Auto Encoders based Model to Predict Data Breaches in Cloud Computing


  • Muzzamil Mustafa Student
  • Muhammad Zunnurain Hussain Assistant Professor, Department of Computer Science Baharia University Lahore Campus
  • Muhammad Zukifl Hasan Senior Lecturer, Department of CS UCP, Lahore
  • Nadeem Sarwar Assistant Professor, Department of CS Baharia University Lahore Campus
  • Waqar Ashiq Lecturer Department of SE UMT Lahore
  • Basit Sattar Lecturer Department of AI UMT, Lahore Pakistan


We are living in the world of the Internet, due to the increase in the use of the Internet our data is also mostly stored using online storage like cloud storage. Due to the increased use of Cloud Computing, cyber-attacks on cloud storage also increased which is causing data breaches. To mitigate these breaches we need to develop AI-based methods/techniques that can predict attacks for data breaches. Auto Encoders is a Neural network-based technique that can help us in the early prediction of cyber-attacks on cloud storage for breaching of data. Auto Encoders depend upon two functions one is an encoding function that changes the data inputs and a decoding function that recreates the input data from the encoded representation. In this paper, we have proposed an Auto Encoders-based ANN model that can early predict data breaches to save data on cloud storage. We have tested our model on neural network fitting and obtained the accuracy of 100%.




How to Cite

Mustafa, M., Hussain, M. Z., Hasan, M. Z., Sarwar, N., Ashiq, W., & Sattar, B. (2024). Auto Encoders based Model to Predict Data Breaches in Cloud Computing. Journal of Computers and Intelligent Systems, 1(1). Retrieved from

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