Learning Cyber ​​Security and Machine Engineering at the University


  • Rickho Rizky Karuniawan University of Raharja
  • Sugeng Santoso University of Raharja
  • Muhamad Al Fikri University of Raharja
  • M. Argadilah University of Raharja
  • Wisnu Ardi Pamungkas University of Raharja




Machine learning, Cyber Security, Machine Engineering, Learning


In this review, key literature reviews on network analysis and intrusion detection using machine learning (ML) and deep learning (DL) approaches are explained. It also provides a brief lesson description for each ML/DL procedure. This paper covers the datasets used in machine learning techniques, which are the main instruments for evaluating network traffic and detecting irregularities. Data holds a key role in ML/DL approaches. We also go into further detail about the problems with using ML/DL to cybersecurity and make suggestions for future research.




How to Cite

Karuniawan, R. R., Santoso, S., Fikri, M. A., Argadilah, M., & Pamungkas, W. A. (2022). Learning Cyber ​​Security and Machine Engineering at the University. Blockchain Frontier Technology, 3(1), 7–12. https://doi.org/10.34306/bfront.v3i1.242