google Scholar
• Davidsen, S. A., and M. Padmavathamma. 2015. “Multi-modal Evolutionary Ensemble Classification in Medical Diagnosis Problems.” 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 1366–1370. IEEE.
   Google Scholar
• De Donno, M., N. Dragoni, A. Giaretta, and A. Spognardi. 2018. “DDoS-capable IoT Malwares: Comparative Analysis and Mirai Investigation.” Security and Communication Networks 2018: 1–30. doi:10.1155/2018/7178164.
   Google Scholar
• Demontis, A., M. Melis, B. Biggio, D. Maiorca, D. Arp, K. Rieck, and F. Roli. 2017. “Yes, Machine Learning Can Be More Secure! a Case Study on Android Malware Detection.” IEEE Transactions on Dependable and Secure Computing 14 (5): 463–477. doi:10.1109/TDSC.2015.2484326.
   Google Scholar
• Desnos, A. 2011. “Androguard.”
   Google Scholar
• Detuxsandbox. 2020, December. detuxsandbox. Detuxsandbox/detux
   Google Scholar
• Dini, G., F. Martinelli, I. Matteucci, M. Petrocchi, A. Saracino, and D. Sgandurra. 2018. “Risk Analysis of Android Applications: A User-centric Solution.” Future Generation Computer Systems 80: 505–518. doi:10.1016/j.future.2016.05.035.
   Google Scholar
• Do˘gru, I. ˙. A., and Ö. Ki˙raz. 2018. “Web-based Android Malicious Software Detection and Classification System.” Applied Sciences 8 (9): 1622. doi:10.3390/app8091622.
   Google Scholar
• Dovom, E. M., A. Azmoodeh, A. Dehghantanha, D. E. Newton, R. M. Parizi, and H. Karimipour. 2019. “Fuzzy Pattern Tree for Edge Malware Detection and Categorization in IoT.” Journal of Systems Architecture 97: 1–7. doi:10.1016/j.sysarc.2019.01.017.
   Google Scholar
• Duda, R. O., P. E. Hart, and D. G. Stork. 2012. Pattern Classification. John Wiley & Sons.
   Google Scholar
• Elayan, H., M. Aloqaily, and M. Guizani. 2021. “Digital Twin for Intelligent Context-aware IoT Healthcare Systems.” IEEE Internet of Things Journal 8 (23): 16749–16757. doi:10.1109/JIOT.2021.3051158.
   Google Scholar
• Executable. 2020. “Executable and Linkable Format.” December. Wikipedia.
   Google Scholar
• Faris, H., I. Aljarah, M. A. Al-Betar, and S. Mirjalili. 2018. “Grey Wolf Optimizer: A Review of Recent Variants and Applications.” Neural Computing and Applications 30 (2): 413–435. doi:10.1007/s00521-017-3272-5.
   Google Scholar
• Farris, I., T. Taleb, Y. Khettab, and J. Song. 2018. “A Survey on Emerging SDN and NFV Security Mechanisms for IoT Systems.” IEEE Communications Surveys & Tutorials 21 (1): 812–837. doi:10.1109/COMST.2018.2862350.
   Google Scholar
• Feizollah, A., N. B. Anuar, R. Salleh, and A. W. A. Wahab. 2015. “A Review on Feature Selection in Mobile Malware Detection.” Digital Investigation 13: 22–37. doi:10.1016/j.diin.2015.02.001.
   Google Scholar
• Frigui, H., and O. Nasraoui. 2004. “Unsupervised Learning of Prototypes and Attribute Weights.” Pattern Recognition 37 (3): 567–581. doi:10.1016/j.patcog.2003.08.002.
   Google Scholar
• Gaurav, A., and A. K. Singh. 2017. “Super-Router: A Collaborative Filtering Technique against DDoS Attacks.” International Conference on Advanced Informatics for Computing Research, 294–305. Springer.
   Google Scholar
• Gaurav, A., B. B. Gupta, A. Castiglione, K. Psannis, and C. Choi. 2020. “A Novel Approach for Fake News Detection in Vehicular Ad-hoc Network (Vanet).” International conference on computational data and social networks, 386–397.
   Google Scholar
• Gaurav, A., B. B. Gupta, C.-H. Hsu, S. Yamaguchi, and K. T. Chui. 2021b. “Fog Layerbased Ddos Attack Detection Approach for Internet-of-things (IoTs) Devices.” In 2021 IEEE International Conference on Consumer Electronics (ICCE), 1–5.
   Google Scholar
• Gaurav, A., B. Gupta, C.-H. Hsu, D. Peraković, and F. J. G. Peñalvo. 2021a. “Deep Learning Based Approach for Secure Web of Things (Wot).” 2021 ieee international conference on communications workshops (icc workshops), 1–6.
   Google Scholar
• Gong, Z., W. Wang, and W.-S. Ku. 2017. “Adversarial and Clean Data are Not Twins.” arXiv preprint arXiv:1704.04960.
   Google Scholar
• Goodfellow, I. J., J. Shlens, and C. Szegedy. 2014. “Explaining and Harnessing Adversarial Examples.” arXiv preprint arXiv:1412.6572.
   Google Scholar
• Gou, Z., S. Yamaguchi, and B. Gupta. 2017. “Analysis of Various Security Issues and Challenges in Cloud Computing Environment: A Survey.” In Identity Theft: Breakthroughs in Research and Practice, 221–247. IGI global.
   Google Scholar
• Grosse, K., N. Papernot, P. Manoharan, M. Backes, and P. McDaniel. 2017b. “Adversarial Examples for Malware Detection.” In European Symposium on Research in Computer Security, 62–79. Springer.
   Google Scholar
• Grosse, K., P. Manoharan, N. Papernot, M. Backes, and P. McDaniel. 2017a. “On the (Statistical) Detection of Adversarial Examples.” arXiv preprint arXiv:1702.06280.
   Google Scholar
• Guizani, N., and A. Ghafoor. 2020. “A Network Function Virtualization System for Detecting Malware in Large IoT Based Networks.” IEEE Journal on Selected Areas in Communications 38 (6, June): 1218–1228. doi:10.1109/JSAC.2020.2986618.
   Google Scholar
• Gulati, N., and P. D. Kaur. 2021. “FriendCare-AAL: A Robust Social IoT Based Alert Generation System for Ambient Assisted Living.” Journal of Ambient Intelligence and Humanized Computing 1–28.
   Google Scholar
• Gupta, B. B., and M. Quamara. 2020. “An Overview of Internet of Things (IoT): Architectural Aspects, Challenges, and Protocols.” Concurrency and Computation: Practice and Experience 32 (21): e4946. doi:10.1002/cpe.4946.
   Google Scholar
• HaddadPajouh, H., A. Dehghantanha, R. Khayami, and -K.-K. R. Choo. 2018. “A Deep Recurrent Neural Network Based Approach for Internet of Things Malware Threat Hunting.” Future Generation Computer Systems 85 (August): 88–96. doi:10.1016/j.future.2018.03.007.
   Google Scholar
• Haddadpajouh, H., A. Mohtadi, A. Dehghantanaha, H. Karimipour, X. Lin, and K. K. R. Choo. 2020. “A Multi-Kernel and Meta-heuristic Feature Selection Approach for IoT Malware Threat Hunting in the Edge Layer.” IEEE Internet of Things Journal 8 (6): 4540-4547. doi: 10.1109/JIOT.2020.3026660.
   Google Scholar
• Han, K. S., J. H. Lim, B. Kang, and E. G. Im. 2015. “Malware Analysis Using Visualized Images and Entropy Graphs.” International Journal of Information Security 14 (1): 1–14. doi:10.1007/s10207-014-0242-0.
   Google Scholar
• Hasegawa, C., and H. Iyatomi. 2018. “One-dimensional Convolutional Neural Networks for Android Malware Detection.” 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), 99–102. Penang, Malaysia: IEEE.
   Google Scholar
• Hashemi, H., A. Azmoodeh, A. Hamzeh, and S. Hashemi. 2017. “Graph Embedding as a New Approach for Unknown Malware Detection.” Journal of Computer Virology and Hacking Techniques 13 (3): 153–166. doi:10.1007/s11416-016-0278-y.
   Google Scholar
• Hou, S., Y. Ye, Y. Song, and M. Abdulhayoglu. 2017. “Hindroid: An Intelligent Android Malware Detection System Based on Structured Heterogeneous Information Network.” Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1507–1515, New York, NY, USA.
   Google Scholar
• Huang, S., N. Papernot, I. Goodfellow, Y. Duan, and P. Abbeel. 2017. “Adversarial Attacks on Neural Network Policies.” arXiv preprint arXiv:1702.02284.
   Google Scholar
• Hussain, F., R. Hussain, S. A. Hassan, and E. Hossain. 2020. “Machine Learning in IoT Security: Current Solutions and Future Challenges.” IEEE Communications Surveys & Tutorials, vol. 22, no. 3, pp. 1686-1721, thirdquarter 2020, doi: 10.1109/COMST.2020.2986444.
   Google Scholar
• Jagadish, H. V. 1997. “Analysis of the Hilbert Curve for Representing Two-dimensional Space.” Information Processing Letters 62 (1): 17–22. doi:10.1016/S0020-0190(97)00014-8.
   Google Scholar
• Jain, A. K., and B. Gupta. 2021. “A Survey of Phishing Attack Techniques, Defence Mechanisms and Open Research Challenges.” Enterprise Information Systems 1–39. doi:10.1080/17517575.2021.1896786.
   Google Scholar
• Jiang, Y., M. Liu, H. Peng, and M. Z. A. Bhuiyan. 2021. “A Reliable Deep Learning-based Algorithm Design for IoT Load Identification in Smart Grid.” Ad Hoc Networks 123: 102643. doi:10.1016/j.adhoc.2021.102643.
   Google Scholar
• John, A., T. A. Kumar, M. Adimoolam, and A. Blessy. 2021. “Energy Management and Monitoring Using IoT with Cupcarbon Platform.” In Green Computing in Smart Cities: Simulation and Techniques, edited by alusamy B., Chilamkurti N., Kadry S., 189–206. Springer.
   Google Scholar
• Jordaney, R., K. Sharad, S. K. Dash, Z. Wang, D. Papini, I. Nouretdinov, and K. Cavallaro. 2017. “Transcend: Detecting Concept Drift in Malware Classification Models.” 26th {USENIX} Security Symposium ({USENIX} Security 17), 625–642.
   Google Scholar
• Kang, H., J.-W. Jang, A. Mohaisen, and H. K. Kim. 2015. “Detecting and Classifying Android Malware Using Static Analysis along with Creator Information.” International Journal of Distributed Sensor Networks 11 (6): 479174. doi:10.1155/2015/479174.
   Google Scholar
• Karbab, E. B., M. Debbabi, A. Derhab, and D. Mouheb. 2018. “MalDozer: Automatic Framework for Android Malware Detection Using Deep Learning.” Digital Investigation 24: S48–S59. doi:10.1016/j.diin.2018.01.007.
   Google Scholar
• Karimipour, H., A. Dehghantanha, R. M. Parizi, -K.-K. R. Choo, and H. Leung. 2019. “A Deep and Scalable Unsupervised Machine Learning System for Cyber-attack Detection in Large-scale Smart Grids.” IEEE Access 7: 80778–80788. doi:10.1109/ACCESS.2019.2920326.
   Google Scholar
• Khoda, M., T. Imam, J. Kamruzzaman, I. Gondal, and A. Rahman. 2020. “Robust Malware Defense in Industrial IoT Applications Using Machine Learning with Selective Adversarial Samples.” IEEE Transactions on Industry Applications 56 (4): 4415–4424, July.
   Google Scholar
• Kolias, C., G. Kambourakis, A. Stavrou, and J. Voas. 2017. “DDoS in the IoT: Mirai and Other Botnets.” Computer 50 (7): 80–84. doi:10.1109/MC.2017.201.
   Google Scholar
• Kumar, A., K. Kuppusamy, and G. Aghila. 2018. “FAMOUS: Forensic Analysis of MObile Devices Using Scoring of Application Permissions.” Future Generation Computer Systems 83: 158–172. doi:10.1016/j.future.2018.02.001.
   Google Scholar
• Kumar, R., X. Zhang, R. U. Khan, and A. Sharif. 2019a. “Research on Data Mining of Permission-induced Risk for Android IoT Devices.” Applied Sciences 9 (2): 277. doi:10.3390/app9020277.
   Google Scholar
• Kumar, R., X. Zhang, W. Wang, R. U. Khan, J. Kumar, and A. Sharif. 2019b. “A Multimodal Malware Detection Technique for Android IoT Devices Using Various Features.” IEEE Access 7: 64411–64430. doi:10.1109/ACCESS.2019.2916886.
   Google Scholar
• Le, H.-V., and Q.-D. Ngo. 2020. “V-Sandbox for Dynamic Analysis IoT Botnet.” IEEE Access 8: 145768–145786. doi:10.1109/ACCESS.2020.3014891.
   Google Scholar
• Lei, T., Z. Qin, Z. Wang, Q. Li, and D. Ye. 2019. “EveDroid: Event-Aware Android Malware Detection against Model Degrading for IoT Devices.” IEEE Internet of Things Journal 6 (4, August): 6668–6680. doi:10.1109/JIOT.2019.2909745.
   Google Scholar
• Liu, L., and B. Wang. 2016. “Malware Classification Using Gray-scale Images and Ensemble Learning.” 2016 3rd International Conference on Systems and Informatics (ICSAI), 1018–1022. Shanghai, China: IEEE.
   Google Scholar
• Liu, Z., L. Zhang, Q. Ni, J. Chen, R. Wang, Y. Li, and Y. He. 2018. “An Integrated Architecture for IoT Malware Analysis and Detection.” International Conference on Internet of Things as a Service, 127–137. Cham: Springer.
   Google Scholar
• Machiry, A., R. Tahiliani, and M. Naik. 2013. “Dynodroid: An Input Generation System for Android Apps.” Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering, 224–234, New York, NY, USA.
   Google Scholar
• Mani, N., M. Moh, and T.-S. Moh. 2021. “Defending Deep Learning Models against Adversarial Attacks.” International Journal of Software Science and Computational Intelligence (IJSSCI) 13 (1): 72–89. doi:10.4018/IJSSCI.2021010105.
   Google Scholar
• Marques, G., A. K. Bhoi, C. Victor Hugo, and K. Hareesha. 2021. IoT in Healthcare and Ambient Assisted Living. Singapore: Springer.
   Google Scholar
• Marzano, A., D. Alexander, O. Fonseca, E. Fazzion, C. Hoepers, K. Steding-Jessen, … W. Meira. 2018. “The Evolution of Bashlite and Mirai IoT Botnets.” 2018 IEEE Symposium on Computers and Communications (ISCC), 00813–00818. Natal, Brazil: IEEE.
   Google Scholar
• Mehmood, M. Y., A. Oad, M. Abrar, H. M. Munir, S. F. Hasan, H. Muqeet, and N. A. Golilarz. 2021. “Edge Computing for IoT-enabled Smart Grid.” Security and Communication Networks 2021: 1–16. doi:10.1155/2021/5524025.
   Google Scholar
• Meidan, Y., M. Bohadana, Y. Mathov, Y. Mirsky, A. Shabtai, D. Breitenbacher, and Y. Elovici. 2018. “N-baiot—network-based Detection of IoT Botnet Attacks Using Deep Autoencoders.” IEEE Pervasive Computing 17 (3): 12–22. doi:10.1109/MPRV.2018.03367731.
   Google Scholar
• Mirjalili, S. 2015. “How Effective Is the Grey Wolf Optimizer in Training Multi-layer Perceptrons.” Applied Intelligence 43 (1): 150–161. doi:10.1007/s10489-014-0645-7.
   Google Scholar
• Mishra, A., N. Gupta, and B. Gupta. 2021. “Defense Mechanisms against Ddos Attack Based on Entropy in Sdn-cloud Using Pox Controller.” Telecommunication Systems 77 (1): 47–62. doi:10.1007/s11235-020-00747-w.
   Google Scholar
• Moh, M., and R. Raju. 2018. “Machine Learning Techniques for Security of Internet of Things (IoT) and Fog Computing Systems.” 2018 International Conference on High Performance Computing Simulation (HPCS), July, 709–715, Orleans, France.
   Google Scholar
• Monnappa, K. 2015. “Automating Linux Malware Analysis Using Limon Sandbox.” Black Hat Europe 2015, 1-13.
   Google Scholar
• Moosavi-Dezfooli, S.-M., A. Fawzi, and P. Frossard. 2016. “Deepfool: A Simple and Accurate Method to Fool Deep Neural Networks.” Proceedings of the IEEE conference on computer vision and pattern recognition, 2574–2582, Las Vegas, NV, USA.
   Google Scholar
• Mwangi, K. E., S. Masupe, and J. Mandu. 2020. “Transfer Learning for Internet of Things Malware Analysis.” In Intelligent Computing Paradigm and Cutting-edge Technologies, edited by L. C. Jain, S.-L. Peng, B. Alhadidi, and S. Pal, 198–208. Vol. 9. Cham: Springer International Publishing.
   Google Scholar
• Nakhodchi, S., A. Upadhyay, and A. Dehghantanha. 2020. “A Comparison between Different Machine Learning Models for IoT Malware Detection.” In Security of Cyber-Physical Systems, edited by H. Karimipour, P. Srikantha, H. Farag, and J. Wei-Kocsis, 195–202. Cham: Springer International Publishing.
   Google Scholar
• Ngo, Q.-D., H.-T. Nguyen, V.-H. Le, and D.-H. Nguyen. 2020. “A Survey of IoT Malware and Detection Methods Based on Static Features.” ICT Express 6 (4, December): 280–286. doi:10.1016/j.icte.2020.04.005.
   Google Scholar
• Nguyen, K. D. T., T. M. Tuan, S. H. Le, A. P. Viet, M. Ogawa, and N. L. Minh. 2018. “Comparison of Three Deep Learning-based Approaches for IoT Malware Detection.” 2018 10th International Conference on Knowledge and Systems Engineering (KSE), November, 382–388, Ho Chi Minh City, Vietnam.
   Google Scholar
• Ojo, M., D. Adami, and S. Giordano. 2016. “A SDN-IoT Architecture with NFV Implementation.” 2016 IEEE Globecom Workshops (GC Wkshps), 1–6. Washington, DC, USA: IEEE.
   Google Scholar
• Oktavianto, D., and I. Muhardianto. 2013. Cuckoo Malware Analysis. Packt Publishing .
   Google Scholar
• Onwuzurike, L., E. Mariconti, P. Andriotis, E. D. Cristofaro, G. Ross, and G. Stringhini. 2019. “MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version).” ACM Transactions on Privacy and Security (TOPS) 22 (2): 1–34. doi:10.1145/3313391.
   Google Scholar
• Pa, Y. M. P., S. Suzuki, K. Yoshioka, T. Matsumoto, T. Kasama, and C. Rossow. 2016. “IoTPOT: A Novel Honeypot for Revealing Current IoT Threats.” Journal of Information Processing 24 (3): 522–533. doi:10.2197/ipsjjip.24.522.
   Google Scholar
• Padawan. (n.d.) “Padawan Live.” (Accessed 5 July 2021. https://padawan.s3.eurecom.fr/about
   Google Scholar
• Pajouh, H. H., A. Dehghantanha, R. Khayami, and -K.-K. R. Choo. 2018. “Intelligent OS X Malware Threat Detection with Code Inspection.” Journal of Computer Virology and Hacking Techniques 14 (3): 213–223. doi:10.1007/s11416-017-0307-5.
   Google Scholar
• Papernot, N., P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami. 2016a. “The Limitations of Deep Learning in Adversarial Settings.” 2016 IEEE European symposium on security and privacy (EuroS&P), 372–387. Saarbruecken, Germany: IEEE.
   Google Scholar
• Papernot, N., P. McDaniel, X. Wu, S. Jha, and A. Swami. 2016b. “Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks.” 2016 IEEE Symposium on Security and Privacy (SP), 582–597. San Jose, CA, USA: IEEE.
   Google Scholar
• Peizhuang, W. 1983. “Pattern Recognition with Fuzzy Objective Function Algorithms (James C. Bezdek).” SIAM Review 25 (3): 442. doi:10.1137/1025116.
   Google Scholar
• Peters, W., A. Dehghantanha, R. M. Parizi, and G. Srivastava. 2020. “A Comparison of State-of-the-Art Machine Learning Models for OpCode-Based IoT Malware Detection.” In Handbook of Big Data Privacy, edited by -K.-K. R. Choo and A. Dehghantanha, 109–120. Cham: Springer International Publishing.
   Google Scholar
• Prokofiev, A. O., Y. S. Smirnova, and V. A. Surov. 2018. “A Method to Detect Internet of Things Botnets.” 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), 105–108. Moscow and St. Petersburg, Russia: IEEE.
   Google Scholar
• Radare2. (n.d.) “Radare2.”Accessed 05 July 2021. https://www.radare.org/r/
   Google Scholar
• Ramos, J. 2003. “Using Tf-idf to Determine Word Relevance in Document Queries.” Proceedings of the First Instructional Conference on Machine Learning Vol. 242, 133–142, New Jersey, USA.
   Google Scholar
• Rebelo, R. M. L., S. C. F. Pereira, and M. M. Queiroz. 2021. “The Interplay between the Internet of Things and Supply Chain Management: Challenges and Opportunities Based on a Systematic Literature Review.” Benchmarking: An International Journal. doi:10.1108/BIJ-02-2021-0085.
   Google Scholar
• Reddy, D. K. S., and A. K. Pujari. 2006. “N-gram Analysis for Computer Virus Detection.” Journal in Computer Virology 2 (3): 231–239. doi:10.1007/s11416-006-0027-8.
   Google Scholar
• REMnux. n.d. “REMnux: A Linux Toolkit for Malware Analysts.” Accessed 05 July 2021. https://remnux.org/
   Google Scholar
• Rhode, M., P. Burnap, and K. Jones. 2018. “Early-stage Malware Prediction Using Recurrent Neural Networks.” Computers & Security 77: 578–594. doi:10.1016/j.cose.2018.05.010.
   Google Scholar
• Sah, S., A. K. Agrawal, and P. Khatri. 2019. “Physical Data Acquisition from Virtual Android Phone Using Genymotion.” In International Conference on Sustainable Communication Networks and Application, edited by Karrupusamy P., Chen J., Shi Y., 286–296. Cham: Springer.
   Google Scholar
• Sahoo, S. R., and B. B. Gupta. 2019. “Classification of Various Attacks and Their Defence Mechanism in Online Social Networks: A Survey.” Enterprise Information Systems 13 (6): 832–864. doi:10.1080/17517575.2019.1605542.
   Google Scholar
• Sahoo, S. R., and B. B. Gupta. 2020. “Classification of Spammer and Nonspammer Content in Online Social Network Using Genetic Algorithm-based Feature Selection.” Enterprise Information Systems 14 (5): 710–736. doi:10.1080/17517575.2020.1712742.
   Google Scholar
• Santos, I., F. Brezo, X. Ugarte-Pedrero, and P. G. Bringas. 2013. “Opcode Sequences as Representation of Executables for Data-mining-based Unknown Malware Detection.” Information Sciences 231: 64–82. doi:10.1016/j.ins.2011.08.020.
   Google Scholar
• Saremi, S., S. Z. Mirjalili, and S. M. Mirjalili. 2015. “Evolutionary Population Dynamics and Grey Wolf Optimizer.” Neural Computing and Applications 26 (5): 1257–1263. doi:10.1007/s00521-014-1806-7.
   Google Scholar
• Sarivougioukas, J., and A. Vagelatos. 2020. “Modeling Deep Learning Neural Networks with Denotational Mathematics in Ubihealth Environment.” International Journal of Software Science and Computational Intelligence (IJSSCI) 12 (3): 14–27. doi:10.4018/IJSSCI.2020070102.
   Google Scholar
• Shabtai, A., R. Moskovitch, C. Feher, S. Dolev, and Y. Elovici. 2012. “Detecting Unknown Malicious Code by Applying Classification Techniques on Opcode Patterns.” Security Informatics 1 (1): 1. doi:10.1186/2190-8532-1-1.
   Google Scholar
• Shaerpour, K., A. Dehghantanha, and R. Mahmod. 2013. “Trends in Android Malware Detection.” Journal of Digital Forensics, Security and Law 8 (3): 2.
   Google Scholar
• Sharmeen, S., S. Huda, J. H. Abawajy, W. N. Ismail, and M. M. Hassan. 2018. “Malware Threats and Detection for Industrial Mobile-IoT Networks.” IEEE Access 6: 15941–15957. doi:10.1109/ACCESS.2018.2815660.
   Google Scholar
• Shire, R., S. Shiaeles, K. Bendiab, B. Ghita, and N. Kolokotronis. 2019. “Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation.” In Internet of Things, Smart Spaces, and Next Generation Networks and Systems, edited by O. Galinina, S. Andreev, S. Balandin, and Y. Koucheryavy, 65–76. Vol. 11660. Cham: Springer International Publishing.
   Google Scholar
• Sihwail, R., K. Omar, and K. Z. Ariffin. 2018. “A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis.” International Journal on Advanced Science, Engineering and Information Technology 8 (4–2): 1662. doi:10.18517/ijaseit.8.4-2.6827.
   Google Scholar
• Song, Q., Y. Chen, Y. Zhong, K. Lan, S. Fong, and R. Tang. 2021. “A Supply-chain System Framework Based on Internet of Things Using Blockchain Technology.” ACM Transactions on Internet Technology (TOIT) 21 (1): 1–24. doi:10.1145/3409798.
   Google Scholar
• Souri, A., and R. Hosseini. 2018. “A State-of-the-art Survey of Malware Detection Approaches Using Data Mining Techniques.” Human-centric Computing and Information Sciences 8 (1): 3. doi:10.1186/s13673-018-0125-x.
   Google Scholar
• Su, X., D. Zhang, W. Li, and K. Zhao. 2016. “A Deep Learning Approach to Android Malware Feature Learning and Detection.” 2016 IEEE Trustcom/BigDataSE/ISPA, 244–251. Tianjin, China: IEEE.
   Google Scholar
• Sugunan, K., T. G. Kumar, and K. Dhanya. 2018. “Static and Dynamic Analysis for Android Malware Detection.” In Advances in Big Data and Cloud Computing, edited by Rajsingh E., Veerasamy J., Alavi A., Peter J., 147–155. Singapore: Springer.
   Google Scholar
• Sui, L. 2016. “Strategy Analytics: Android Captures Record 88 Percent Share of Global Smartphone Shipments in Q3 2016.” Strategy Anal Res Experts Anal 28: 28–35.
   Google Scholar
• Szegedy, C., W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus. 2013. “Intriguing Properties of Neural Networks.” arXiv preprint arXiv:1312.6199
   Google Scholar
• Tahir, R. 2018. “A Study on Malware and Malware Detection Techniques.” International Journal of Education and Management Engineering 8 (2): 20. doi:10.5815/ijeme.2018.02.03.
   Google Scholar
• Tan, Y. 2016. Artificial Immune System: Applications in Computer Security. Two thousand, sixteenth ed. John Wiley & Sons.
   Google Scholar
• Tewari, A., and B. B. Gupta. 2020. “Secure Timestamp-based Mutual Authentication Protocol for IoT Devices Using RFID Tags.” International Journal on Semantic Web and Information Systems (IJSWIS) 16 (3): 20–34. doi:10.4018/IJSWIS.2020070102.
   Google Scholar
• Thoma, M., H. Cheng, A. Gretton, J. Han, H.-P. Kriegel, A. Smola, … K. M. Borgwardt. 2010. “Discriminative Frequent Subgraph Mining with Optimality Guarantees.” Statistical Analysis and Data Mining: The ASA Data Science Journal 3 (5): 302–318. doi:10.1002/sam.10084.
   Google Scholar
• Tien, C.-W., S.-W. Chen, T. Ban, and S.-Y. Kuo. 2020. “Machine Learning Framework to Analyze IoT Malware Using ELF and Opcode Features.” Digital Threats: Research and Practice 1 (1): 5:1–5:19, March.
   Google Scholar
• Uhrıcek, D. 2020. “LiSa–Multiplatform Linux Sandbox for Analyzing IoT Malware.”
   Google Scholar
• UI. n.d. “UI/Application Exerciser Monkey — Android Developers.” Accessed 05 July 2021. https://developer.android.com/studio/test/monkey
   Google Scholar
• Vasan, D., M. Alazab, S. Venkatraman, J. Akram, and Z. Qin. 2020. “MTHAEL: Cross-Architecture IoT Malware Detection Based on Neural Network Advanced Ensemble Learning.” IEEE Transactions on Computers 69 (11, November): 1654–1667. doi:10.1109/TC.2020.3015584.
   Google Scholar
• Vignau, B., R. Khoury, and S. Hallé. 2019. “10 Years of IoT Malware: A Feature-based Taxonomy.” 2019 IEEE 19th International Conference on Software Quality, Reliability and Security Companion (QRS-C), 458–465. Sofia, Bulgaria: IEEE.
   Google Scholar
• Wang, W., M. Zhao, Z. Gao, G. Xu, H. Xian, Y. Li, and X. Zhang. 2019. “Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions.” IEEE Access 7: 67602–67631. doi:10.1109/ACCESS.2019.2918139.
   Google Scholar
• Wang, X., K. Sun, Y. Wang, and J. Jing. 2015. “DeepDroid: Dynamically Enforcing Enterprise Policy on Android Devices.” NDSS.
   Google Scholar
• Wang, X., Y. Wang, and L. Wang. 2004. “Improving Fuzzy C-means Clustering Based on Feature-weight Learning.” Pattern Recognition Letters 25 (10): 1123–1132. doi:10.1016/j.patrec.2004.03.008.
   Google Scholar
• Wen, H., W. Zhang, Y. Hu, Q. Hu, H. Zhu, and L. Sun. 2019. “Lightweight IoT Malware Visualization Analysis via Two-Bits Networks.” In Wireless Algorithms, Systems, and Applications, edited by E. S. Biagioni, Y. Zheng, and S. Cheng, 613–621. Vol. 11604. Cham: Springer International Publishing.
   Google Scholar
• Witten, D. M., and R. Tibshirani. 2010. “A Framework for Feature Selection in Clustering.” Journal of the American Statistical Association 105 (490): 713–726. doi:10.1198/jasa.2010.tm09415.
   Google Scholar
• Wong, M. Y., and D. Lie. 2016. “IntelliDroid: A Targeted Input Generator for the Dynamic Analysis of Android Malware.” NDSS 16: 21–24.
   Google Scholar
• Wu, T.-Y., T. Wang, Y.-Q. Lee, W. Zheng, S. Kumari, and S. Kumar. 2021. “Improved Authenticated Key Agreement Scheme for Fog-driven IoT Healthcare System.” Wireless Networks 2021, 25: 4737–4750.
   Google Scholar
• Yan, L. K., and H. Yin. 2012. “Droidscope: Seamlessly Reconstructing the {OS} and Dalvik Semantic Views for Dynamic Android Malware Analysis.” Presented as part of the 21 st {USENIX} Security Symposium ({USENIX} Security 12), 569–584, Bellevue, WA.
   Google Scholar
• Yang, C., Z. Xu, G. Gu, V. Yegneswaran, and P. Porras. 2014. “Droidminer: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications.” In: Kutyłowski M., Vaidya J. (eds) Computer Security - ESORICS 2014. ESORICS 2014. Lecture Notes in Computer Science, vol 8712. Springer, Cham.10.1007/9783.319112039.10
   Google Scholar
• Yang, M.-S. 1993. “A Survey of Fuzzy Clustering.” Mathematical and Computer Modelling 18 (11): 1–16. doi:10.1016/0895-7177(93)90202-A.
   Google Scholar
• Yang, W., D. Kong, T. Xie, and C. A. Gunter. 2017. “Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps.” Proceedings of the 33rd Annual Computer Security Applications Conference, 288–302, New York, NY, USA.
   Google Scholar
• Yaqoob, I., I. A. T. Hashem, A. Ahmed, S. A. Kazmi, and C. S. Hong. 2019. “Internet of Things Forensics: Recent Advances, Taxonomy, Requirements, and Open Challenges.” Future Generation Computer Systems 92: 265–275. doi:10.1016/j.future.2018.09.058.
   Google Scholar
• Yerima, S. Y., S. Sezer, and I. Muttik. 2014. “Android Malware Detection Using Parallel Machine Learning Classifiers.” 2014 Eighth International Conference on Next Generation Mobile Apps, Services and Technologies, 37–42. Oxford, UK: IEEE.
   Google Scholar
• Yeung, D. S., and X. Wang. 2002. “Improving Performance of Similarity-based Clustering by Feature Weight Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (4): 556–561. doi:10.1109/34.993562.
   Google Scholar
• Zahra, S. R., and M. A. Chishti. 2021. “Smart Cities Pilot Projects: An IoT Perspective.” In Smart Cities: A Data Analytics Perspective, edited by Khan M.A., Algarni F., Quasim M.T., 231–255. Cham: Springer. doi: 10.1007/978-3-030-60922-1_12
   Google Scholar
• Zarni Aung, W. Z. 2013. “Permission-based Android Malware Detection.” International Journal of Scientific & Technology Research 2 (3): 228–234.
   Google Scholar
• Zhang, J., Z. Qin, H. Yin, L. Ou, S. Xiao, and Y. Hu. 2016. “Malware Variant Detection Using Opcode Image Recognition with Small Training Sets.” 2016 25th International Conference on Computer Communication and Networks (ICCCN), 1–9. Waikoloa, HI, USA: IEEE.
   Google Scholar
• Zhang, Z.-K., M. C. Y. Cho, C.-W. Wang, C.-W. Hsu, C.-K. Chen, and S. Shieh. 2014. “IoT Security: Ongoing Challenges and Research Opportunities.” 2014 IEEE 7th international conference on service-oriented computing and applications, 230–234. Matsue, Japan: IEEE.
   Google Scholar
• Zhou, D., Z. Yan, Y. Fu, and Z. Yao. 2018. “A Survey on Network Data Collection.” Journal of Network and Computer Applications 116: 9–23. doi:10.1016/j.jnca.2018.05.004.
   Google Scholar
• Zhou, Z., A. Gaurav, B. Gupta, H. Hamdi, and N. Nedjah. 2021. “A Statistical Approach to Secure Health Care Services from Ddos Attacks during Covid-19 Pandemic.” Neural Computing and Applications 1–14. doi:10.1007/s00521-021-06389-6.
   Google Scholar