Secure IoT: Deep Learning-Based Intrusion Detection for Attack Detection and Prevent

1. Buber, E, Diri, B & Sahingoz, OK 2017, ‘NLP based phishing attack detection from URLs’, International Conference on Intelligent
Systems Design and Applications, vol. 736, pp. 608-618.
2. Elsaeidy, A, Munasinghe, KS, Sharma, D & Jamalipour, A 2019, ‘Intrusion detection in smart cities using Restricted
BoltzmannMachines’, Journal of Network and Computer Applications, vol. 135,pp. 76-83.
3. Hepsiba, CL & Sathiaseelan, J 2016, ‘Security issues in service modelsof cloud computing’, International Journal of Computer Science
andMobile Computing, vol. 5, no. 3, pp. 610-615.
4. Li, D, Deng, L, Lee, M & Wang, H 2019, ‘IoT data feature extractionand intrusion detection system for smart cities based on deep
migrationlearning’, International Journal of Information Management, vol. 49,pp. 533-545.
5. Pacheco, J & Hariri, S 2016, ‘IoT security framework for smart cyberinfrastructures’, IEEE 1st International Workshops on Foundations
andApplications of Self* Systems (FAS* W), Universidad De Sonara,pp. 242-247.
6. Stevanovic, M & Pedersen, JM 2014, ‘An efficient flow-based botnetdetection using supervised machine learning’, 2014
internationalconference on computing, networking and communications (ICNC), 3-6 Feb. 2014, Honolulu, Hawaii, USA, pp. 797-801.