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Optimally Configured Deep Convolutional Neural Network for Attack Detection in Internet of Things: Impact of Algorithm of the Innovative Gunner

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Abstract

Nowadays, the internet of things (IoT) has gained significant research attention. It is becoming critically imperative to protect IoT devices against cyberattacks with the phenomenal intensification. The malicious users or attackers might take control of the devices and serious things will be at stake apart from privacy violation. Therefore, it is important to identify and prevent novel attacks in the IoT context. This paper proposes a novel attack detection system by interlinking the development and operations framework. This proposed detection model includes two stages such as proposed feature extraction and classification. The preliminary phase is feature extraction, the data from every application are processed by integrating the statistical and higher-order statistical features together with the extant features. Based on these extracted features the classification process is evolved for this, an optimized deep convolutional neural network (DCNN) model is utilized. Besides, the count of filters and filter size in the convolution layer, as well as the activation function, are optimized using a new modified algorithm of the innovative gunner (MAIG), which is the enhanced version of the AIG algorithm. Finally, the proposed work is compared and proved over other traditional works concerning positive and negative measures as well. The experimental outcomes show that the proposed MAIG algorithm for application 1 under the GAF-GYT attack achieves higher accuracy of 64.52, 2.38 and 3.76% when compared over the methods like DCNN, AIG and FAE-GWO-DBN, respectively.

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Abbreviations

IoT :

Internet of Things

SVM :

Support Vector Machine

NN :

Neural Network

KNN :

K Nearest Neighbor

DL :

Deep Learning

PD :

Perceptron Detection

PDE :

PD with Enhancement

DoS :

Denial of service

CNN :

Convolutional Neural Network

PSI :

Printable Strings Information

LR :

Linear Regression

ANN :

Artificial Neural Network

RF :

Random Forest

ML :

Machine Learning

DT :

Decision Tree

SPRT :

Sequential Probability Ratio Test

ABC :

Artificial Bee Colony

RPL :

Routing Protocol for Low power and lossy networks

ESFCM :

ELM-based Semi-supervised Fuzzy C-Means

ELM :

Extreme Learning Machine

ADE :

Averaged Dependence Estimator

AIG :

Algorithm of the Innovative Gunner

References

  1. Huang, X. (2020). Intelligent remote monitoring and manufacturing system of production line based on industrial Internet of Things. Computer Communications, 150, 421–428.

    Article  Google Scholar 

  2. Wang, Y. (2020). Construction and simulation of performance evaluation index system of Internet of Things based on cloud model. Computer Communications, 153, 177–187.

    Article  Google Scholar 

  3. Lyu, Yi., & Yin, P. (2020). Internet of Things transmission and network reliability in complex environment. Computer Communications, 150, 757–763.

    Article  Google Scholar 

  4. Sun, C. (2020). Research on investment decision-making model from the perspective of Internet of Things + Big data. Future Generation Computer Systems, 107, 286–292.

    Article  Google Scholar 

  5. Pour, M. S., Mangino, A., Friday, K., Rathbun, M., & Ghan, N. (2020). On data-driven curation, learning and analysis for inferring evolving internet-of-Things (IoT) botnets in the wild. Computers & Security, 91, 101707.

    Article  Google Scholar 

  6. Chen, Y., Kintis, P., Antonakakis, M., Nadji, Y., & Farrell, M. (2017). Measuring lower bounds of the financial abuse to online advertisers: A four year case study of the TDSS/TDL4 Botnet. Computers & Security, 67, 164–180.

    Article  Google Scholar 

  7. Koroniotis, N., Moustafa, N., Sitnikova, E., & Turnbull, B. (2019). Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Generation Computer Systems, 100, 779–796.

    Article  Google Scholar 

  8. Asadi, M., Ali, M., Jamali, J., Parsa, S., & Majidnezhad, V. (2020). Detecting botnet by using particle swarm optimization algorithm based on voting system”. Future Generation Computer Systems, 107, 95–111.

    Article  Google Scholar 

  9. Jung, W., Zhao, H., Sun, M., & Zhou, G. (2020). IoT botnet detection via power consumption modelling. Smart Health, 15, 100103.

    Article  Google Scholar 

  10. Alauthman, M., Aslam, N., Al-kasassbeh, M., Suleman Khan, K. K., & Choo, R. (2020). An efficient reinforcement learning-based Botnet detection approach. Journal of Network and Computer Applications, 150, 15.

    Article  Google Scholar 

  11. Mousavi, S. H., Khansari, M., & Rahmani, R. (2020). A fully scalable big data framework for Botnet detection based on network traffic analysis. Information Sciences, 512, 629–640.

    Article  Google Scholar 

  12. Shafiq, M., Tian, Z., Sun, Y., & Xiaojiang, Du. (2020). Mohsen Guizani”, Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city”. Future Generation Computer Systems, 107, 433–442.

    Article  Google Scholar 

  13. Alfian, G., Syafrudin, M., Farooq, U., Ma’arif, M. R., & Rhee, J. (2020). Improving efficiency of RFID-based traceability system for perishable food by utilizing IoT sensors and machine learning model. Food Control, 110, 107016.

    Article  Google Scholar 

  14. Cheng, J. C. P., Chen, W., Chen, K., & Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms”. Automation in Construction, 112, 103087.

    Article  Google Scholar 

  15. Azar, J., Makhoul, A., Barhamgi, M., & Couturier, R. (2019). An energy efficient IoT data compression approach for edge machine learning. Future Generation Computer Systems, 96, 168–175.

    Article  Google Scholar 

  16. Marsaline Beno, M., Valarmathi, I. R., Swamy, S. M., & Rajakumar, B. R. (2014). Threshold prediction for segmenting tumour from brain MRI scans. International Journal of Imaging Systems and Technology, 24(2), 129–137. https://doi.org/10.1002/ima.22087.

    Article  Google Scholar 

  17. Ninu Preetha, N. S., Brammya, G., Ramya, R., Praveena, S., Binu, D., & Rajakumar, B. R. (2018). Grey wolf optimisation-based feature selection and classification for facial emotion recognition. IET Biometrics, 7(5), 490–499. https://doi.org/10.1049/iet-bmt.2017.0160.

    Article  Google Scholar 

  18. Aloysius George and B. R. Rajakumar (2013)"On hybridizing fuzzy min max neural network and firefly algorithm for automated heart disease diagnosis”, Fourth international conference on computing, communications and networking technologies, Tiruchengode, India

  19. Ren, Z., Haomin, Wu., Ning, Q., Hussain, I., & Chen, B. (2020). End-to-end malware detection for android IoT devices using deep learning”. Ad Hoc Networks, 101, 15.

    Article  Google Scholar 

  20. Brun, O., & Yin, Y. (2018). Erol Gelenbe”, deep learning with dense random neural network for detecting attacks against IoT-connected home environments”. Procedia Computer Science, 134, 458–463.

    Article  Google Scholar 

  21. GhouseBasha, T. S., Aloysius, G., Rajakumar, B. R., Giri Prasad, M. N., & Sridevi, P. V. (2012). A constructive smart antenna beam-forming technique with spatial diversity. IET Microwaves, Antennas & Propagation, 6(7), 773–780. https://doi.org/10.1049/iet-map.2011.0356.

    Article  Google Scholar 

  22. Swamy, SM., Rajakumar, BR. & Valarmathi, IR (2013) “Design of hybrid wind and photovoltaic power system using opposition-based genetic algorithm with cauchy mutation”, IET Chennai Fourth international conference on sustainable energy and intelligent systems (SEISCON 2013), Chennai, India.

  23. Rajakumar, B. R. (2018). Optimization using lion algorithm: a biological inspiration from lion’s social behavior. Evolutionary Intelligence, 11(1–2), 31–52. https://doi.org/10.1007/s12065-018-0168-y.

    Article  Google Scholar 

  24. Rajakumar, B. R. (2014). Lion algorithm for standard and large scale bilinear system identification: A global optimization based on Lion’s social behavior. IEEE Congress on Evolutionary Computation. https://doi.org/10.1109/CEC.2014.6900561.

    Article  Google Scholar 

  25. Rajakumar, BR. (2012) The Lion's algorithm: A new nature inspired search algorithm. Procedia Technology 2nd International Conference on Communication Computing Security. 6: 126–135. https://doi.org/10.1016/j.protcy.2012.10.016

  26. George, A., & Rajakumar, B. R. (2013). Fuzzy Aided Ant Colony Optimization Algorithm to solve Optimization Problem. Intelligent Informatics, Advances in Intelligent Systems and Computing, 182, 207–215. https://doi.org/10.1007/978-3-642-32063-7_23.

    Article  Google Scholar 

  27. Giridhar Reddy, B. & Sai Ambati, L. (2020) A novel framework for crop pests and disease identification using social media. MWAIS 2020 Proceedings. 9.

  28. Ambati L.S., Narukonda, K., Bojja, G.R. and Bishop, D., (2020) Factors influencing the adoption of artificial intelligence in organizations—from an employee’s perspective (2020). MWAIS Proceedings 20.

  29. Agnoletti, M., Conti, L., Frezza, L., Monti, M., & Santoro, A. (2015). Features analysis of dry stone walls of Tuscany (Italy). Sustainability, 7(10), 13887–13903.

    Article  Google Scholar 

  30. Conti, L., Bartolozzi, S., Racanelli, V., Sorbettiguerri, F., & Iacobelli, S. (2018). Alarm guard systems for the prevention of damage produced by ungulates in a chestnut grove of Middle Italy. Agronomy Research, 16(3), 679–687.

    Google Scholar 

  31. Liang Liu, Zuchao Ma, Weizhi Meng (1989) Detection of multiple-mix-attack malicious nodes using perceptron-based trust in IoT networks”, Future generation computer systems, vol. 101, pp. 865–879, 2019M. Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science.

  32. Baig, Z. A., Sanguanpong, S., Naeem Firdous, S., Nhan Vo, V., & So-In, C. (2020). Averaged dependence estimators for DoS attack detection in IoT networks. Future Generation Computer Systems, 102, 198–209.

    Article  Google Scholar 

  33. Huy-Trung Nguyen., Quoc-Dung Ngo., Doan-Hieu Nguyen., Van-Hoang Le (2020) PSI-rooted subgraph: A novel feature for IoT botnet detection using classifier algorithms, ICT Express, In press, corrected proof, Available online 7.

  34. Hasan, M., Islam, M., Zarif, I., & Hashem, M. M. A. (2019). Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches". Internet of Things, 7, 100059.

    Article  Google Scholar 

  35. Ho, J. (2018). Efficient and robust detection of code-reuse attacks through probabilistic packet inspection in industrial IoT devices. IEEE Access, 6, 54343–54354.

    Article  Google Scholar 

  36. Murali, S., & Jamalipour, A. (2020). A lightweight intrusion detection for sybil attack under mobile RPL in the Internet of Things. IEEE Internet of Things Journal, 7(1), 379–388.

    Article  Google Scholar 

  37. Shailendra Rathore, J., & Park, H. (2018). Semi-supervised learning based distributed attack detection framework for IoT. Applied Soft Computing, 72, 79–89.

    Article  Google Scholar 

  38. https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT#.

  39. Arul, V. H., Sivakumar, V. G., Marimuthu, R., & Chakraborty, B. (2019). An approach for speech enhancement using deep convolutional neural network. Multimedia Research (MR), 2(1), 37–44.

    Google Scholar 

  40. Raviraj Vishwambhar, D., & Ashwinikumar Panjabrao, D. (2019). Emotion recognition from speech signals using DCNN with hybrid GA-GWO algorithm. Multimedia Research, 2(4), 12–22.

    Google Scholar 

  41. https://archive.ics.uci.edu/ml/datasets/detection_of_IoT_botnet_attacks_N_BaIoT#

  42. Li, Y., Yingying, Xu., Liu, Z., Hou, H., & Cui, L. (2020). Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement, 154, 15.

    Google Scholar 

  43. Pijarski, P., & Kacejko, P. (2019). A new metaheuristic optimization method: the algorithm of the innovative gunner (AIG). Engineering Optimization, 51(12), 2049–2068.

    Article  MathSciNet  Google Scholar 

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  1. UST, Europe, United Kingdom

    Subramonian Krishna Sarma

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Sarma, S.K. Optimally Configured Deep Convolutional Neural Network for Attack Detection in Internet of Things: Impact of Algorithm of the Innovative Gunner. Wireless Pers Commun 118, 239–260 (2021). https://doi.org/10.1007/s11277-020-08011-9

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