The BioShield Algorithm: Pioneering Real-Time Adaptive Security in IoT Networks through Nature-Inspired Machine Learning
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 9 |
| Year of Publication : 2024 |
| Authors : Ch. Rammohan, P. Laxmikanth, Doddi Srikar, M. Ayyappa Chakravarthi, Terrance Frederick Fernandez, P. Hussain Basha |
10.14445/23488379/IJEEE-V11I9P115
How to Cite?
Ch. Rammohan, P. Laxmikanth, Doddi Srikar, M. Ayyappa Chakravarthi, Terrance Frederick Fernandez, P. Hussain Basha, "The BioShield Algorithm: Pioneering Real-Time Adaptive Security in IoT Networks through Nature-Inspired Machine Learning," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 9, pp. 172-185, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I9P115
Abstract:
This paper introduces the BioShield Algorithm, aimed at the crucial task of securing IoT networks through real-time adaptive mechanisms that draw inspiration from nature. It delves into the critical issues plaguing IoT security, such as the dynamic and heterogeneous nature of both threats and network architectures. It proposes a nature-inspired machine learning model designed for adaptive, real-time threat detection and mitigation. By employing the "UNSW-NB15" dataset, the algorithm undergoes a rigorous evaluation across various metrics, including detection accuracy, response time, and scalability. The quantitative analysis reveals the algorithm's high proficiency in dealing with diverse cyber-attack scenarios, with precision scores ranging from 95.9% for Malware to 98.4% for Tampering attacks. Recall rates also show impressive figures, peaking at 96% for DDoS attacks, alongside consistently high F1 scores that underscore the model's balanced precision and recall capabilities. Additionally, accuracy rates across different attack types further confirm the algorithm's effectiveness, with scores oscillating between 94.95% and 97.2%. These results strongly endorse the BioShield Algorithm's capacity to accurately detect and classify cyber threats within IoT environments, spotlighting its applicability in significantly enhancing the security framework of IoT networks. This algorithm stands out for its adaptive, efficient, and scalable nature, positioning it as a pivotal contribution to the field of IoT security.
Keywords:
IoT security, BioShield algorithm, Machine Learning, Real-time adaptive mechanisms, UNSW-NB15 dataset, Cyber threat detection.
References:
[1] M. Sri Lakshmi et al., “Evaluating the Isolation Forest Method for Anomaly Detection in Software-Defined Networking Security,” Journal of Electrical Systems, vol. 19, no. 4, pp. 279-297, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Elhadj Benkhelifa, Lokhande Gaurav, and Vidya Sagar S.D., “BioShieldNet: Advanced Biologically Inspired Mechanisms for Strengthening Cybersecurity in Distributed Computing Environments,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 3, pp. 1-9, 2024.
[CrossRef] [Publisher Link]
[3] Usman Tariq, “A Critical Cybersecurity Analysis and Future Research Directions for the Internet of Things: A Comprehensive Review,” Sensors, vol. 23, no. 8, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Tariq Ahamed Ahanger, Abdullah Aljumah, and Mohammed Atiquzzaman, “State-of-the-Art Survey of Artificial Intelligent Techniques for IoT Security,” Computer Networks, vol. 206, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Jayashree Mohanty et al., “IoT Security, Challenges, and Solutions: A Review,” Progress in Advanced Computing and Intelligent Engineering, vol. 2, pp. 493-504, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Tabssum Khan, Arkan Ahmed Hussein, and Ahmad M. Hussein Shabani, “StreamDrift: A Unified Model for Detecting Gradual and Sudden Changes in Data Streams,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 5, pp. 58-65, 2024.
[CrossRef] [Publisher Link]
[7] Paolo Dini, Mykola Makhortykh, and Maryna Sydorova, “DataStreamAdapt: Unified Detection Framework for Gradual and Abrupt Concept Drifts,” Synthesis: A Multidisciplinary Research Journal, vol. 1, no. 4, pp. 1-9, 2023.
[Google Scholar] [Publisher Link]
[8] M. Jahir Pasha et al., “LRDADF: An AI Enabled Framework for Detecting Low-Rate DDoS Attacks in Cloud Computing Environments,” Measurement: Sensors, vol. 28, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Leela Mahesh Reddy, and K. Madhavi, “Blockchain Split-Join Architecture: A Novel Framework for Improved Transaction Processing,” Frontiers in Collaborative Research, vol. 1, no. 3, pp. 20-29, 2023.
[Google Scholar] [Publisher Link]
[10] Mukerjee Jaydeep, Vamsi Uppari, and Maloth Bhavsingh, “GeoFusionAI: Advancing Terrain Analysis with Hybrid AI and Multi-Dimensional Data Synthesis,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 2, pp. 50-60, 2024.
[CrossRef] [Publisher Link]
[11] Tian Wang et al., “Preserving Balance between Privacy and Data Integrity in Edge-Assisted Internet of Things,” IEEE Internet of Things Journal, vol. 7, no. 4, pp. 2679-2689, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Caciano Machado, and Antonio Augusto Medeiros Frohlich, “IoT Data Integrity Verification for Cyber-Physical Systems Using Blockchain,” 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), Singapore, pp. 83-90, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Tomasz Bosakowsk, David Hutchison, and P. Radhika Raju, “CyberEcoGuard: Evolutionary Algorithms and Nature-Mimetic Defenses for Enhancing Network Resilience in Cloud Infrastructures,” International Journal of Computer Engineering in Research Trends, vol. 11, no. 2, pp. 89-99, 2024.
[CrossRef] [Publisher Link]
[14] V.S.K. Reddy et al., “MDC-Net: Intelligent Malware Detection and Classification Using Extreme Learning Machine,” 2023 Third International Conference on Artificial Intelligence and Smart Energy (ICAIS), Coimbatore, India, pp. 1590-1594, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[15] M. Jahir Pasha et al., “Bug2 Algorithm-Based Data Fusion Using Mobile Element for IoT-Enabled Wireless Sensor Networks,” Measurement: Sensors, vol. 24, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] M. Sri Lakshmi et al., “Minimizing the Localization Error in Wireless Sensor Networks Using Multi-Objective Optimization Techniques,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 2s, pp. 306-312, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Sobhy Abdelkader et al., “Securing Modern Power Systems: Implementing Comprehensive Strategies to Enhance Resilience and Reliability against Cyber-Attacks,” Results in Engineering, vol. 23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ryan Paul Badman, Thomas Trenholm Hills, and Rei Akaishi, “Multiscale Computation and Dynamic Attention in Biological and Artificial Intelligence,” Brain Sciences, vol. 10, no. 6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Laith Abualigah, Deborah Falcone, and Agostino Forestiero, “Swarm Intelligence to Face IoT Challenges,” Computational Intelligence and Neuroscience, vol. 2023, no. 1, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Muhammad Saleem, Gianni A. Di Caro, and Muddassar Farooq, “Swarm Intelligence Based Routing Protocol for Wireless Sensor Networks: Survey and Future Directions,” Information Sciences, vol. 181, no. 20, pp. 4597-4624, 2011.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Ahmed G. Gad, “Particle Swarm Optimization Algorithm and Its Applications: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, no. 5, pp. 2531-2561, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Weifeng Sun et al., “A Survey of Using Swarm Intelligence Algorithms in IoT,” Sensors, vol. 20, no. 5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Liang Xiao et al., “IoT Security Techniques Based on Machine Learning: How do IoT Devices Use AI to Enhance Security?,” IEEE Signal Processing Magazine, vol. 35, no. 5, pp. 41-49, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Astha Srivastava et al., “Future IoT‐Enabled Threats and Vulnerabilities: State of the Art, Challenges, and Future Prospects,” International Journal of Communication Systems, vol. 33, no. 12, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Parushi Malhotra et al., “Internet of Things: Evolution, Concerns and Security Challenges,” Sensors, vol. 21, no. 5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Leeladhar Gudala et al., “Leveraging Artificial Intelligence for Enhanced Threat Detection, Response, and Anomaly Identification in Resource-Constrained IoT Networks,” Distributed Learning and Broad Applications in Scientific Research, vol. 5, pp. 23-54, 2019.
[Google Scholar] [Publisher Link]
[27] Charilaos Akasiadis et al., “Developing Complex Services in an IoT Ecosystem,” 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, Italy, pp. 52-56, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Sarika Choudhary, and Nishtha Kesswani, “Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 Datasets Using Deep Learning in IoT,” Procedia Computer Science, vol. 167, pp. 1561-1573, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Abeera Malik et al., “SecureNet: A Convergence of ML, Blockchain and Federated Learning for IoT Protection”, UCP Journal of Engineering & Information Technology, vol. 2, no. 1, pp. 24–35, Sep. 2024.
[Google Scholar] [Publisher Link]