Illicit Events Evaluation Using NSGA-2 Algorithms Based on Energy Consumption
Abstract
The proposed work objective is to adopt the non-dominated sorting genetic algorithm II (NSGA-II), a type of MOEA (multi-objective evolutionary algorithms), to reduce the dimensionality and identify the most relevant features. The objectives and hypotheses tested using NSGA-II and MOEA are to develop predictive models that best fit the problem of traffic analysis, analyse the set of features associated with the dataset, and apply NSGA techniques to reduce the data dimensionality.
The work used the datasets and considered the Transnational Terrorist Hostage Event (TTHE) and Counter Trafficking Data Collaborative (CTDC) datasets, which relate to global human trafficking (HTr).
The method used is focused on analysing the energy consumption (Econ) with the carbon footprint (CFP) of the proposed algorithm for different values of the main parameters on different hardware platforms, where the quality of each individual is computed based on ML algorithms.
The result represents the "F-measure” metric obtained from support vector machines (SVM) and the number of features in order to determine the dimensionality, the execution time, the Econ, and the CFP during the feature engineering process. To estimate performance, the parameter values are computed using SVM.
The proposed work concludes that cybercrime employs and represents susceptible behaviour on the OSN Channels network. We identified the threat patterns by using NSGA-II and Econ analysis to establish connections between the variables in the dataset.
Full Text:
PDFReferences
. Aksu, D., & Aydin, M. A. (2022). MGA-IDS: Optimal feature subset selection for anomaly detection framework on in-vehicle networks-CAN bus based on genetic algorithm and intrusion detection approach. Computers & Security, 118, 102717.
. Enoch, S. Y., Mendonça, J., Hong, J. B., Ge, M., & Kim, D. S. (2022). An integrated security hardening optimization for dynamic networks using security and availability modeling with multi-objective algorithm. Computer Networks, 208, 108864.
. Kim, W., George, J., & Sandler, T. (2021). Introducing transnational terrorist hostage event (TTHE) data set, 1978 to 2018. Journal of Conflict Resolution, 65(2-3), 619-641
. Guermouche, A., & Orgerie, A. C. (2022). Thermal design power and vectorized instructions behavior. Concurrency and Computation: Practice and Experience, 34(2), e6261.
. Budennyy, S. A., Lazarev, V. D., Zakharenko, N. N., Korovin, A. N., Plosskaya, O. A., Dimitrov, D. V. E., ... & Zhukov, L. E. E. (2022, December). Eco2ai: carbon emissions tracking of machine learning models as the first step towards sustainable ai. In Doklady Mathematics (Vol. 106, No. Suppl 1, pp. S118-S128). Moscow: Pleiades Publishing.
. Bayerl, P. S., Akhgar, B., Brewster, B., Domdouzis, K., & Gibson, H. (2014). Social media and its role for leas: review and applications. Cyber Crime and Cyber Terrorism Investigator's Handbook, 197-220.
. Das, S. P. (2022). Hostage-Taking, Ransom, and Negotiations. In Economics of Terrorism and Counter-Terrorism Measures: History, Theory, and Evidence (pp. 481-503). Cham: Springer International Publishing.
. International Energy Agency. (2022). World energy outlook 2022. International Energy Agency. https://www.iea.org/reports/world-energy-outlook-2022
. Karamchandani, A., Mozo, A., Gómez-Canaval, S., & Pastor, A. (2024). A methodological framework for optimizing the energy consumption of deep neural networks: a case study of a cyber threat detector. Neural Computing and Applications, 1-42.
. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.
. Oh, O., Agrawal, M., & Rao, H. R. (2011). Information control and terrorism: Tracking the Mumbai terrorist attack through twitter. Information Systems Frontiers, 13, 33-43.
. Xia, T., Qu, G., Hariri, S., & Yousif, M. (2005, April). An efficient network intrusion detection method based on information theory and genetic algorithm. In PCCC 2005. 24th IEEE International Performance, Computing, and Communications Conference, 2005. (pp. 11-17). IEEE.
. Chohra, A., Shirani, P., Karbab, E. B., & Debbabi, M. (2022). Chameleon: Optimized feature selection using particle swarm optimization and ensemble methods for network anomaly detection. Computers & Security, 117, 102684.
. Tamimi, A., Naidu, D. S., & Kavianpour, S. (2015, October). An Intrusion detection system based on NSGA-II Algorithm. In 2015 Fourth International Conference on Cyber Security, Cyber Warfare, and Digital Forensic (CyberSec) (pp. 58-61). IEEE.
. Panigrahi, R., Borah, S., Pramanik, M., Bhoi, A. K., Barsocchi, P., Nayak, S. R., & Alnumay, W. (2022). Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multi-objective evolutionary feature selection. Computer Communications, 188, 133-144.
. Bu, S. J., Kang, H. B., & Cho, S. B. (2022). Ensemble of deep convolutional learning classifier system based on genetic algorithm for database intrusion detection. Electronics, 11(5), 745.
. Rawat, R., Mahor, V., Chirgaiya, S., Shaw, R. N., & Ghosh, A. (2021). Analysis of darknet traffic for criminal activities detection using TF-IDF and light gradient boosted machine learning algorithm. In Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2021 (pp. 671-681). Springer Singapore.
. Dimolianis, M., Pavlidis, A., & Maglaris, V. (2021). Signature-based traffic classification and mitigation for DDoS attacks using programmable network data planes. IEEE Access, 9, 113061-113076.
. Chen, Y., Lin, Q., Wei, W., Ji, J., Wong, K. C., & Coello, C. A. C. (2022). Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing. Knowledge-Based Systems, 244, 108505.
. Jaisingh, W., Nanjundan, P., & George, J. P. (2024). Machine Learning in Cyber Threats Intelligent System. In Artificial Intelligence for Cyber Defense and Smart Policing (pp. 1-20). Chapman and Hall/CRC.
. Zidi, I., Issaoui, I., El Khediri, S., & Khan, R. U. (2024). An approach based on NSGA-III algorithm for solving the multi-objective federated learning optimization problem. International Journal of Information Technology, 1-13.
. Yadav, S., Hashmi, H., & Vekariya, D. (2024). Mitigation of attacks via improved network security in IOT network environment using RNN. Measurement: Sensors, 101046.
. Rekeraho, A., Cotfas, D. T., Cotfas, P. A., Bălan, T. C., Tuyishime, E., & Acheampong, R. (2024). Cybersecurity challenges in IoT-based smart renewable energy. International Journal of Information Security, 23(1), 101-117.
. Alwaisi, Z., Soderi, S., & De Nicola, R. (2023, October). Detection of Energy Consumption Cyber Attacks on Smart Devices. In International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures (pp. 160-176). Cham: Springer Nature Switzerland.
. Brehler, M., Camphausen, L., Heidebroek, B., Krön, D., Gründer, H., & Camphausen, S. (2023). Making Machine Learning More Energy Efficient by Bringing It Closer to the Sensor. IEEE Micro.
. Bai, G., Chai, Z., Ling, C., Wang, S., Lu, J., Zhang, N., ... & Zhao, L. (2024). Beyond efficiency: A systematic survey of resource-efficient large language models. arXiv preprint arXiv:2401.00625.
. Jaiprakash, S. P., Arya, H. K., Gupta, I., & Badal, T. (2023). Energy Optimized Workflow Scheduling in IaaS Cloud: A Flower Pollination based Approach. Authorea Preprints.
. Kocot, B., Czarnul, P., & Proficz, J. (2023). Energy-aware scheduling for high-performance computing systems: A survey. Energies, 16(2), 890.
. Baidoo, C. Y. M., Yaokumah, W., & Owusu, E. (2023). Estimating Overhead Performance of Supervised Machine Learning Algorithms for Intrusion Detection. International Journal of Information Technologies and Systems Approach (IJITSA), 16(1), 1-19.
. Macy, R. J., Klein, L. B., Shuck, C. A., Rizo, C. F., Van Deinse, T. B., Wretman, C. J., & Luo, J. (2023). A scoping review of human trafficking screening and response. Trauma, Violence, & Abuse, 24(3), 1202-1219.
. Hu, Y., & Man, Y. (2023). Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives. Renewable and Sustainable Energy Reviews, 182, 113405.
. Chattaraj, R., & Chimalakonda, S. (2023, September). RJoules: An Energy Measurement Tool for R. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 2026-2029). IEEE.
. Raffin, G., & Trystram, D. (2024). Dissecting the software-based measurement of CPU energy consumption: a comparative analysis. arXiv preprint arXiv:2401.15985.
. Zhang, X., Ding, A. A., & Fei, Y. (2023, October). Deep-Learning Model Extraction Through Software-Based Power Side-Channel. In 2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD) (pp. 1-9). IEEE.
. Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L., & Ganiyu, S. (2023). Data-driven tools for building energy consumption prediction: A review. Energies, 16(6), 2574.
. Sallou, J., Cruz, L., & Durieux, T. (2023). EnergiBridge: Empowering Software Sustainability through Cross-Platform Energy Measurement. arXiv preprint arXiv:2312.13897.
. Loyarte-López, E., Barral, M., & Morla, J. C. (2020). Methodology for carbon footprint calculation towards sustainable innovation in intangible assets. Sustainability, 12(4), 1629.
. Rahman, F., O’Brien, C., Ahamed, S. I., Zhang, H., & Liu, L. (2011). Design and implementation of an open framework for ubiquitous carbon footprint calculator applications. Sustainable Computing: Informatics and Systems, 1(4), 257-274.
. Chen, J. L., Chen, W. C., & Kuo, A. (2016, September). Developing carbon footprint calculation software for display industry in Taiwan. In 2016 Electronics Goes Green 2016+(EGG) (pp. 1-7). IEEE.
. Sipilä, A., Partanen, L., & Porras, J. (2023, November). Carbon Footprint Calculations for a Software Company–Adapting GHG Protocol Scopes 1, 2 and 3 to the Software Industry. In International Conference on Software Business (pp. 442-455). Cham: Springer Nature Switzerland.
. Gaur, L., Afaq, A., Arora, G. K., & Khan, N. (2023). Artificial intelligence for carbon emissions using system of systems theory. Ecological Informatics, 102165.
. Peng, Y., Wang, Y., Chen, H., Wang, L., Luo, B., Tong, H., ... & Chen, S. (2024). Carbon reduction potential of a rain garden: A cradle-to-grave life cycle carbon footprint assessment. Journal of Cleaner Production, 434, 139806.
. Roopak, M., Tian, G. Y., & Chambers, J. (2020, January). An intrusion detection system against DDoS attacks in IoT networks. In 2020 10th annual computing and communication workshop and conference (CCWC) (pp. 0562-0567). IEEE.
. Dirik, M. (2022). Predicting credit card fraud using multipurpose classification based on evolutionary rules. Security and Privacy, 5(5), e239.
DOI: https://doi.org/10.31449/inf.v48i18.6234
This work is licensed under a Creative Commons Attribution 3.0 License.