Skip to main content

Using Wireless Sensor to Acquire Live Data on a SCADA System, Towards Monitoring File Integrity

  • Chapter
  • First Online:
Dynamic Wireless Sensor Networks

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 165))

Abstract

SCADA systems are network presence systems that face significant threats and attacks. After an attack occurred, SCADA requires forensic investigation to understand the cause and effects of the intrusion or disruption on the systems services. However, forensic investigators cannot turn it off during acquiring the live data that is required for the investigation and analysis process. That is because the systems services need to be continuously operational. Despite the great efforts to acquire live data on SCADA systems, the continuously change of this type of data and the risk on the systems services make it a big challenge. In this proposal, we suggest a new method to acquire live data on a SCADA system using wireless sensor network. The proposed idea attempts to monitor file integrity and acquire live data in a way that minimizes risk to the systems services. In addition, it aims to help Forensic investigators by guarantee early data acquisition after incident and digital evidence validity as well.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Ahmed, I., Obermeier, S., Naedele, M., & Richard, G. (2012). Scada systems: Challenges for forensic investigators. Computer, 45(12), 44–51.

    Article  Google Scholar 

  2. Elhoseny, M., Hosny, A., Hassanien, A. E., Muhammad, K., & Sangaiah, A. K. (2017). Secure automated forensic investigation for sustainable critical infrastructures compliant with green computing requirements. IEEE Transactions on Sustainable Computing, PP(99). https://doi.org/10.1109/TSUSC.2017.2782737.

  3. Elhoseny, M., Abdelaziz, A., Salama, A. S., Riad, A. M., Muhammad, K., & Sangaiah, A. K. (2018). A hybrid model of internet of things and cloud computing to manage big data in health services applications. Future Generation Computer Systems. Elsevier. (in Press).

    Google Scholar 

  4. Abdelaziz, A., Elhoseny, M., Salama, A. S., & Riad, A. M. (2018). A machine learning model for improving healthcare services on cloud computing environment. Measurement, 119, 117–128. https://doi.org/10.1016/j.measurement.2018.01.022.

    Article  Google Scholar 

  5. Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2017). The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: Opportunities, challenges, and open problems. Journal of Ambient Intelligence and Humanized Computing. Springer. https://doi.org/10.1007/s12652-017-0659-1.

  6. Yuan, X., Li, D., Mohapatra, D., & Elhoseny, M. (2017). Automatic removal of complex shadows from indoor videos using transfer learning and dynamic thresholding. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.12.026. (in Press).

  7. Sajjad, M., Nasir, M., Muhammad, K., Khan, S., Jan, Z., Sangaiah, A. K., Elhoseny, M., & Baik, S. W. (2017). Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities. Future Generation Computer Systems. Elsevier. https://doi.org/10.1016/j.future.2017.11.013.

  8. Shehab, A., Elhoseny, M., El Aziz, M. A., & Hassanien A. E. (2018). Efficient schemes for playout latency reduction in P2P-VoD systems. In A. Hassanien, & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9_22.

  9. Elhoseny, M., Nabil, A., Hassanien, A. E., & Oliva, D. (2018). Hybrid rough neural network model for signature recognition. In A. Hassanien, & D. Oliva (Eds.) Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9_14.

  10. Abdeldaim A. M., Sahlol A. T., Elhoseny M., & Hassanien A. E. (2018). Computer-aided acute lymphoblastic Leukemia diagnosis system based on image analysis. In: A. Hassanien, D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9.

  11. Abbas, H. (2014). Future SCADA challenges and the promising solution: The agent-based SCADA. International Journal of Critical Infrastructures, 10(3/4), 307–333.

    Article  Google Scholar 

  12. Elhoseny, M., Ramírez-González, G., Abu-Elnasr, O. M., Shawkat, S. A., Arunkumar, N., & Farouk, A. (2018). Secure medical data transmission model for IoT-based healthcare systems. IEEE Access, PP(99). https://doi.org/10.1109/ACCESS.2018.2817615.

  13. Shehab, A., Elhoseny, M., Muhammad, K., Sangaiah, A. K., Yang, P., Huang, H., & Hou, G. (2018). Secure and robust fragile watermarking scheme for medical images. IEEE Access, 6(1), 10269–10278. https://doi.org/10.1109/ACCESS.2018.2799240.

    Article  Google Scholar 

  14. Farouk, A., Batle, J., Elhoseny, M., Naseri, M., Lone, M., Fedorov, A., Alkhambashi, M., Ahmed, S. H., & Abdel-Aty, M. (2018). Robust general N user authentication scheme in a centralized quantum communication network via generalized GHZ states, Frontiers of Physics, 13, 130306. Springer. https://doi.org/10.1007/s11467-017-0717-3.

  15. Elhoseny, M., Elkhateb, A., Sahlol, A., & Hassanien, A. E. (2018). Multimodal biometric personal identification and verification. In A. Hassanien, & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in Computational Intelligence, Vol. 730. Cham: Springer. https://doi.org/10.1007/978-3-319-63754-9_12.

  16. Elhoseny, M., Essa, E., Elkhateb, A., Hassanien, A. E., & Hamad, A. (2018). Cascade multimodal biometric system using fingerprint and Iris patterns. In A. Hassanien, K. Shaalan, T. Gaber, & M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_55.

  17. Wu, T., Disso, J. F. P., Jones, K., & Campos, A. (2013). Towards a SCADA forensics architecture. In Proceedings of the 1st international symposium for ICS and SCADA cyber security research, (pp. 12–21).

    Google Scholar 

  18. Spyridopoulos, T., Tryfonas, T., & May, J. (2014). Incident analysis & digital forensics in SCADA and industrial control systems. In 8th IET international system safety conference incorporating the cyber security. IEEE.

    Google Scholar 

  19. Pedro, N. (2013). SCADA live forensics: real time data acquisition process to detect, prevent, or evaluate critical situations. In 1st annual international interdisciplinary conference, (pp. 24–26).

    Google Scholar 

  20. Tharwat, A., Mahdi, H., Elhoseny, M., & Hassanien, A. E. (2018). Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm. Expert Systems With Applications. https://doi.org/10.1016/j.eswa.2018.04.017. Accessed 12 April 2018.

    Article  Google Scholar 

  21. Tharwat, A., Elhoseny, M., Hassanien, A. E., Gabel, T., & Kumar, A. (2018). Intelligent Bezir curve-based path planning model using chaotic particle swarm optimization algorithm. Cluster Computing, (pp. 1–22). Springer. https://doi.org/10.1007/s10586-018-2360-3.

  22. Sarvaghad-Moghaddam, M., Orouji, A. A., Ramezani, Z., Elhoseny, M., & Farouk, A. (2018). Modelling the spice parameters of SOI MOSFET using a combinational algorithm. Cluster Computing. Springer. https://doi.org/10.1007/s10586-018-2289-6. (in Press).

  23. Rizk-Allah, R. M., Hassanien, A. E., & Elhoseny, M. (2018). A multi-objective transportation model under neutrosophic environment. Computers and Electrical Engineering. Elsevier. https://doi.org/10.1016/j.compeleceng.2018.02.024. (in Press).

  24. Batle, J., Naseri, M., Ghoranneviss, M., Farouk, A., Alkhambashi, M., & Elhoseny, M. (2017). Shareability of correlations in multiqubit states: Optimization of nonlocal monogamy inequalities. Physical Review A, 95(3), 032123. https://doi.org/10.1103/PhysRevA.95.032123.

  25. El Aziz, M. A., Hemdan, A. M., Ewees, A. A., Elhoseny, M., Shehab, A., Hassanien, A. E., & Xiong, S. (2017). Prediction of biochar yield using adaptive neuro-fuzzy inference system with particle swarm optimization. In IEEE PES PowerAfrica conference, (pp. 115–120), June 27–30, 2017. Accra-Ghana: IEEE. https://doi.org/10.1109/PowerAfrica.2017.7991209.

  26. Ewees, A. A., El Aziz, M. A., & Elhoseny, M. (2017) Social-spider optimization algorithm for improving ANFIS to predict biochar yield. In 8th International conference on computing, communication and networking technologies (8ICCCNT), July 3–5. Delhi-India: IEEE.

    Google Scholar 

  27. Metawa, N., Elhoseny, M., Hassan, M. K., & Hassanien, A. E. (2016). Loan portfolio optimization using genetic algorithm: A case of credit constraints. In Proceedings of 12th international computer engineering conference (ICENCO), (pp. 59–64). IEEE. https://doi.org/10.1109/ICENCO.2016.7856446.

  28. Elhoseny, M., Farouk, A., Batle, J., Shehab, A., & Hassanien, A. E. (2017). Secure image processing and transmission schema in cluster-based wireless sensor network. In Handbook of research on machine learning innovations and trends, Chapter 45, pp. 1022–1040, IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2229-4.ch045.

  29. Elhoseny, M., Elleithy, K., Elminir, H., Yuan, X., & Riad, A. (2015). Dynamic clustering of heterogeneous wireless sensor networks using a genetic algorithm towards balancing energy exhaustion. International Journal of Scientific & Engineering Research, 6(8), 1243–1252.

    Google Scholar 

  30. Yuan, X., Elhoseny, M., El-Minir, H., & Riad, A. (2017). A genetic algorithm-based, dynamic clustering method towards improved wsn longevity. Journal of Network and Systems Management, 25(1), 21–46.

    Article  Google Scholar 

  31. Elhoseny, M., Yuan, X., Yu, Z., Mao, C., El-Minir, H., & Riad, A. (2015). Balancing energy consumption in heterogeneous wireless sensor networks using genetic algorithm. IEEE Communications Letters, 19(12), 2194–2197. IEEE. https://doi.org/10.1109/LCOMM.2014.2381226.

    Article  Google Scholar 

  32. Elhoseny, M., Tharwat, A., Yuan, X., & Hassanien, A. E. (2018). Optimizing K-coverage of mobile WSNs. Expert Systems with Applications, 92, 142–153. Elsevier. https://doi.org/10.1016/j.eswa.2017.09.008.

    Article  Google Scholar 

  33. Elhoseny, M., Tharwat, A., Farouk, A., & Hassanien, A. E. (2017). K-coverage model based on genetic algorithm to extend WSN lifetime. IEEE Sensors Letters, 1(4), 1–4. IEEE. https://doi.org/10.1109/LSENS.2017.2724846.

    Article  Google Scholar 

  34. Elhoseny, M., Farouk, A., Zhou, N., Wang, M. M., Abdalla, S., & Batle, J. (2017). Dynamic multi-hop clustering in a wireless sensor network: Performance improvement. Wireless Personal Communications, 95(4), 3733–3753. Springer. https://doi.org/10.1007/s11277-017-4023-8.

    Article  Google Scholar 

  35. Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. (2014). Extending self-organizing network availability using genetic algorithm. In International Conference on Computing, Communication and Networking Technologies (ICCCNT), (pp. 1–6). IEEE.

    Google Scholar 

  36. Yuan, X., Elhoseny, M., El-Minir, H. K., & Riad, A. M. (2017). A genetic algorithm-based, dynamic clustering method towards improved WSN longevity. Journal of Network and Systems Management, 25(1), 21–46. Springer. https://doi.org/10.1007/s10922-016-9379-7.

    Article  Google Scholar 

  37. Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. M. (2016b). An energy efficient encryption method for secure dynamic WSN. Security and Communication Networks, 9(13), 2024–2031.

    Google Scholar 

  38. Elhoseny, M., Elminir, H., Riad, A., & Yuan, X. (2016a). A secure data routing schema for WSN using elliptic curve cryptography and homomorphic encryption. Journal of King Saud University-Computer and Information Sciences, 28(3), 262–275.

    Article  Google Scholar 

  39. Elsayed, W., Elhoseny, M., Riad, A., & Hassanien, A. E. (2017). Autonomic self-healing approach to eliminate hardware faults in wireless sensor networks. In International conference on advanced intelligent systems and informatics, (pp. 151–160). Springer.

    Google Scholar 

  40. Elsayed, W., Elhoseny, M., Sabbeh, S., & Riad, A. (2017). Self-maintenance model for wireless sensor networks. Computers and Electrical Engineering. https://doi.org/10.1016/j.compeleceng.2017.12.022. (in Press).

  41. Elhoseny, M., Yuan, X., El-Minir, H. K., & Riad, A. M. (2016). An energy efficient encryption method for secure dynamic WSN. Security and Communication Networks, 9(13) 2024–2031. https://doi.org/10.1002/sec.1459.

  42. Hosseinabadi, A. A. R., Vahidi, J., Saemi, B., Sangaiah, A. K., & Elhoseny, M. (2018). Extended genetic algorithm for solving open-shop scheduling problem. Soft Computing. https://doi.org/10.1007/s00500-018-3177-y.

  43. Abbas, H. A. (2014). Exploiting the overlapping of higher order: Entities within multi-agent systems. International Journal of Agent Technologies and Systems (IJATS), 6(3), 32–57.

    Article  Google Scholar 

  44. Abbas, H. A. (2015). Realizing the NOSHAPE MAS Organizational model: An operational view. International Journal of Agent Technologies and Systems (IJATS), 7(2), 75–104.

    Article  Google Scholar 

  45. Abbas, H. A., Shaheen, S. I., & Amin, M. H. (2016). Self-adaptive large-scale SCADA system based on self-organised multi-agent systems. International Journal of Automation and Control, 10(3), 234266.

    Article  Google Scholar 

  46. Bellifemine, F., Poggi, A., & Rimassa, G. (1999). JADE: A FIPA-compliant agent framework. In Proceedings of the practical applications of intelligent agents and multi-agents, (pp. 97–108).

    Google Scholar 

  47. Foundation for Intelligent Physical Agents (FIPA) (2000) FIPA Agent Management Specification. http://www.fipa.org/specs/fipa00023/.

  48. Moreno, A., Valls, A., & Viejo, A. (2003). Using JADE-LEAP to Implement Agents in Mobile Devices. http://jade.tilab.com/papers/EXP/02Moreno.pdf.

  49. Braubach, L., Pokahr, A., Bade, D., Krempels, K. H., & Lamersdorf, W. (2004). Deployment of distributed multi-agent systems. In International workshop on engineering societies in the agents world, (pp. 261–276). Heidelberg: Springer.

    Google Scholar 

  50. Saqib, A., Anwar, R. W., Hussain, O. K., Ahmad, M., Ngadi, M. A., Mohamad, M. M., Malki, Z. O. H. A. I. R., Noraini, C., Jnr, B. A., Nor, R. N. H. & Murad, M. A. A. (2015). Cyber security for cyber physcial systems: a trust-based approach. Journal of theoretical and applied information technology, 71(2).

    Google Scholar 

  51. Neuman, C., & Tan, K. (2011). Mediating cyber and physical threat propagation in secure smart grid architectures. IEEE International Conference on Smart Grid Communications, 17–20, 238243.

    Google Scholar 

  52. Elhoseny, H., Elhoseny, M., Riad, A. M., Hassanien, A. E. (2018). A framework for big data analysis in smart cities. In A. Hassanien, M. Tolba, M. Elhoseny, M. Mostafa (Eds.), AMLTA 2018 the international conference on advanced machine learning technologies and applications (AMLTA2018). Advances in Intelligent Systems and Computing, Vol. 723. Cham: Springer. https://doi.org/10.1007/978-3-319-74690-6_40.

    Chapter  Google Scholar 

  53. Elhoseny M., Shehab A., & Osman L. (2018) An empirical analysis of user behavior for P2P IPTV workloads. In A. Hassanien, M. Tolba, M. Elhoseny, & M. Mostafa (Eds.) AMLTA 2018 The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). Advances in Intelligent Systems and Computing, Vol. 723. Cham: Springer https://doi.org/10.1007/978-3-319-74690-6_25.

    Chapter  Google Scholar 

  54. Wang, M. M., Qu, Z. G., Elhoseny, M. (2017). Quantum secret sharing in noisy environment. In X. Sun, H. C. Chao, X. You, & E. Bertino (Eds.) Cloud computing and security, ICCCS 2017. Lecture Notes in Computer Science, Vol. 10603. Cham: Springer. https://doi.org/10.1007/978-3-319-68542-7_9.

    Chapter  Google Scholar 

  55. Elsayed, W., Elhoseny, M., Riad, A. M., & Hassanien, A. E. (2018). Autonomic self-healing approach to eliminate hardware faults in wireless sensor networks. In A. Hassanien, K. Shaalan, T. Gaber, & M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_14.

  56. Abdelaziz, A., Elhoseny, M., Salama, A. S., Riad, A. M., & Hassanien, A. E. (2018). Intelligent algorithms for optimal selection of virtual machine in cloud environment, towards enhance healthcare services. In A. Hassanien, K. Shaalan, T. Gaber, & M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_27.

  57. Shehab, A., Ismail, A., Osman, L., Elhoseny, M., El-Henawy, I. M. (2018). Quantified self using IoT wearable devices. In A. Hassanien, K. Shaalan, T. Gaber, M. Tolba (Eds.), Proceedings of the international conference on advanced intelligent systems and informatics 2017, AISI 2017. Advances in Intelligent Systems and Computing, Vol. 639. Cham: Springer. https://doi.org/10.1007/978-3-319-64861-3_77.

  58. Weyns, D., Helleboogh, A., & Holvoet, T. (2009). How to get multi-agent systems accepted in industry? International Journal of Agent-Oriented Software Engineering, 3(4), 383–390.

    Article  Google Scholar 

  59. Foundation For Intelligent Physical Agents (1997), Agent Communication Language, FIPA 97 Specification Part 2.

    Google Scholar 

  60. Annamalai, M., & Sterling, L. (2003). Guidelines for constructing reusable domain ontologies. In OAS, (pp. 71–74).

    Google Scholar 

  61. Ahmed, I., Obermeier, S., Naedele, M., & Richard III, G. G. (2012). SCADA systems: Challenges for forensic investigators. Computer, 45(12), 44–51.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohamed Elhoseny .

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Elhoseny, M., Hassanien, A.E. (2019). Using Wireless Sensor to Acquire Live Data on a SCADA System, Towards Monitoring File Integrity. In: Dynamic Wireless Sensor Networks. Studies in Systems, Decision and Control, vol 165. Springer, Cham. https://doi.org/10.1007/978-3-319-92807-4_8

Download citation

Publish with us

Policies and ethics