Abstract
In the Internet of Things, intrusion detection entails keeping an eye on device activity and network traffic in order to spot and address possible security lapses. Early threat detection, sensitive data protection, and cyberattack mitigation are some of its benefits. False positives, resource-intensive monitoring, and the difficulty of staying up to date with changing threats in the ever-changing IoT world are possible downsides. This study suggested a novel Bagging-DRL-based Intrusion Detection model, which comprises of four stages, to address these issues. (i) Gathering and pre-processing data; (ii) extracting features; (iii) selecting features; and (iv) utilizing deep reinforcement learning for intrusion detection. Initially, the CSE-CIC-IDS2018 and NSL-KDD databases are used to get the raw data. Z-Score normalization and data cleaning were used as preprocessing techniques for the gathered data. Features such as correlation, protocol-based features, higher-order statistical features, statistical features, and the newly proposed Enriched Principal Component Optimization with Self-Improved Seagull Algorithm (EPCO-SISA) are extracted from the pre-processed data. A novel Correlation-based Recursive Feature Elimination (C-RFE) method is used to choose the best features from the extracted features. Finally, Deep Reinforcement Learning is used to detect the incursion using the features that have been chosen. In order to improve detection accuracy, the DRL combines Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and Optimized Recurrent Neural Networks (O-RNN). To do this, the weight function of the RNN is adjusted using the recently developed Self-Improved Seagull Optimization Algorithm (SI-SOA). The result is derived from Deep Reinforcement Learning's bagging value. The suggested model's performance is implemented using the MATLAB platform, and performance metrics including accuracy, precision, recall, and F1-score are used to evaluate the model's performance. The suggested model outperformed previous efforts with the maximum accuracy of 0.9836 and 0.9606 on the NSL-KDD and CSE-CIC-IDS2018 datasets, respectively.
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References
Brindha Devi V, Ranjan NM, Sharma H (2022) IoT attack detection and mitigation with optimized deep learning techniques. Cybernetics and Systems, pp1–27.
Regan C, Nasajpour M, Parizi RM, Pouriyeh S, Dehghantanha A, Choo KKR (2022) Federated IoT attack detection using decentralized edge data. Machine Learn Appl 8:100263
Ravi N, Shalinie SM (2020) Semisupervised-learning-based security to detect and mitigate intrusions in IoT networks. IEEE Internet Things J 7(11):11041–11052
Filus K, Domańska J, Gelenbe E (2021) Random neural network for lightweight attack detection in the iot. In: Modelling, Analysis, and Simulation of Computer and Telecommunication Systems: 28th International Symposium, MASCOTS 2020, Nice, France, November 17–19, 2020, Revised Selected Papers 28 (pp. 79–91). Springer International Publishing
Pecori R, Tayebi A, Vannucci A, Veltri L (2020) IoT attack detection with deep learning analysis. In: 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1–8). IEEE
Injadat, M., Moubayed, A. and Shami, A., 2020, December. Detecting botnet attacks in IoT environments: An optimized machine learning approach. In 2020 32nd International Conference on Microelectronics (ICM) (pp. 1–4). IEEE.
Ariffin TAMT, Abdullah SNHS, Fauzi F, Murah MZ (2022) IoT attacks and mitigation plan: a preliminary study with machine learning algorithms, 2022 International Conference on Business Analytics for Technology and Security (ICBATS), Dubai, United Arab Emirates, pp 1–6. https://doi.org/10.1109/ICBATS54253.2022.9758933
Matheu-García SN, Skarmeta A (2022) Defining the threat manufacturer usage description model for sharing mitigation actions, 2022 1st International Conference on 6G Networking (6GNet), Paris, France, pp 1–4. https://doi.org/10.1109/6GNet54646.2022.9830415
Malhotra M, Ganjoo M, Kulkarni S, Paranjape S, Kelkar S (2020) Mitigating Iot attacks. In: Smart medical networks using enhanced dirichlet based algorithm for trust management system, 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONNECT), Bangalore, India, pp 1–6. https://doi.org/10.1109/CONECCT50063.2020.9198414
Meidan Y, Avraham D, Libhaber H, Shabtai A (2022) CADeSH: Collaborative Anomaly Detection for Smart Homes, in IEEE Internet Things J. https://doi.org/10.1109/JIOT.2022.3194813
Shayshab Azad KM, Hossain N, Islam MJ, Rahman A, Kabir S (2021) Preventive determination and avoidance of DDoS attack with SDN over the IoT networks, 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI), Rajshahi, Bangladesh, pp 1–6. https://doi.org/10.1109/ACMI53878.2021.9528133
Lou X et al (2020) Assessing and mitigating impact of time delay attack: case studies for power grid controls. IEEE J Sel Areas Commun 38(1):141–155. https://doi.org/10.1109/JSAC.2019.2951982
Tawfik M, Al-Zidi NM, Alsellami B, Al-Hejri AM, Nimbhore S (2021) Internet of things-based middleware against cyber-attacks on smart homes using software-defined networking and deep learning, 2021 2nd International Conference on Computational Methods in Science & Technology (ICCMST), Mohali, India, pp 7–13. https://doi.org/10.1109/ICCMST54943.2021.00014
Satam P, Satam S, Hariri S, Alshawi A (2020) Anomaly behavior analysis of IoT protocols, In: Modeling and design of secure internet of things, IEEE, pp 295–330. https://doi.org/10.1002/9781119593386.ch13
Diro AA, Chilamkurti N (2018) Distributed attack detection scheme using deep learning approach for Internet of Things. Futur Gener Comput Syst 82:761–768
Sethi K, Sai Rupesh E, Kumar R, Bera P, Venu Madhav Y (2020) A context-aware robust intrusion detection system: a reinforcement learning-based approach. Int J Inf Secur 19:657–678
Baniasadi S, Rostami O, Martín D, Kaveh M (2022) A novel deep supervised learning-based approach for intrusion detection in IoT systems. Sensors 22(12):4459
Alruhaily NM, Ibrahim DM (2021) A multi-layer machine learning-based intrusion detection system for wireless sensor networks. Int J Adv Comput Sci Appl 12(4):281–288
Da Costa KA, Papa JP, Lisboa CO, Munoz R, de Albuquerque VHC (2019) Internet of things: a survey on machine learning-based intrusion detection approaches. Comput Netw 151:147–157
Rahman MA, Asyhari AT, Leong LS, Satrya GB, Tao MH, Zolkipli MF (2020) Scalable machine learning-based intrusion detection system for IoT-enabled smart cities. Sustain Cities Soc 61:102324
Dey S, Ye Q, Sampalli S (2019) A machine learning-based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks. Information Fusion 49:205–215
Gupta SK, Tripathi M, Grover J (2022) Hybrid optimization and deep learning-based intrusion detection system. Comput Electr Eng 100:107876
Ramkumar MP, Reddy PB, Thirukrishna JT, Vidyadhari C (2022) Intrusion detection in big data using hybrid feature fusion and optimization enabled deep learning based on spark architecture. Comput Secur 116:102668
Sethi K, Madhav YV, Kumar R, Bera P (2021) Attention-based multi-agent intrusion detection systems using reinforcement learning. J Inform Security Appl 61:102923
Xu Y, Xu W, Wang Z, Lin J, Cui S (2019) Load balancing for ultra-dense networks: A DRL -based approach. IEEE Internet Things J 6(6):9399–9412
Mamdouh Farghaly H, Abd El-Hafeez T (2023) A high-quality feature selection method based on frequent and correlated items for text classification. Soft Computing 27(16):11259–11274
Mamdouh Farghaly H, Abd El-Hafeez T (2022) A new feature selection method based on frequent and associated itemsets for text classification. Concurr Comput: Pract Exp 34(25):e7258
Khairy M et al. (2021) User awareness of privacy, reporting system and cyberbullying on Facebook. Advanced machine learning technologies and applications: proceedings of AMLTA 2021. Springer International Publishing
Gao J, et al. (2019) Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis. Energies 12.7:1223
Yang S, Linares-Barranco B, Chen B (2022) Heterogeneous ensemble-based spike-driven few-shot online learning. Front Neurosci 16:850932
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E., G.F., S., S. Enhanced intrusion detection in wireless sensor networks using deep reinforcement learning with improved feature extraction and selection. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19305-6
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DOI: https://doi.org/10.1007/s11042-024-19305-6