Network Attack Detection Using NeuroEvolution of Augmenting Topologies (NEAT) Algorithm

Tamara Zhukabayeva - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan
Aigul Adamova - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan
Khu Ven-Tsen - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan
Zhanserik Nurlan - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan
Yerik Mardenov - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan
Nurdaulet Karabayev - International Science Complex “Astana”, Kabanbay Batyr 8, Astana, 020000, Kazakhstan

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The imperfection of existing intrusion detection methods and the changing nature of malicious actions on the attacker's part led to the Internet of Things (IoT) network interaction in an unsafe state. The actual problem of improving the technology of the IOT is counteracting malicious network impacts. In this regard, research and development aimed at creating effective tools for solving applied problems within the framework of this problem are becoming increasingly important.  This study seeks to develop tools for detecting anomalous network conditions resulting from malicious attacks. In particular, the accuracy of the identification of DoS and DDoS attacks is sufficient for operational use. This study analyzes various multi-level architectures, relevant communication protocols, and different types of network attacks. The presented research was conducted on open datasets TON_IOT DATASETS, which include multiple data sources collected from IoT sensors. The modified HyperNEAT algorithm was used as the basis for the development. The NEAT methodology used in the study allows you to combine various network nodes. Results of the study: a neuro-evolutionary algorithm for identifying DoS and DDoS attacks was implemented, integrated, and real-tested based on a multi-level analysis of network traffic combined with various adaptive modules. The accuracy of identifying DoS and DDoS attacks is 0.9242 in the Accuracy metric. The study implies that the proposed approach can be recommended for network intrusion detection, ensuring security when interacting with the IoT.


Internet of Things; attacks; HyperNEAT; neuro-evolutionary algorithm; wireless sensor network

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M. Aljabri et al., “Intelligent Techniques for Detecting Network Attacks: Review and Research Directions,” Sensors, vol. 21, no. 21, p. 7070, Oct. 2021, doi:

M. Shafiq, Z. Gu, O. Cheikhrouhou, W. Alhakami, and H. Hamam, “The Rise of ‘Internet of Things’: Review and Open Research Issues Related to Detection and Prevention of IoT-Based Security Attacks,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–12, Aug. 2022, doi:

P. Kumari and A. K. Jain, “A Comprehensive Study of DDoS Attacks over IoT Network and Their Countermeasures,” Computers & Security, p. 103096, Jan. 2023, doi:

U. Inayat, M. F. Zia, S. Mahmood, H. M. Khalid, and M. Benbouzid, “Learning-Based Methods for Cyber Attacks Detection in IoT Systems: A Survey on Methods, Analysis, and Future Prospects,” Electronics, vol. 11, no. 9, p. 1502, May 2022, doi:

A. Jerkins, “Motivating a market or regulatory solution to IoT insecurity with the Mirai botnet code,” 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2017, doi: 10.1109/ccwc.2017.7868464.

Y. Kayode Saheed, A. Idris Abiodun, S. Misra, M. Kristiansen Holone, and R. Colomo-Palacios, “A machine learning-based intrusion detection for detecting internet of things network attacks,” Alexandria Engineering Journal, vol. 61, no. 12, pp. 9395–9409, Dec. 2022, doi:

K. Levchenko, Ramamohan Paturi, and G. Varghese, “On the difficulty of scalably detecting network attacks,” Computer and Communications Security, Oct. 2004, doi:

T. Talaei Khoei and N. Kaabouch, “A Comparative Analysis of Supervised and Unsupervised Models for Detecting Attacks on the Intrusion Detection Systems,” Information, vol. 14, no. 2, p. 103, Feb. 2023, doi:

H. Bai, R. Cheng, and Y. Jin, “Evolutionary Reinforcement Learning: A Survey,” Intelligent Computing, Apr. 2023, doi:

B. Kaur et al., “Internet of Things (IoT) security dataset evolution: Challenges and future directions,” Internet of Things, p. 100780, Apr. 2023, doi:

M. A. Umer, K. N. Junejo, M. T. Jilani, and A. P. Mathur, “Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations,” International Journal of Critical Infrastructure Protection, p. 100516, Feb. 2022, doi:

A. Adamova, T. Zhukabayeva, and Y. Mardenov, “Machine Learning in Action: An Analysis of its Application for Fault Detection in Wireless Sensor Networks,” 2023 IEEE International Conference on Smart Information Systems and Technologies (SIST), May 2023, doi:

M. Bouzidi, N. Gupta, F. A. Cheikh, A. Shalaginov, and M. Derawi, “A Novel Architectural Framework on IoT Ecosystem, Security Aspects and Mechanisms: A Comprehensive Survey,” IEEE Access, pp. 1–1, 2022, doi:

A. Raj and S. D. Shetty, “IoT Eco-system, Layered Architectures, Security and Advancing Technologies: A Comprehensive Survey,” Wireless Personal Communications, Aug. 2021, doi:

R. Nath N and H. V Nath, “Critical analysis of the layered and systematic approaches for understanding IoT security threats and challenges,” Computers and Electrical Engineering, vol. 100, p. 107997, May 2022, doi:

B. Nagajayanthi, “Decades of Internet of Things Towards Twenty-first Century: A Research-Based Introspective,” Wireless Personal Communications, Nov. 2021, doi:

S. Graziani and M. G. Xibilia, “Innovative Topologies and Algorithms for Neural Networks,” Future Internet, vol. 12, no. 7, p. 117, Jul. 2020, doi: 10.3390/fi12070117.

B. Patel and P. Shah, “Operating system support, protocol stack with key concerns and testbed facilities for IoT: A case study perspective,” Journal of King Saud University - Computer and Information Sciences, Jan. 2021, doi:

S. Mahmoodi Khaniabadi, A. Javadpour, M. Gheisari, W. Zhang, Y. Liu, and A. K. Sangaiah, “An intelligent sustainable efficient transmission internet protocol to switch between User Datagram Protocol and Transmission Control Protocol in IoT computing,” Expert Systems, Sep. 2022, doi:

W. Bekri, T. Layeb, R. Jmal, and L. Fourati, “Intelligent IoT Systems: security issues, attacks, and countermeasures,” 2022 International Wireless Communications and Mobile Computing (IWCMC), May 2022, doi:

B. B. Gupta, P. Chaudhary, X. Chang, and N. Nedjah, “Smart defense against distributed Denial of service attack in IoT networks using supervised learning classifiers,” Computers & Electrical Engineering, vol. 98, p. 107726, Mar. 2022, doi:

K. O. Stanley and R. Miikkulainen, “Evolving Neural Networks through Augmenting Topologies,” Evolutionary Computation, vol. 10, no. 2, pp. 99–127, Jun. 2002, doi:

M. Y. Ibrahim, R. Sridhar, T. V. Geetha, and S. S. Deepika, “Advances in Neuroevolution through Augmenting Topologies – A Case Study,” 2019 11th International Conference on Advanced Computing (ICoAC), Dec. 2019, doi: 10.1109/icoac48765.2019.246825

A. Behjat, N. Maurer, S. Chidambaran, and S. Chowdhury, “Adaptive Neuroevolution with Genetic Operator Control and Two-Way Complexity Variation,” IEEE Transactions on Artificial Intelligence, pp. 1–14, 2022, doi:

J. Hohenheim, M. Fischler, S. Zarubica, and J. Stucki, “Combining Neuro-Evolution of Augmenting Topologies with Convolutional Neural Networks,” arXiv (Cornell University), Oct. 2022, doi:

H. Yang and Y. Kim, “Design and Implementation of Fast Fault Detection in Cloud Infrastructure for Containerized IoT Services,” Sensors, vol. 20, no. 16, p. 4592, Aug. 2020, doi: 10.3390/s20164592.

M. Rodríguez, Á. Alesanco, L. Mehavilla, and J. García, “Evaluation of Machine Learning Techniques for Traffic Flow-Based Intrusion Detection,” Sensors, vol. 22, no. 23, p. 9326, Jan. 2022, doi:

S. Kumar, S. Gupta, and S. Arora, “Research Trends in Network-Based Intrusion Detection Systems: A Review,” IEEE Access, vol. 9, pp. 157761–157779, 2021, doi:

Y. Mardenov, A. Adamova, T. Zhukabayeva, and M. Othman, “Enhancing Fault Detection in Wireless Sensor Networks Through Support Vector Machines: A Comprehensive Study,” Journal of Robotics and Control (JRC), vol. 4, no.6, pp. 868-877, 2023.

P. García-Teodoro, J. Díaz-Verdejo, G. Maciá-Fernández, and E. Vázquez, “Anomaly-based network intrusion detection: Techniques, systems and challenges,” Computers & Security, vol. 28, no. 1–2, pp. 18–28, Feb. 2009, doi:

P. Mishra, E. S. Pilli, V. Varadharajan, and U. Tupakula, “Intrusion detection techniques in cloud environment: A survey,” Journal of Network and Computer Applications, vol. 77, pp. 18–47, Jan. 2017, doi:

Kotenko, V. Desnitsky, and E. Novikova, “Defect Detection in Industrial IoT-based Machines: Case of Small Training Dataset,” 2023 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS), Nov. 2023, doi: 10.1109/iotais60147.2023.10346064.

A. Si-Ahmed, M. A. Al-Garadi, and N. Boustia, “Survey of Machine Learning based intrusion detection methods for Internet of Medical Things,” Applied Soft Computing, vol. 140, p. 110227, Jun. 2023, doi: 10.1016/j.asoc.2023.110227.

A. Jamalipour and S. Murali, “A Taxonomy of Machine-Learning-Based Intrusion Detection Systems for the Internet of Things: A Survey,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9444–9466, Jun. 2022, doi: 10.1109/jiot.2021.3126811.

P. Gupta, L. Yadav, and D. S. Tomar, “Internet of Things: A Survey on Fused Machine Learning-Based Intrusion Detection Approaches,” Advanced Machine Intelligence and Signal Processing, pp. 147–161, 2022, doi: 10.1007/978-981-19-0840-8_11.

M. V. R. Sarobin, P. Rukmani, and E. A. M. Anita, “Machine Learning-Based Intrusion Detection for Internet of Things Network Traffic,” Handbook of Research of Internet of Things and Cyber-Physical Systems, pp. 315–335, Mar. 2022, doi: 10.1201/9781003277323-17.