Hybrid dual-channel convolution neural network (DCCNN) with spider monkey optimization (SMO) for cyber security threats detection in internet of things

Measurement: Sensors - Tập 27 - Trang 100783 - 2023
P. Vijayalakshmi1, D. Karthika2
1Research Scholar, P.K.R Arts College for Women, Gobichettipalayam, Tamil Nadu, India
2School of Computer Science, VET Institute of Arts and Science (Co-education) College, Erode, Tamil Nadu, India

Tài liệu tham khảo

Srinivasan, 2019, A review on the different types of Internets of Things (IoT), J. Adv. Res. Dyn. Control Syst, 11, 154 Karbab, 2017 Zarpelao, 2017, A survey of intrusion detection in internet of things, J. Netw. Comput. Appl., 84, 25, 10.1016/j.jnca.2017.02.009 Ashton, 2009, That ‘internet of things’ thing, RFID J, 22, 97 Egele, 2012, ‘A survey on automated dynamic malware-analysis techniques and tools, ACM Comput. Surv., 44, 6, 10.1145/2089125.2089126 Imran, 2018, An enhanced framework for extrinsic plagiarism avoidance for research article, Tech. J., 23, 84 Borgia, 2014, The internet of things vision: key features, applications and open issues, Comput. Commun., 54, 1, 10.1016/j.comcom.2014.09.008 Restuccia, 2018, Securing the internet of things: new perspectives and research challenges, IEEE Internet Things J., 1, 1 Moustafa, 2018, An ensemble intrusion detection technique based on proposed statistical flow features for protecting network traffic of internet of things, IEEE Internet Things J., 6, 4815, 10.1109/JIOT.2018.2871719 Shafiq, 2020, CorrAUC: a malicious bot-IoT traffic detection method in IoT network using machine-learning techniques, IEEE Internet Things J., 8, 3242, 10.1109/JIOT.2020.3002255 Naeem, 2020, 1 Sitnikova, 2019, Leveraging deep learning models for ransomware detection in the industrial internet of things environment, Proceedings of International Conference on Military Communications and Information Systems, 1 Diro, 2018, vol. 82, 761 Parra, 2020, vol. 163, 1 HaddadPajouh, 2018, A deep recurrent neural network based approach for internet of things malware threat hunting, Future Generat. Comput. Syst., 85, 88, 10.1016/j.future.2018.03.007 Kumar, 2018, ‘Malicious code detection based on image processing using deep learning, Proc. Int. Conf. Comput. Artif. Intell., 81 Nataraj, 2011, Malware images: visualization and automatic classification, Proc. 8th Int. Symp. Vis. Cyber Secur, 4 Baylor, 2017, tensorflow-based production-scale machine learning platform, inProc. 23rd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 1387, 10.1145/3097983.3098021 Bandara, 2012, Detection of source code plagiarism using machine learning approach, Int. J. Comput. Theory Eng., 4, 674, 10.7763/IJCTE.2012.V4.555 Cosma, 2012, An approach to source-code plagiarism detection and investigation using latent semantic analysis, IEEE Trans. Comput., 61, 379, 10.1109/TC.2011.223 Son, 2013, An application for plagiarized source code detection based on a parse tree kernel, Eng. Appl. Artif. Intell., 26, 1911, 10.1016/j.engappai.2013.06.007 Ullah, 2020, Plagiarism detection in students’ programming assignments based on semantics: multimedia e-learning based smart assessment methodology, Multimed. Tool. Appl., 79, 8581, 10.1007/s11042-018-5827-6 Ullah, 2021, Software plagiarism detection in multiprogramming languages using machine learning approach, Concurrency Comput. Pract. Ex., 33, 10.1002/cpe.5000 Ullah, 2019, Cyber security threats detection in internet of things using deep learning approach, IEEE Access, 7, 124379, 10.1109/ACCESS.2019.2937347 Moore, 2017