Utilizing Deep Learning to Identify Potentially Dangerous Routing Attacks in the IoT
DOI:
https://doi.org/10.18034/abcjar.v11i2.699Keywords:
Deep Learning, Artificial Intelligence, IoT Applications, Neural NetworksAbstract
Due to the rapid increase of cyber-security difficulties brought about by sophisticated assaults such as data injection attacks, replay attacks, etc., the design of cyber-attack detection and control systems has emerged as an essential subfield within cyber-physical systems (CPSs) during the past few years. The outcome of these attacks could be a system failure, malfunctioning, or other undesirable effects. Consequently, it may be necessary to implement the cyber defense system in preparation for impending CPSs to have an improved security system. The various cyber-attack detection schemes based on deep learning algorithms have been intended to detect and mitigate the cyber-attacks that can be launched against CPSs, smart grids, power systems, and other similar infrastructure. This article comprehensively reviews several different deep learning algorithms suggested for use in CPSs to accomplish cyber defense. In the beginning, several methods devised by earlier academics are analyzed in great detail. After that, a comparison study is performed to determine the shortcomings of each algorithm and offer a recommendation for how further improvements to CPSs might be made more effectively.
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Amin, R., & Mandapuram, M. (2021). CMS - Intelligent Machine Translation with Adaptation and AI. ABC Journal of Advanced Research, 10(2), 199-206. https://doi.org/10.18034/abcjar.v10i2.693 DOI: https://doi.org/10.18034/abcjar.v10i2.693
Ángel, A., Marco, A., Blasco, R., Casas, R. (2014). Protocol and Architecture to Bring Things into the Internet of Things. International Journal of Distributed Sensor Networks. https://doi.org/10.1155/2014/158252 DOI: https://doi.org/10.1155/2014/158252
Assem, H., Xu, L., Buda, T.S. (2016). Machine learning as a service for enabling the Internet of Things and People. Personal and Ubiquitous Computing, 20(6), 899-914. https://doi.org/10.1007/s00779-016-0963-3 DOI: https://doi.org/10.1007/s00779-016-0963-3
Ballamudi, V. K. R., Lal, K., Desamsetti, H., & Dekkati, S. (2021). Getting Started Modern Web Development with Next.js: An Indispensable React Framework. Digitalization & Sustainability Review, 1(1), 1–11. https://upright.pub/index.php/dsr/article/view/83
Bodepudi, A., Reddy, M., Gutlapalli, S. S., & Mandapuram, M. (2019). Voice Recognition Systems in the Cloud Networks: Has It Reached Its Full Potential?. Asian Journal of Applied Science and Engineering, 8(1), 51–60. https://doi.org/10.18034/ajase.v8i1.12 DOI: https://doi.org/10.18034/ajase.v8i1.12
Bodepudi, A., Reddy, M., Gutlapalli, S. S., & Mandapuram, M. (2021). Algorithm Policy for the Authentication of Indirect Fingerprints Used in Cloud Computing. American Journal of Trade and Policy, 8(3), 231–238. https://doi.org/10.18034/ajtp.v8i3.651 DOI: https://doi.org/10.18034/ajtp.v8i3.651
Chen, S., Thaduri, U. R., & Ballamudi, V. K. R. (2019). Front-End Development in React: An Overview. Engineering International, 7(2), 117–126. https://doi.org/10.18034/ei.v7i2.662 DOI: https://doi.org/10.18034/ei.v7i2.662
DANIEL, O. (2016). THE INTERNET OF THINGS. JOURNAL OF DEMOCRACY, 27(3), 176-178. https://doi.org/10.1353/jod.2016.0042 DOI: https://doi.org/10.1353/jod.2016.0042
Dekkati, S., & Thaduri, U. R. (2017). Innovative Method for the Prediction of Software Defects Based on Class Imbalance Datasets. Technology & Management Review, 2, 1–5. https://upright.pub/index.php/tmr/article/view/78
Dekkati, S., Gutlapalli, S. S., Thaduri, U. R., & Ballamudi, V. K. R. (2022). AI and Machine Learning for Remote Suspicious Action Detection and Recognition. ABC Journal of Advanced Research, 11(2), 97-102. https://doi.org/10.18034/abcjar.v11i2.694 DOI: https://doi.org/10.18034/abcjar.v11i2.694
Dekkati, S., Lal, K., & Desamsetti, H. (2019). React Native for Android: Cross-Platform Mobile Application Development. Global Disclosure of Economics and Business, 8(2), 153-164. https://doi.org/10.18034/gdeb.v8i2.696 DOI: https://doi.org/10.18034/gdeb.v8i2.696
Deming, C., Dekkati, S., & Desamsetti, H. (2018). Exploratory Data Analysis and Visualization for Business Analytics. Asian Journal of Applied Science and Engineering, 7(1), 93–100. https://doi.org/10.18034/ajase.v7i1.53 DOI: https://doi.org/10.18034/ajase.v7i1.53
Desamsetti, H. (2016a). A Fused Homomorphic Encryption Technique to Increase Secure Data Storage in Cloud Based Systems. The International Journal of Science & Technoledge, 4(10), 151-155.
Desamsetti, H. (2016b). Issues with the Cloud Computing Technology. International Research Journal of Engineering and Technology (IRJET), 3(5), 321-323.
Desamsetti, H., & Mandapuram, M. (2017). A Review of Meta-Model Designed for the Model-Based Testing Technique. Engineering International, 5(2), 107–110. https://doi.org/10.18034/ei.v5i2.661 DOI: https://doi.org/10.18034/ei.v5i2.661
Gutlapalli, S. S. (2016a). An Examination of Nanotechnology’s Role as an Integral Part of Electronics. ABC Research Alert, 4(3), 21–27. https://doi.org/10.18034/ra.v4i3.651 DOI: https://doi.org/10.18034/ra.v4i3.651
Gutlapalli, S. S. (2016b). Commercial Applications of Blockchain and Distributed Ledger Technology. Engineering International, 4(2), 89–94. https://doi.org/10.18034/ei.v4i2.653 DOI: https://doi.org/10.18034/ei.v4i2.653
Gutlapalli, S. S. (2017a). Analysis of Multimodal Data Using Deep Learning and Machine Learning. Asian Journal of Humanity, Art and Literature, 4(2), 171–176. https://doi.org/10.18034/ajhal.v4i2.658 DOI: https://doi.org/10.18034/ajhal.v4i2.658
Gutlapalli, S. S. (2017b). The Role of Deep Learning in the Fourth Industrial Revolution: A Digital Transformation Approach. Asian Accounting and Auditing Advancement, 8(1), 52–56. Retrieved from https://4ajournal.com/article/view/77
Gutlapalli, S. S. (2017c). An Early Cautionary Scan of the Security Risks of the Internet of Things. Asian Journal of Applied Science and Engineering, 6, 163–168. Retrieved from https://ajase.net/article/view/14
Gutlapalli, S. S., Mandapuram, M., Reddy, M., & Bodepudi, A. (2019). Evaluation of Hospital Information Systems (HIS) in terms of their Suitability for Tasks. Malaysian Journal of Medical and Biological Research, 6(2), 143–150. https://mjmbr.my/index.php/mjmbr/article/view/661 DOI: https://doi.org/10.18034/mjmbr.v6i2.661
Khadija, F., Hassan, S., Ayesha, T., Aisha, D., Zohaib, A. (2017). A Systematic Literature Review on the Security Challenges of Internet of Things and their Classification. International Journal of Technology and Research, 5(2), 40-48.
Koehler, S., Desamsetti, H., Ballamudi, V. K. R., & Dekkati, S. (2020). Real World Applications of Cloud Computing: Architecture, Reasons for Using, and Challenges. Asia Pacific Journal of Energy and Environment, 7(2), 93-102. https://doi.org/10.18034/apjee.v7i2.698 DOI: https://doi.org/10.18034/apjee.v7i2.698
Lal, K. (2015). How Does Cloud Infrastructure Work?. Asia Pacific Journal of Energy and Environment, 2(2), 61-64. https://doi.org/10.18034/apjee.v2i2.697 DOI: https://doi.org/10.18034/apjee.v2i2.697
Lal, K. (2016). Impact of Multi-Cloud Infrastructure on Business Organizations to Use Cloud Platforms to Fulfill Their Cloud Needs. American Journal of Trade and Policy, 3(3), 121–126. https://doi.org/10.18034/ajtp.v3i3.663 DOI: https://doi.org/10.18034/ajtp.v3i3.663
Lal, K., & Ballamudi, V. K. R. (2017). Unlock Data’s Full Potential with Segment: A Cloud Data Integration Approach. Technology &Amp; Management Review, 2, 6–12. https://upright.pub/index.php/tmr/article/view/80
Lal, K., Ballamudi, V. K. R., & Thaduri, U. R. (2018). Exploiting the Potential of Artificial Intelligence in Decision Support Systems. ABC Journal of Advanced Research, 7(2), 131-138. https://doi.org/10.18034/abcjar.v7i2.695 DOI: https://doi.org/10.18034/abcjar.v7i2.695
Liu, X., Zhao, M., Li, S., Zhang, F., Wade, T. (2017). A Security Framework for the Internet of Things in the Future Internet Architecture. Future Internet, 9(3), 27. https://doi.org/10.3390/fi9030027 DOI: https://doi.org/10.3390/fi9030027
Mandapuram, M. (2016). Applications of Blockchain and Distributed Ledger Technology (DLT) in Commercial Settings. Asian Accounting and Auditing Advancement, 7(1), 50–57. Retrieved from https://4ajournal.com/article/view/76
Mandapuram, M. (2017a). Application of Artificial Intelligence in Contemporary Business: An Analysis for Content Management System Optimization. Asian Business Review, 7(3), 117–122. https://doi.org/10.18034/abr.v7i3.650 DOI: https://doi.org/10.18034/abr.v7i3.650
Mandapuram, M. (2017b). Security Risk Analysis of the Internet of Things: An Early Cautionary Scan. ABC Research Alert, 5(3), 49–55. https://doi.org/10.18034/ra.v5i3.650 DOI: https://doi.org/10.18034/ra.v5i3.650
Mandapuram, M., & Hosen, M. F. (2018). The Object-Oriented Database Management System versus the Relational Database Management System: A Comparison. Global Disclosure of Economics and Business, 7(2), 89–96. https://doi.org/10.18034/gdeb.v7i2.657 DOI: https://doi.org/10.18034/gdeb.v7i2.657
Mandapuram, M., Gutlapalli, S. S., Bodepudi, A., & Reddy, M. (2018). Investigating the Prospects of Generative Artificial Intelligence. Asian Journal of Humanity, Art and Literature, 5(2), 167–174. https://doi.org/10.18034/ajhal.v5i2.659 DOI: https://doi.org/10.18034/ajhal.v5i2.659
Mandapuram, M., Gutlapalli, S. S., Reddy, M., Bodepudi, A. (2020). Application of Artificial Intelligence (AI) Technologies to Accelerate Market Segmentation. Global Disclosure of Economics and Business 9(2), 141–150. https://doi.org/10.18034/gdeb.v9i2.662 DOI: https://doi.org/10.18034/gdeb.v9i2.662
Reddy, M., Bodepudi, A., Mandapuram, M., & Gutlapalli, S. S. (2020). Face Detection and Recognition Techniques through the Cloud Network: An Exploratory Study. ABC Journal of Advanced Research, 9(2), 103–114. https://doi.org/10.18034/abcjar.v9i2.660 DOI: https://doi.org/10.18034/abcjar.v9i2.660
Thaduri, U. R., & Lal, K. (2020). Making a Dynamic Website: A Simple JavaScript Guide. Technology & Management Review, 5, 15–27. https://upright.pub/index.php/tmr/article/view/81
Thaduri, U. R., Ballamudi, V. K. R., Dekkati, S., & Mandapuram, M. (2016). Making the Cloud Adoption Decisions: Gaining Advantages from Taking an Integrated Approach. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 11–16. https://upright.pub/index.php/ijrstp/article/view/77
Thodupunori, S. R., & Gutlapalli, S. S. (2018). Overview of LeOra Software: A Statistical Tool for Decision Makers. Technology & Management Review, 3(1), 7–11.
Tien, J. M. (2017). Internet of Things, Real-Time Decision Making, and Artificial Intelligence. Annals of Data Science, 4(2), 149-178. https://doi.org/10.1007/s40745-017-0112-5 DOI: https://doi.org/10.1007/s40745-017-0112-5
Valliammal, N., Shaju, B. (2018). Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey. International Journal of Advanced Technology and Engineering Exploration, 5(49), 489-494. https://doi.org/10.19101/IJATEE.2018.547030
Valliammal, N., & Shaju, B. (2018). Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey. International Journal of Advanced Technology and Engineering Exploration, 5(49), 489-494. https://doi.org/10.19101/IJATEE.2018.547030 DOI: https://doi.org/10.19101/IJATEE.2018.547030
Wortmann, F., & Flüchter, K. (2015). Internet of Things: Technology and Value Added. Business & Information Systems Engineering, 57(3), 221-224. https://doi.org/10.1007/s12599-015-0383-3 DOI: https://doi.org/10.1007/s12599-015-0383-3
Zhang, J., Zhang K., Qin, Z., Yin, H., Wu, Q. (2018). Sensitive system calls-based packed malware variants detection using principal component initialized MultiLayers neural networks. Cybersecurity, 1(10). https://doi.org/10.1186/s42400-018-0010-y DOI: https://doi.org/10.1186/s42400-018-0010-y
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