Acta Scientific Computer Sciences

Review Article Volume 3 Issue 8

Intrusion Detection Using Deep Learning Techniques in the Cloud System: A Survey

Khalid Al Makdi1,2* and Frederick T Sheldon1

1Computer Science Department, University of Idaho, Moscow, USA
2Computer Science Department, Najran University, Najran, Saudi Arabia

*Corresponding Author: Khalid Al Makdi, Computer Science Department, University of Idaho, Moscow, USA.

Received: June 24, 2021; Published: July 13, 2021


The rapid growth of data and connectivity among computers has left the complex problem of information security. To protect data and the computer networks, numerous intrusion detection systems (IDS) have been developed that utilize machine learning (ML). However, many issues arise, especially since malicious attacks are constantly changing due to the huge volume of data stored in a distributed manner. This necessitates a scalable solution that incorporates effective feature extraction and a deep learning-based classification method. Due to the dynamic nature of malware and its continuously changing attack morphology, the malware signature datasets available publicly are updated systematically and benchmarked. This study presents a comprehensive review of IDS that uses deep learning and offers future research directions required to achieve a state-of-the-art IDS method: an objective with global security implications.

Keywords: Data Security; Deep Learning Technique; Intrusion Detection System; Cloud Database System


  1. Abid A and Jemili F. “Intrusion Detection based on Graph oriented Big Data Analytics”. Procedia Computer Science 176 (2020): 572-581.
  2. Alsafi HM., et al. “IDPS: An Integrated Intrusion Handling Model for Cloud” (2012).
  3. An X., et al. “Hypergraph clustering model-based association analysis of DDOS attacks in fog computing intrusion detection system”. EURASIP Journal on Wireless Communications and Networking 1 (2018): 1-9.
  4. Bai , et al. “Malware detection method based on the control-flow construct feature of software”. IET Information Security 8.1 (2014): 18-24.
  5. Bashah N., et al. “Hybrid intelligent intrusion detection system”. World Academy of Science, Engineering and Technology 11 (2005): 23-26.
  6. Bhuyan , et al. “Network Anomaly Detection: Methods, Systems and Tools”. IEEE Communications Surveys and Tutorials 16.1 (2014): 303-336.
  7. Bhuyan MH., et al. “Network Traffic Anomaly Detection and Prevention”. Computer Communications and Networks. Cham: Springer International Publishing (2017).
  8. Blanco-Filgueira , et al. “Deep Learning- Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications”. IEEE Internet of Things Journal 6.3 (2019): 5423-5431.
  9. Chandola V., et al. “Anomaly detection”. ACM Computing Surveys 3 (2009): 1-58.
  10. Chen J., et al. “Distributed deep learning model for intelligent video surveillance systems with edge computing”. IEEE Transactions on Industrial Informatics (2019).
  11. Corona , et al. “Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues”. Information Sciences 239 (2013): 201-225.
  12. da Costa K., et al. “Internet of Things: A survey on machine learning-based intrusion detection approaches”. Computer Networks 151 (2019): 147-157.
  13. Dey S., et al. “A machine learning based intrusion detection scheme for data fusion in mobile clouds involving heterogeneous client networks”. Information Fusion 49 (2019): 205-215.
  14. Ding , et al. “Application of deep belief networks for opcode based malware detection”. In: 2016 International Joint Conference on Neural Networks (IJCNN) IEEE (2016): 3901-3908.
  15. Diro AA and Chilamkurti “Distributed attack detection scheme using deep learning approach for Internet of Things”. Future Generation Computer Systems 82 (2018): 761-768.
  16. Dua S and Du X. “Data Mining and Machine Learning in Cybersecurity”. 1st Ed. Auerbach
  17. Elsaeidy , et al. “Intrusion detection in smart cities using Restricted Boltzmann Machines”. Journal of Network and Computer Applications 135 (2019): 76-83.
  18. Ghasempour “Internet of Things in Smart Grid: Architecture, Applications, Services, Key Technologies, and Challenges”. Inventions 4.1 (2019): 22.
  19. Hajimirzaei B and Navimipour NJ. “Intrusion detection for cloud computing using neural networks and artificial bee colony optimization algorithm”. ICT Express 5.1 (2019): 56-59.
  20. Hasan M., et al. “Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches”. Internet of Things 7 (2019): 100059.
  21. Hosseini Bamakan SM., et al. “An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization”. Neurocomputing 199 (2016): 90-102.
  22. Huang L., et al. “Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing”. Digital Communications and Networks 1 (2019): 10-17.
  23. Huda S., et al. “A malicious threat detection model for cloud assisted Internet of things (CoT) based industrial control system (ICS) networks using deep belief network”. Journal of Parallel and Distributed Computing 120 (2018): 23-31.
  24. Inoue D., et al. “Automated Malware Analysis System and Its Sandbox for Revealing Malware’s Internal and External Activities”. IEICE Transactions on Information and Systems (2018): E92-D (5) 945-954.
  25. Ishakian , et al. “Serving deep learning models in a serverless platform”. In: 2018 IEEE International Conference on Cloud Engineering (IC2E). IEEE (2018): 257-262.
  26. Jan , et al. “Toward a Lightweight Intrusion Detection System for the Internet of Things”. IEEE Access 7 (2019): 42450-42471.
  27. Jauro F., et al. “Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend”. Applied Soft Computing 96 (2020): 106582.
  28. Jian C., et al. “An Improved Chaotic Bat Swarm Scheduling Learning Model on Edge Computing”. IEEE Access 7 (2020): 58602-58610.
  29. Jiang F., et al. “Deep learning based multi-channel intelligent attack detection for data security”. IEEE transactions on Sustainable Computing (2018).
  30. Kang MJ and Kang JW. “Intrusion detection system using deep neural network for in-vehicle network security”. PloS one 11.6 (2016): e0155781.
  31. Karsligil E., et al. “Network intrusion detection using machine learning anomaly detection algorithms”. In: 25th Signal Processing and Communications Applications Conference (SIU). IEEE (2017): 1-4.
  32. Khan M., et al. “Deep Learning: Convergence to Big Data Analytics”. SpringerBriefs in Computer Singapore: Springer Singapore (2019).
  33. Kijsipongse E., et al. “A hybrid GPU cluster and volunteer computing platform for scalable deep learning”. The Journal of Supercomputing 74.7 (2018): 3236-3263.
  34. Kirda E., et al. “Behavior-based Spyware Detection”. In: Usenix Security Symposium 2006 (2006): 694.
  35. Kolias C., et al. “Intrusion Detection in 802.11 Networks: Empirical Evaluation of Threats and a Public Dataset”. IEEE Communications Surveys and Tutorials 18.1 (2016): 184-208.
  36. Li L., et al. “Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing”. IEEE Transactions on Industrial Informatics 10 (2018): 4665-4673.
  37. Li , et al. “Edge caching for D2D enabled hierarchical wireless networks with deep reinforcement learning”. Wireless Communications and Mobile Computing (2019).
  38. Liu C., et al. “A New Deep Learning-Based Food Recognition System for Dietary Assessment on An Edge Computing Service Infrastructure”. IEEE Transactions on Services Computing 11.2 (2018): 249-261.
  39. Liu G., et al. “Deep Learning-Based Channel Prediction for Edge Computing Networks Toward Intelligent Connected Vehicles”. IEEE Access 7 (2019): 114487-114495.
  40. Lo CC., et al. “A cooperative intrusion detection system framework for cloud computing networks”. In: 2010 39th International Conference on Parallel Processing Workshops IEEE (2018): 280-284.
  41. Maim LF., et al. “A self-adaptive deep learning- based system for anomaly detection in 5G networks”. IEEE Access 6 (2018): 7700-7712.
  42. Mehmood T and Rais HBM. “Machine learning algorithms in context of intrusion detection”. In: 3rd International Conference on Computer and Information Sciences (ICCOINS). IEEE (2016): 369-373.
  43. Meryem A and Ouahidi B EL. “Hybrid intrusion detection system using machine learning”. Network Security 5 (2020): 8-19.
  44. Moustafa , et al. “A holistic review of Network Anomaly Detection Systems: A comprehensive survey”. Journal of Network and Computer Applications 128 (2019): 33-55.
  45. Othman SM., et al. “Intrusion detection model using machine learning algorithm on Big Data environment”. Journal of Big Data 1 (2018): 34.
  46. Pang S., et al. “An Improved Convolutional Network Architecture Based on Residual Modeling for Person Re-Identification in Edge Computing”. IEEE Access 7 (2019): 106748-
  47. Priyadarshini R and Barik “A deep learning based intelligent framework to mitigate DDoS attack in fog environment”. Journal of King Saud University-Computer and Information Sciences (2019).
  48. Rahman MA., et al. “Scalable machine learning-based intrusion detection system for IoT-enabled smart cities”. Sustainable Cities and Society 61 (2020): 102324.
  49. Rehman ZU., et al. “Machine learning-assisted signature and heuristic-based detection of malwares in Android devices”. Computers and Electrical Engineering 69 (2018): 828-841.
  50. Saranya T., et al. “Performance Analysis of Machine Learning Algorithms in Intrusion Detection System: A Review”. Procedia Computer Science 171 (2020): 1251-1260.
  51. Singh R., et al. “An intrusion detection system using network traffic profiling and online sequential extreme learning machine”. Expert Systems with Applications 42.22 (2015): 8609-8624.
  52. Sohn I. “Deep belief network based intrusion detection techniques: A survey”. Expert Systems with Applications (2020): 114170.
  53. de Souza CA., et al. “Hybrid approach to intrusion detection in fog-based IoT environments”. Computer Networks 180 (2020): 107417.
  54. Subramanian N and Jeyaraj “Recent security challenges in cloud computing”. Computers and Electrical Engineering 71 (2018): 28-42.
  55. Vijayanand R., et al. “Intrusion detection system for wireless mesh network using multiple support vector machine classifiers with genetic-algorithm-based feature selection”. Computers and Security 77 (2018): 304-314.
  56. Wang H., et al. “An effective intrusion detection framework based on SVM with feature augmentation”. Knowledge-Based Systems 136 (2017): 130-139.
  57. Wani MA., et al. “Advances in deep learning”. Springer (2020).
  58. Watson , et al. “Malware Detection in Cloud Computing Infrastructures”. IEEE Transactions on Dependable and Secure Computing 13.2 (2016): 192-205.
  59. Wei P., et al. “An optimisation method for intrusion detection classification model based on deep belief network”. IEEE Access 7 (2019): 87593-87605.
  60. Yang , et al. “HIDS-DT: An effective hybrid intrusion detection system based on decision tree”. In: 2010 International Conference on Communications and Mobile Computing IEEE (2010): 70-75.
  61. Yang , et al. “Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks”. Applied Sciences 9.2 (2019): 23.
  62. Al Yaseen WL., et al. “Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system”. Expert Systems with Applications 67 (2017): 296-303.
  63. Yin C., et al. “A deep learning approach for intrusion detection using recurrent neural networks”. Ieee Access 5 (2017): 21954-21961.
  64. Zhang C and Zheng Z. “Task migration for mobile edge computing using deep reinforcement learning”. Future Generation Computer Systems 96 (2019): 111-118.
  65. Zhang H., et al. “A real-time and ubiquitous network attack detection based on deep belief network and support vector machine”. IEEE/CAA Journal of Automatica Sinica 7.3 (2020): 790-799.
  66. Zhang Z and Meddahi A. “Intrusion Prevention and Detection in NFV”. In: Security in Network Functions Virtualisation Elsevier (2017): 157-172.
  67. Zhao G., et al. “Intrusion detection using deep belief network and probabilistic neural network”. In: 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC). IEEE (2017): 639-642.
  68. Zhao R., et al. “Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing 115 (2019): 213-237.
  69. Zhou Y., et al. “An efficient intrusion detection system based on feature selection and ensemble classifier”. arXiv preprint arXiv:1904.01352 (2019).


Citation: Khalid Al Makdi and Frederick T Sheldon. “Intrusion Detection Using Deep Learning Techniques in the Cloud System: A Survey". Acta Scientific Computer Sciences 3.8 (2021): 17-31.


Copyright: © 2021 Khalid Al Makdi and Frederick T Sheldon. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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