A Study on Heuristic Algorithms Combined With LR on a DNN-Based IDS Model to Detect IoT Attacks

  • Tran Thi Thanh Thuy Faculty of Electronic Engineering 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
  • Luong Duc Thuan Faculty of Telecommunication 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
  • Nguyen Hong Duc Faculty of Telecommunication 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
  • Hoang Trong Minh Faculty of Electronic Engineering 1, Posts and Telecommunication Institute of Technology, Hanoi, Vietnam
Keywords: Intrusion Detection System, Deep Neuron Network, Heuristic Algorithms, IoT-23 dataset


Current security challenges are made more difficult by the complexity and difficulty of spotting cyberattacks due to the Internet of Things explosive growth in connected devices and apps. Therefore, various sophisticated attack detection techniques have been created to address these issues in recent years. Due to their effectiveness and scalability, machine learning-based intrusion detection systems (IDSs) have increased. However, several factors, such as the characteristics of the training dataset and the training model, affect how well these AI-based systems identify attacks. In particular, the heuristic algorithms (GA, PSO, CSO, FA) optimized by the logistic regression (LR) approach employ it to pick critical features of a dataset and deal with data imbalance problems. This study offers an intrusion detection system (IDS) based on a deep neural network and heuristic algorithms combined with LR to boost the accuracy of attack detections. Our proposed model has a high attack detection rate of up to 99% when testing on the IoT-23 dataset.


Al-Thanoon, N. A., Qasim, O. S., and Algamal, Z. Y. A new hybrid firefly algorithm and particle swarm optimization for tuning parameter estimation in penalized support vector machine with application in chemometrics. Chemometrics and Intelligent Laboratory Systems 184 (2019), 142–152.

Alshamy, R., Ghurab, M., Othman, S., and Alshami, F. Intrusion detection model for imbalanced dataset using smote and random forest algorithm. In Advances in Cyber Security: Third International Conference, ACeS 2021, Penang, Malaysia, August 24–25, 2021, Revised Selected Papers 3 (2021), Springer, pp. 361–378.

Camacho-Villalon, C. L., Dorigo, M., and Stutzle, T. An analysis of why cuckoo search does not bring any novel ideas to optimization. Computers & Operations Research 142 (2022), 105747.

Camacho-Villalon, C. L., Dorigo, M., and Stutzle, T. Exposing the grey wolf, moth-flame, whale, firefly, bat, and antlion algorithms: six misleading optimization techniques inspired by bestial metaphors. International Transactions in Operational Research (2022).

Chen, P., Guo, Y., Zhang, J., Wang, Y., and Hu, H. A novel preprocessing methodology for dnn-based intrusion detection. In 2020 IEEE 6th International Conference on Computer and Communications (ICCC) (2020), IEEE, pp. 2059–2064.

Choudhary, S., and Kesswani, N. Analysis of kdd-cup’99, nsl-kdd and unsw-nb15 datasets using deep learning in iot. Procedia Computer Science 167 (2020), 1561–1573.

Gamage, S., and Samarabandu, J. Deep learning methods in network intrusion detection: A survey and an objective comparison. Journal of Network and Computer Applications 169 (2020), 102767.

Heidari, A., and Jabraeil Jamali, M. A. Internet of things intrusion detection systems: A comprehensive review and future directions. Cluster Computing (2022), 1–28.

Hosseini, S. A new machine learning method consisting of ga-lr and ann for attack detection. Wireless Networks 26 (2020), 4149–4162.

Kanagaraj, G., Masthan, S. S., and Vincent, F. Y. Meta-heuristics based inverse kinematics of robot manipulator’s path tracking capability under joint limits. In Mendel (2022), vol. 28, pp. 41–54.

Kashani, M. H., Madanipour, M., Nikravan, M., Asghari, P., and Mahdipour, E. A systematic review of iot in healthcare: Applications, techniques, and trends. Journal of Network and Computer Applications 192 (2021), 103164.

Kshirsagar, V. K., Tidke, S. M., and Vishnu, S. Intrusion detection system using genetic algorithm and data mining: An overview. International Journal of Computer Science and Informatics ISSN (PRINT) 2231, 5292 (2012).

Kudela, J. A critical problem in benchmarking and analysis of evolutionary computation methods. Nature Machine Intelligence, 4 (2022), 1238–1245.

Li, L.-H., Ahmad, R., Tsai, W.-C., and Sharma, A. K. A feature selection based dnn for intrusion detection system. In 2021 15th International Conference on Ubiquitous Information Management and Communication (IMCOM) (2021), IEEE, pp. 1–8.

Liu, Z., Liang, X., and Huang, M. Design of logistic regression health assessment model using novel quantum pso. In 2018 IEEE 3rd International Conference on Cloud Computing and Internet of Things (CCIOT) (2018), IEEE, pp. 39–42.

Maalouf, M. Logistic regression in data analysis: an overview. International Journal of Data Analysis Techniques and Strategies 3, 3 (2011), 281–299.

Marso, S., and El Merouani, M. Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm. Procedia Computer Science 170 (2020), 1134–1140.

Muller, J. Improving initial aerofoil geometry using aerofoil particle swarm optimisation. In Mendel (2022), vol. 28, pp. 63–67.

Ngatman, M. F., Sharif, J. M., and Ngadi, M. A. A study on modified pso algorithm in cloud computing. In 2017 6th ICT international student project conference (ICT-ISPC) (2017), IEEE, pp. 1–4.

Parmisano, A., Garcia, S., and Erquiaga, M. J. A labeled dataset with malicious and benign iot network traffic. Stratosphere Laboratory: Praha, Czech Republic (2020).

Potluri, S., and Diedrich, C. Accelerated deep neural networks for enhanced intrusion detection system. In 2016 IEEE 21st international conference on emerging technologies and factory automation (ETFA) (2016), IEEE, pp. 1–8.

Qasim, O. S., and Algamal, Z. Y. Feature selection using particle swarm optimization-based logistic regression model. Chemometrics and Intelligent Laboratory Systems 182 (2018), 41–46.

Qi, B., Wu, M., and Zhang, L. A dnnbased object detection system on mobile cloud computing. In 2017 17th International Symposium on Communications and Information Technologies (ISCIT) (2017), IEEE, pp. 1–6.

Rachit, Bhatt, S., and Ragiri, P. R. Security trends in internet of things: A survey. SN Applied Sciences 3 (2021), 1–14.

Sahar, N., Mishra, R., and Kalam, S. Deep learning approach-based network intrusion detection system for fog-assisted iot. In Proceedings of international conference on big data, machine learning and their applications: ICBMA 2019 (2021), Springer, pp. 39–50.

Samizadeh Nikoui, T., Rahmani, A. M., Balador, A., and Haj Seyyed Javadi, H. Internet of things architecture challenges: A systematic review. International Journal of Communication Systems 34, 4 (2021), e4678.

Sarker, I. H. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science 2, 6 (2021), 420.

Singh, G., and Khare, N. A survey of intrusion detection from the perspective of intrusion datasets and machine learning techniques. International Journal of Computers and Applications 44, 7 (2022), 659–669.

Wang, K.-J., Makond, B., Chen, K.-H., and Wang, K.-M. A hybrid classifier combining smote with pso to estimate 5-year survivability of breast cancer patients. Applied Soft Computing 20 (2014), 15–24.

Wang, S., Balarezo, J. F., Kandeepan, S., Al-Hourani, A., Chavez, K. G., and Rubinstein, B. Machine learning in network anomaly detection: A survey. IEEE Access 9 (2021), 152379–152396.

Zhang, H., Shi, Y., Yang, X., and Zhou, R. A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance. Research in International Business and Finance 58 (2021), 101482.

Zhang, Y., Li, P., and Wang, X. Intrusion detection for iot based on improved genetic algorithm and deep belief network. IEEE Access 7 (2019), 31711–31722.

Zhang, Z. Speech feature selection and emotion recognition based on weighted binary cuckoo search. Alexandria Engineering Journal 60, 1 (2021), 1499–1507.

How to Cite
Thuy, T.T., Thuan, L., Duc, N. and Minh, H. 2023. A Study on Heuristic Algorithms Combined With LR on a DNN-Based IDS Model to Detect IoT Attacks. MENDEL. 29, 1 (Jun. 2023), 62-70. DOI:https://doi.org/10.13164/mendel.2023.1.062.
Research articles