Intrusion Detection System with an Ensemble Learning and Feature Selection Framework for IoT Networks

References

• Y. Zhou, G. Cheng, S. Jiang, and M. Dai, “Building an efficient intrusion detection system based on feature selection and ensemble classifier,” Comput. Netw., Vol. 174, pp. 107247, Mar. 2020.
   Web of Science ®Google Scholar
• A. Davahli, M. Shamsi, and G. Abaei, “Hybridizing genetic algorithm and grey wolf optimizer to advance an intelligent and lightweight intrusion detection system for IoT wireless networks,” J. Ambient. Intell. Humaniz. Comput., Vol. 11, pp. 5581–609, Nov. 2020.
   Web of Science ®Google Scholar
• C. Samuel, B. M. Alvarez, E. G. Ribera, P. P. Ioulianou, and V. G. Vassilakis, “Performance evaluation of a wormhole detection method using round-trip times and hop counts in RPL-based 6LoWPAN networks,” in 12th International Symposium on Communication Systems, Networks and Digital Signal Processing, Porto, 2020, pp. 1–6.
   Google Scholar
• C. S. XianweiGao, Z. Changzhen Hu, and Z. Liu, “An adaptive ensemble machine learning model for intrusion detection,” IEEE Access, Vol. 7, pp. 82512–21, Jun. 2019.
   Google Scholar
• M. Asadi, M. A. Jabraeil Jamali, S. Parsa, and V. Majidnezhad, “Detecting botnet by using particle swarm optimization algorithm based on voting system,” Future Gener. Comput. Syst., Vol. 107, pp. 95–111, Feb. 2020.
   Web of Science ®Google Scholar
• C. Gnana Kousalya, and G. S. Anandha Mala, “Secure and energy-efficient traffic-aware key management scheme for wireless sensor network,” Int. J. Wireless Inf. Networks, Vol. 19, pp. 112–21, 2012.
   Google Scholar
• P. Kumar, G. P. Gupta, and R. Tripathi, “An ensemble learning and fog-cloud architecture-driven cyber-attack detection framework for IoMT networks,” Comput. Commun., Vol. 166, pp. 110–24, Dec. 2020.
   Web of Science ®Google Scholar
• G. Kaur, “A comparison of two hybrid ensemble techniques for network anomaly detection in spark distributed environment,” J. Inf. Secur. Appl., Vol. 55, pp. 102601, Dec. 2020.
   Web of Science ®Google Scholar
• B. A. Tama, M. Comuzzi, and K.-H. Rhee, “TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system,” IEEE Access, Vol. 7, pp. 94497–507, Jul. 2019.
   Google Scholar
• T. Hamed, R. Dara, and S. C. Kremer, “Network intrusion detection system based on recursive feature addition and bigram technique,” Comput. Secur., Vol. 73, pp. 137–55, Mar. 2018.
   Web of Science ®Google Scholar
• N. Kunhare, R. Tiwari, and J. Dhar, “Particle swarm optimization and feature selection for intrusion detection system,” Sādhanā, Vol. 45, pp. 1–14, May 2020.
   Web of Science ®Google Scholar
• A. Nagaraja, B. Uma, and R. K. Gunupudi, “UTTAMA: An intrusion detection system based on feature clustering and feature transformation,” Found. Sci., Vol. 25, pp. 1049–75, Dec. 2020.
   Web of Science ®Google Scholar
• Kunal, and M. Dua, “Attribute selection and ensemble classifier based novel approach to intrusion detection system,” Procedia. Comput. Sci., Vol. 167, pp. 2191–9, Jan. 2020.
   Google Scholar
• B. B. Zarpelão, R. S. Miani, C. T. Kawakani, and S. C. de Alvarenga, “A survey of intrusion detection in Internet of Things,” J. Netw. Comput. Appl., Vol. 84, pp. 25–37, 2017.
   Web of Science ®Google Scholar
• N. Chaabouni, M. Mosbah, A. Zemmari, C. Sauvignac, and P. Faruki, “Network intrusion detection for IoT security based on learning techniques,” IEEE Commun. Surv. Tutorials, Vol. 21, no. 3, pp. 2671–701, 2019.
   Web of Science ®Google Scholar
• A. K. Cherukuri, I. S. Thaseen, G. Li, X. Liu, V. Das, and A. Raj, “Integrity of IoT network flow records in encrypted traffic analytics,” in Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications, A. I. Awad, and J. Abawajy, Eds. 2021, pp. 177–205.
   Google Scholar
• A. Verma, and V. Ranga, “Machine learning based intrusion detection systems for IoT applications,” Wirel. Pers. Commun., Vol. 111, pp. 2287–310, Apr. 2020.
   Web of Science ®Google Scholar
• A. Khraisat, I. Gondal, P. Vamplew, J. Kamruzzaman, and A. Alazab, “A novel ensemble of hybrid intrusion detection system for detecting internet of things attacks,” Electronics., Vol. 8, p. 1210, 2019.
   Web of Science ®Google Scholar
• Y. Yanmiao Li, H. Zhi Liu, Y. X. YushuoZheng, Y. Zhao, and L. Cui, “Robust detection for network intrusion of industrial IoT based on multi-CNN fusion,” Measurement, Vol. 154, pp. 107450, Oct. 2019.
   Web of Science ®Google Scholar
• Y. Zhang, P. Li, and X. Wang, “Intrusion detection for IoT based on improved genetic algorithm and deep belief network,” IEEE Access, Vol. 7, pp. 31711–22, Mar. 2019.
   Google Scholar
• J. Mebawondu Olamantanmi, O. D. Alowolodu, J. O. Mebawondu, and A. O. Adetunmbi, “Network intrusion detection system using supervised learning paradigm,” Sci. Afr., Vol. 9, pp. e00497, Jul. 2020.
   Google Scholar
• S. T. Ikram, A. K. Cherukuri, B. Poorva, P. S. Ushasree, Y. Zhang, X. Liu, and G. Li, “Anomaly detection using XGBoost ensemble of deep neural network models,” Cybern. Inf. Technol., Vol. 21, no. 3, pp. 175–88, 2021.
   Web of Science ®Google Scholar
• S. Tsimenidis, T. Lagkas, and K. Rantos, “Deep learning in IoT intrusion detection,” J. Netw. Syst. Manage., Vol. 30, no. 1, pp. 1–40, 2022.
   Web of Science ®Google Scholar
• Y. Al-Hadhrami, and F. K. Hussain, “Real time dataset generation framework for intrusion detection systems in IoT,” Future Gener. Comput. Syst., Vol. 108, pp. 414–23, Feb. 2020.
   Web of Science ®Google Scholar
• A. K. Shukla, “Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm,” Neural Comput. Appl., Vol. 33, pp. 1–21, Jul. 2021.
   Web of Science ®Google Scholar
• J. Pokala, and B. Lalitha, “A novel intrusion detection system for RPL based IoT networks with bio-inspired feature selection and ensemble classifier,” 2021.
   Google Scholar
• B. S. Bhati, and C. S. Rai, “Analysis of support vector machine-based intrusion detection techniques,” Arab. J. Sci. Eng., Vol. 45, pp. 2371–83, Apr. 2020.
   Web of Science ®Google Scholar
• A. Rahman, A. T. Asyhari, L. S. Leong, G. B. Satrya, M. Hai Tao, and M. F. Zolkipli, “Scalable machine learning-based intrusion detection system for IoT-enabled smart cities,” Sustain. Cities Soc., Vol. 61, pp. 102324, Jun. 2020.
   Web of Science ®Google Scholar
• M. A. Khan, M. A. Khan Khattk, S. Latif, A. A. Shah, M. Ur Rehman, W. Boulila, and J. Ahmad., “Voting classifier-based intrusion detection for IoT networks,” in Advances on Smart and Soft Computing, Singapore: Springer, 2022, pp. 313–28.
   Google Scholar
• S. Dwivedi, M. Vardhan, and S. Tripathi, “Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection,” Cluster. Comput., Vol. 24, pp. 1881–900, Dec. 2020.
   Web of Science ®Google Scholar
• C. Gnana Kousalya, and G. Rohini, “Energy efficient geographical key management scheme forauthentication in mobile wireless sensor networks,” Wireless Netw., Vol. 23, pp. 1–11, 2017.
   Web of Science ®Google Scholar
• N. V. Chawla, K. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res., Vol. 16, pp. 321–57, Jun. 2002.
   Web of Science ®Google Scholar
• Y. Yan, R. Liu, Z. Ding, X. Du, J. Chen, and Y. Zhang, “A parameter-free cleaning method for SMOTE in imbalanced classification,” IEEE Access, Vol. 7, pp. 23537–48, Feb. 2019.
   Web of Science ®Google Scholar
• M. T. Nguyen, and K. Kim, “Genetic convolutional neural network for intrusion detection systems,” Future Gener. Comput. Syst., Vol. 113, pp. 418–27, Jul. 2020.
   Web of Science ®Google Scholar
• A. Pashaei, M. Ghatee, and H. Sajedi, “Convolution neural network joint with mixture of extreme learning machines for feature extraction and classification of accident images,” Journal of Real-Time Image Processing, Vol. 17, pp. 1051–66, Aug. 2020.
   Web of Science ®Google Scholar
• G. Rohini, “Autonomous mobile object classification,” in National Conference on Recent Trends in Information and Communication (NCRTIC–18), Ernakulam, 2018.
   Google Scholar
• L. Abualigaha, A. Diabatb, S. Mirjalilid, M. AbdElazizf, and A. H. Gandomih, “The arithmetic optimization algorithm,” Comput. Methods. Appl. Mech. Eng., Vol. 376, pp. 113609 , Apr. 2021.
   Web of Science ®Google Scholar
• S. Arora, and S. Singh, “Butterfly optimization algorithm: A novel approach for global optimization,” Soft. Comput., Vol. 23, pp. 715–34, Feb. 2019.
   Web of Science ®Google Scholar
• S. Arora, and P. Anand, “Binary butterfly optimization approaches for feature selection,” Expert. Syst. Appl., Vol. 116, pp. 147–60, Feb. 2019.
   Web of Science ®Google Scholar
• E. Pertishiya, A. Fathimah, and G. Rohini, “Energy harvesting from machineries for industries: Vibration as a source of energy,” in International Conference on System, Computation, Automation and Networking, Pondicherry, 2020, pp. 1–5.
   Google Scholar
• S. M. Kasongo, and Y. Sun, “Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset,” J. Big. Data., Vol. 7, pp. 1–20, Nov. 2020.
   Google Scholar
• B. S. Bhati, C. S. Rai, B. Balamurugan, and F. Al-Turjman, “An intrusion detection scheme based on the ensemble of discriminant classifiers,” Comput. Electr. Eng., Vol. 86, pp. 106742, Sep. 2020.
   Web of Science ®Google Scholar
• B. Janney, G. Rohini, and R. Pauline, “Detection of malignancy of skin lesions based on local texture and fractal analysis,” Res. J. Pharm. Biol. Chem. Sci., Vol. 6, pp. 408–12, Jan. 2015.
   Google Scholar
• S. Sreejith, H. Khanna Nehemiah, and A. Kannan, “Clinical data classification using an enhanced SMOTE and chaotic evolutionary feature selection,” Comput. Biol. Med., Vol. 126, pp. 103991, Sep. 2020.
   PubMed Web of Science ®Google Scholar
• E.-S. M. El-Kenawy, A. Ibrahim, S. Mirjalili, M. M. Eid, and S. E. Hussein, “Novel feature selection and voting classifier algorithms for COVID-19 classification in CT images,” IEEE Access, Vol. 8, pp. 179317–35, Oct. 2020.
   PubMedGoogle Scholar
• T. Sasilatha, K. R. Anupriya, C. Gnana Kousalya, and S. Arun, “Digital image falsification detection system for effective data communication,” Int. J. Eng. Adv. Technol. (IJEAT), Vol. 9, 4081–6, Dec. 2019.
   Google Scholar
• G. Rohini, and C. Gnana Kousalya, “Secured transmission of data in mobile wireless sensor networks,” Int. J. Eng. Adv. Technol., Vol. II, pp. 822–6, Jan.-March 2016.
   Google Scholar
• A. Verma, and V. Ranga, “Evaluation of network intrusion detection systems for RPL based 6LoWPAN networks in IoT,” Wirel. Pers. Commun., Vol. 108, no. 3, pp. 1571–94, 2019.
   Web of Science ®Google Scholar
• J. Yang, Y. Sheng, and J. Wang, “A GBDT-paralleled quadratic ensemble learning for intrusion detection system,” IEEE Access, Vol. 8, pp. 175467–82, Sep. 2020.
   Google Scholar