{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:52:44Z","timestamp":1772301164574,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T00:00:00Z","timestamp":1672876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>With the significant growth of the cyber environment over recent years, defensive mechanisms against adversaries have become an important step in maintaining online safety. The adaptive defense mechanism is an evolving approach that, when combined with nature-inspired algorithms, allows users to effectively run a series of artificial intelligence-driven tests on their customized networks to detect normal and under attack behavior of the nodes or machines attached to the network. This includes a detailed analysis of the difference in the throughput, end-to-end delay, and packet delivery ratio of the nodes before and after an attack. In this paper, we compare the behavior and fitness of the nodes when nodes under a simulated attack are altered, aiding several nature-inspired cyber security-based adaptive defense mechanism approaches and achieving clear experimental results. The simulation results show the effectiveness of the fitness of the nodes and their differences through a specially crafted metric value defined using the network performance statistics and the actual throughput difference of the attacked node before and after the attack.<\/jats:p>","DOI":"10.3390\/systems11010027","type":"journal-article","created":{"date-parts":[[2023,1,5]],"date-time":"2023-01-05T02:28:53Z","timestamp":1672885733000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Adaptive Artificial Bee Colony Algorithm for Nature-Inspired Cyber Defense"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6617-313X","authenticated-orcid":false,"given":"Chirag","family":"Ganguli","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology, VIT Bhopal University, Bhopal 466114, India"}]},{"given":"Shishir Kumar","family":"Shandilya","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology, VIT Bhopal University, Bhopal 466114, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9243-1534","authenticated-orcid":false,"given":"Maryna","family":"Nehrey","sequence":"additional","affiliation":[{"name":"Department, National University of Life and Environmental Sciences of Ukraine, 03041 Kyiv, Ukraine"}]},{"given":"Myroslav","family":"Havryliuk","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, Ukraine"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,5]]},"reference":[{"key":"ref_1","first-page":"2","article-title":"Machine Learning Techniques for Anomaly Detection An Overview","volume":"79","author":"Omar","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.patcog.2017.09.037","article-title":"A comparative evaluation of outlier detection algorithms: Experiments and analyses","volume":"74","author":"Domingues","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_3","unstructured":"Hodge, V., and Austin, J. (2018). An Evaluation of Classification and Outlier Detection Algorithms. arXiv."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1016\/j.asoc.2018.12.029","article-title":"Outlier Detection Based on Gaussian Process with Application to Industrial Processes","volume":"76","author":"Wang","year":"2019","journal-title":"Appl. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4477","DOI":"10.1007\/s12652-019-01417-9","article-title":"Performance evaluation of unsupervised techniques in cyber-attack anomaly detection","volume":"11","author":"Meira","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.jtbi.2013.07.010","article-title":"Local behavioral rules sustain the cell allocation pattern in the combs of honey bee colonies (Apis mellifera)","volume":"336","author":"Montovan","year":"2013","journal-title":"J. Theor. Biol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9798","DOI":"10.1038\/s41598-020-66115-5","article-title":"The prediction of swarming in honeybee colonies using vibrational spectra","volume":"10","author":"Ramsey","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.future.2021.09.018","article-title":"AI-assisted Computer Network Operations testbed for Nature-Inspired Cyber Security based adaptive defense simulation and analysis","volume":"127","author":"Shandilya","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"012056","DOI":"10.1088\/1755-1315\/634\/1\/012056","article-title":"Application of An Improved Artificial Bee Colony Algorithm","volume":"634","author":"Zhang","year":"2021","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_10","unstructured":"Atighetchi, M., Pal, P., Webber, F., and Jones, C. (2003, January 14\u201316). Adaptive Use of Network-Centric Mechanisms in Cyber-Defense. Proceedings of the Sixth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, Hokkaido, Japan."},{"key":"ref_11","unstructured":"Soliman, O.S., and Rassem, A. (2014). A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107901","DOI":"10.1016\/j.compeleceng.2022.107901","article-title":"Lightweight intrusion detection for edge computing networks using deep forest and bio-inspired algorithms","volume":"100","author":"Bangui","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2730","DOI":"10.1002\/sec.588","article-title":"Application of artificial bee colony for intrusion detection systems","volume":"8","author":"Aldwairi","year":"2012","journal-title":"Secur. Commun. Netw."},{"key":"ref_14","unstructured":"Celik, M., Kurban, R., and Kurban, T. (2022, December 19). Artificial Bee Colony Algorithm for Anomaly Based Intrusion Detection. Available online: https:\/\/www.researchgate.net\/profile\/Rifat-Kurban\/publication\/356854870_Artificial_Bee_Colony_Algorithm_for_Anomaly_Based_Intrusion_Detection\/links\/61b0a7371a5f480388c19525\/Artificial-Bee-Colony-Algorithm-for-Anomaly-Based-Intrusion-Detection.pdf."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Qureshi, A., Larijani, H., Mtetwa, N., Javed, A., and Ahmad, J. (2019). RNN-ABC: A New Swarm Optimization Based Technique for Anomaly Detection. Computers, 8.","DOI":"10.3390\/computers8030059"},{"key":"ref_16","first-page":"148","article-title":"Firefly algorithm based Feature Selection for Network Intrusion Detection","volume":"81","author":"Selvakumar","year":"2018","journal-title":"Comput. Secur."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Alzaqebah, A., Aljarah, I., Al-Kadi, O., and Damasevicius, R. (2022). A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System. Mathematics, 10.","DOI":"10.3390\/math10060999"},{"key":"ref_18","unstructured":"Tyugu, E. (June, January 31). Artificial intelligence in cyber defense. Proceedings of the 2011 3rd International Conference on Cyber Conflict, Tallinn, Estonia."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.cose.2013.11.004","article-title":"Framework and principles for active cyber defense","volume":"40","author":"Denning","year":"2014","journal-title":"Comput. Secur."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Das, S.K., Nita-Rotaru, C., and Kantarcioglu, M. (2013, January 11\u201312). Optimizing Active Cyber Defense. Proceedings of the Decision and Game Theory for Security, Fort Worth, TX, USA.","DOI":"10.1007\/978-3-319-02786-9"},{"key":"ref_21","unstructured":"Tirenin, W., and Faatz, D. (November, January 31). A concept for strategic cyber defense. Proceedings of the MILCOM 1999, IEEE Military Communications, Atlantic City, NJ, USA. Conference Proceedings (Cat. No.99CH36341)."},{"key":"ref_22","unstructured":"Bushnell, L., Poovendran, R., and Ba\u015far, T. (2018, January 29\u201331). Analysis and Computation of Adaptive Defense Strategies Against Advanced Persistent Threats for Cyber-Physical Systems. Proceedings of the Decision and Game Theory for Security, Seattle, WA, USA."},{"key":"ref_23","unstructured":"Tambe, M. (2016). Workshop on Adaptive Defense in the Cyber-Security Domain, University of Southern California. Technical Report."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1109\/COMST.2019.2963791","article-title":"Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense","volume":"22","author":"Cho","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dasgupta, D. (2006, January 16\u201317). Computational intelligence in cyber security. Proceedings of the 2006 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, Alexandria, VA, USA.","DOI":"10.1109\/CIHSPS.2006.313289"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/1\/27\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T17:59:23Z","timestamp":1760119163000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/11\/1\/27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,5]]},"references-count":25,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["systems11010027"],"URL":"https:\/\/doi.org\/10.3390\/systems11010027","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,5]]}}}