The rapid growth of the Internet of Things (IoT) brings new options to innovative healthcare, transportation, and industrial systems. However, this expansion also increases cyber threats to these infrastructures. Standard anomaly detection systems use fixed machine learning models. Such models require frequent retraining and are not very sensitive to concept drift which results in many false positives when used in adaptive IoT systems. In order to overcome these issues, this paper will present a bio-inspired, adaptive anomaly detector. It also presents a framework for selecting dynamic detectors via Artificial Immune Systems (AISs). The system architecture combines several immune-inspired concepts. Adverse selection separates normal from abnormal patterns, danger theory classifies anomalies in context, and clonal selection and mutation help detectors evolve. Immune memory supports long-term learning and quick response. The proposed model was tested on three benchmark IoT security datasets: UNSW-NB15, BoT-IoT, and TON_IoT. This allowed assessment against legacy and new attack scenarios. In the experiment, the approach achieved 97.5% accuracy, 96.9% precision, 97.8% recall, and a 97.3 F1-score. Compared to related 2023-2025 works, it performs 2.4-8.4% better across various measures. Detection latency decreased due to immune memory integration, and adaptation to zero-day attacks improved. These results confirm that AIS-based anomaly detection is a scalable and adaptive tool for securing future IoT environments.
Myakala PK, Bura C, Jonnalagadda AK. Artificial Immune Systems: A Bio-Inspired Paradigm for Computational Intelligence. Journal of Artificial Intelligence and Big Data. 2025;5(1):1–13.
2.
Selvaraj R, kuthadi VM, Baskar DrS, Acevedo R. Tiny ML-Enabled Energy-Efficient Intrusion Detection System for Sustainable IoT Security in Green Cybersecurity Ecosystems. Journal of Internet Services and Information Security. 2025;15(3):602–25.
3.
Dharmireddi S, Hameed A, Albdairi M, Samudro EG, Nandy M. Cybersecurity in Digital Finance: Artificial Intelligence-Powered Fraud Detection and Risk Management. 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE; 2025. p. 1–5.
4.
Purnama Y, Asdlori A, Ciptaningsih EMSS, Kraugusteeliana K, Triayudi A, Rahim R. Machine Learning for Cybersecurity: A Bibliometric Analysis from 2019 to 2023. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2024;15(4):243–58.
5.
Jalal S, Yousif R, Al-Mukhtar F, Sw K. An optimized up to 16-user and 160 Gbps dual cascaded optical modulators PON-based power combined array fiber Bragg grating and pre-distortion device for 5th G system. Photonic Network Communications. 2025;(1):1.
6.
Mehra A, Iyer R. Improving cybersecurity using artificial intelligence: overview, modeling, future directions. International Academic Journal of Science and Engineering. 2021;(2):11–5.
7.
Amin Ali OM, Hamaamin RA, Kareem SW. Deep Learning Techniques for Early Fault Detection in Bearings: An Intelligent Approach. Kurdistan Journal of Applied Research. 2025;10(1):18–34.
8.
Ali O, Hamaamin R, Youns B, Kareem S. Innovative Machine Learning Strategies for DDoS Detection: A Review. UHD Journal of Science and Technology. 2024;(2):38–49.
9.
Aalsaud A, Kareem SW, Zuhair Yousif R, Salahuddin Mohammed A. Ensemble Transfer Learning for Botnet Detection in the Internet of Things. Scalable Computing: Practice and Experience. 2024;25(5):4312–22.
10.
Pinto C, Pinto R, Gonçalves G. Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm. Algorithms. 2021;15(1):1.
11.
Soni V, Bhatt DP, Yadav NS. Bio Inspired Methods for Intrusion Detection in Internet of Things: A Survey. 2024 IEEE Region 10 Symposium (TENSYMP). IEEE; 2024. p. 1–8.
12.
Wlodarczak P. Cyber Immunity: A bio-inspired cyber defense system. InInternational Conference on Bioinformatics and Biomedical Engineering. 2017;199–208.
13.
Saadouni R, Gherbi C, Aliouat Z, Harbi Y, Khacha A. Intrusion detection systems for IoT based on bio-inspired and machine learning techniques: a systematic review of the literature. Cluster Computing. 2024;27(7):8655–81.
14.
Balasubramaniam S, Kadry S, Mk T, Kumar K. Bio-inspired Algorithms in Machine Learning and Deep Learning for Disease Detection. 2025;
15.
Tandiya N, Colbert E, Marojevic V, Reed J. Biologically inspired artificial intelligence techniques. InCyber Resilience of Systems and Networks. 2018;287–313.
16.
Soni V, Saxena S, Bhatt D, Yadav N. ImmuneGAN: Bio-inspired Artificial Immune System Secure IoT Ecosystem. 2021;110–21.
17.
Ashwini A, Balasubramaniam S, Sundaravadivazhagan B. Bio-Inspired Intelligence in Early Cancer Detection A Machine Learning Approach. InBio-inspired Algorithms in Machine Learning and Deep Learning for Disease Detection. :122–40.
18.
Sharma P, Chaudhary K. Adaptive Cybersecurity for IoT Networks Using Artificial Immune Systems: A Scalable Approach for Real-Time Threat Detection. 2024;733–46.
19.
Pham TH, Raahemi B. Bio-Inspired Feature Selection Algorithms With Their Applications: A Systematic Literature Review. IEEE Access. 2023;11:43733–58.
20.
Alabdulatif A, Thilakarathne NN. Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions. Biomimetics. 2023;8(4):373.
21.
Efiong J, Ajayi T, Akinwale A, Olajubu E. Towards a Bio-inspired Real-Time. 2024;289.
22.
Alabdulatif A, Thilakarathne NN. Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions. Biomimetics. 2023;8(4):373.
23.
Usha G, Madhavan P, Kumar MVR. A Novel Design Augmentation of Bio-Inspired Artificial Immune Technique in Securing Internet of Things (IOT). EAI/Springer Innovations in Communication and Computing. Springer International Publishing; 2019. p. 103–14.
24.
Bouramoul IE, Zertal S, Derdour M, Zenbout I. Enhancing IoT Security Through Deep learning and Evolutionary Bio-Inspired Intrusion Detection in IoT systems. 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE; 2024. p. 1–8.
25.
Mthunzi S, Benkhelifa E, Bosakowski T, Hariri S. A bio-inspired approach to cyber security. InMachine Learning for Computer and Cyber Security. 2019;75–104.
26.
Bala A, Bahnasse A, El Bhiri B, Zegrari M, Tardif PM. Immunity-Inspired Approaches to Cybersecurity: A Review. Procedia Computer Science. 2025;257:274–81.
27.
Pourmoafi S. A Solution for Securing the Information Environment Inspired by Living Organisms and Biology.
28.
Rehman A, Alharbi O. Bioinspired Blockchain Framework for Secure and Scalable Wireless Sensor Network Integration in Fog–Cloud Ecosystems. Computers. 2024;14(1):3.
29.
Ahsan MM, Gupta KD, Nag AK, Poudyal S, Kouzani AZ, Mahmud MAP. Applications and Evaluations of Bio-Inspired Approaches in Cloud Security: A Review. IEEE Access. 2020;8:180799–814.
30.
Sherin R C J, K P. Investigations on Bio-Inspired Algorithm for Network Intrusion Detection – A Review. International Journal of Computer Networks and Applications. 2022;9(4):399.
31.
Md Fatin, Marufur Rahman. Artificial Intelligence in Healthcare Systems: From Clinical Imaging to Epidemic Forecasting. Asia Pacific Journal of Surgical Advances. 2025;2(3):139–51.
32.
Soni V, Bhatt DP, Yadav NS, Saxena S. DAIS: deep artificial immune system for intrusion detection in IoT ecosystems. International Journal of Bio-Inspired Computation. 2024;23(3):148–56.
33.
Procopiou A, Chen T. Malicious activity detection in IoT networks: A nature-inspired approach. InAdvances in Nature-Inspired Cyber Security and Resilience. 2021;55–83.
34.
Krishna GB, Udayasri G, T RD, Gnaneshwar K, Rani DS, Ala R. Biologically-Inspired Artificial Lymphocyte Networks for Adaptive and Scalable Malware Detection Against Zero-Day and Persistent Threats. 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE; 2025. p. 1–7.
35.
Rakhmanovich IU, Ryad Hossein R, Albdairi M, Omonov Q, Kumaraswamy B. Predictive Analytics and Automation: Transforming Logistics with Artificial Intelligence with Blockchain Intelligence. 2025 International Conference on Computational Innovations and Engineering Sustainability (ICCIES). IEEE; 2025. p. 1–6.
36.
Qadri S, Albdairi M, Almusawi A, Kabarcik A, Abdulrahman H. Optimization of Signalized Intersections: Analyzing Autonomous Vehicle Behaviors Through Data-Driven Simulations. InInternational Conference on Optimization and Data Science in Industrial Engineering. 2024;232–44.
37.
Dynamic Parameter Optimization for Industrial Internet Security Models Using Neural Networks. International Journal of Intelligent Engineering and Systems. 2025;18(6):933–46.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
