Construing Attacks of Internet of Things (IoT) and A Prehensile Intrusion Detection System for Anomaly Detection Using Deep Learning Approach

  • Marjia Akter
  • Gowrab Das Dip
  • Moumita Sharmin Mira
  • Md. Abdul Hamid
  • M. F. MridhaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1059)


An abundance of physical instruments around a group of countries which are now associated to the hyperspace, collecting or sharing data known as the internet of things (IOT). As the statistic of IoT devices increases, new security and privacy dare will be confronted for both home and office devices. An intrusion detection system (IDS) helps to detect the malicious system to get notified when any malicious flurry or anomaly occurred in the system. In this paper, we dispute four types of attacks of IoT ambiance. We have proposed such a model that recuperates from attacks like DoS (Denial of Services), DDoS (Distributed Denial of Services), R2L (Remote 2 Local), U2R (User to Root), and probe attack. Our model mainly focused on the security of home-based appliances like air-condition, fan, light, television, oven, refrigerator, printer, heater, washing machine, geysers, electric stove, and others electronic devices. We have developed an algorithm by using deep learning approach to dispute attacks and give security to the user. Deep learning is divergent from regular machine learning approach which has self-taught techniques (STL) that represents data such as images, video or text, without using human domain knowledge. They have more ductile architectures that comprehend from raw data and can increase their accuracy level when acquires more data. Our model analyses six features a server to identify whether it is malicious or not. Self-taught technique of deep learning has been approached in our paper. We have used NSL-KDD dataset for training and testing.


Internet of Things Intrusion detection Attacks Deep learning 



This paper is supported by The Institute for Energy, Environment, Research and Development (IEERD), University of Asia Pacific (UAP).


  1. 1.
    Sharma A, Singh A (2017) Review on Internet of Things attacks and their countermeasure using lightweight cipher algorithms. Int J Adv Res Comput Sci 8(5), May–June 2017Google Scholar
  2. 2.
    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions (30-01-2013)CrossRefGoogle Scholar
  3. 3.
    Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks (07-11-2017)Google Scholar
  4. 4.
    Singh O, Singh J, Singh R () An intelligent intrusion detection and prevention system for safeguard mobile adhoc networks against malicious nodes. Indian J Sci Technol 10(14)., April 2017Google Scholar
  5. 5.
    Lee B, Amaresh S, Green C, Engels D Comparative study of deep learning models for network intrusion detection. SMU Data Sci Rev 1(1), Article 8Google Scholar
  6. 6.
    Punia A, Vatsa VR (2017) Current trends and approaches of network intrusion detection system. JCSMC 6(6):266–270Google Scholar
  7. 7.
    Liao H-J, Lin C-HR, Lin Y-C, Tung K-Y (2012) Intrusion detection system: a comprehensive review (27-08-2012)Google Scholar
  8. 8.
    Mridha MF, Hamid MA, Asaduzzaman M (2017) Issues of Internet of Things (IoT) and an intrusion detection system for IoT using machine learning paradigmGoogle Scholar
  9. 9.
    Niyaz Q, Sun W, Javaid AY, Alam M (2016) A deep learning approach for network intrusion detection system (24-05-2016)Google Scholar
  10. 10.
    Ponkarthika M, Saraswathy VR (2018) Network intrusion detection using deep neural networks (28-04-2018)Google Scholar
  11. 11.
    Glass-Vanderlan TR, Iannacone MD (2018) A survey of intrusion detection systems leveraging host data (18-05-2018)Google Scholar
  12. 12.
    Elsherif A (2018) Automatic intrusion detection system using deep recurrent neural network paradigmGoogle Scholar
  13. 13.
    Raafat HM, Shamim Hossain M, Essa E, Elmougy S, Tolba AS, Muhammad G, Ghoneim A (2017) Fog intelligence for real-time IoT sensor data analytics (25-09-2017)CrossRefGoogle Scholar
  14. 14.
    Abdul Hamid M, Abdullah-Al-Wadud M, Hassan MM, Almogren A, Alamri A, Kamal ARM, Mamun-Or-Rashid M (2017) A key distribution scheme for secure communication in acoustic sensor networks (10-06-2017)Google Scholar
  15. 15.
    Diro AA, Chilamkurti N (2017) Distributed attack detection scheme using deep learning approach for Internet of Things (23-07-2017)Google Scholar
  16. 16.
    Kim J, Kim J, Thu HLT, Kim H (2016) Long short term memory recurrent neural network classifier for intrusion detection (17-02-2016)Google Scholar
  17. 17.
    Hodo E, Bellekens X, Hamilton A, Dubouilh P-L, Iorkyase E, Tachtatzis C, Atkinson R (2016) Threat analysis of IoT networks using artificial neural network intrusion detection system (13-05-2016)Google Scholar
  18. 18.
    Diro AA, Chilamkurti N (2017) Distributed attack detection scheme using deep learning approach for Internet of Things (12-06-2017)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Marjia Akter
    • 1
  • Gowrab Das Dip
    • 1
  • Moumita Sharmin Mira
    • 1
  • Md. Abdul Hamid
    • 1
  • M. F. Mridha
    • 1
    Email author
  1. 1.Department of CSEUniversity of Asia PacificDhakaBangladesh

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