@article{55,
  author = {Dipankar Dasgupta and Zahid Akhtar and Sajib Sen},
  title = {Machine learning in cybersecurity: a comprehensive survey},
  abstract = {we provide a comprehensive survey of the works that have been carried
out most recently (from 2013 to 2018) on ML in cybersecurity, describing the basics of cyber-attacks and corresponding
defenses, the basics of the most commonly used ML algorithms, and proposed ML and data mining schemes for cybersecurity in terms of features, dimensionality reduction, and classification/detection techniques. In this context, this article
also provides an overview of adversarial ML, including the security characteristics of deep learning methods. Finally, open
issues and challenges in cybersecurity are highlighted and potential future research directions are discussed.},
  year = {2020},
  journal = {SAGE Publications},
  month = {2020-09-19},
  issn = {1548-5129},
  url = {https://journals.sagepub.com/doi/abs/10.1177/1548512920951275},
  doi = {10.1177/1548512920951275},
  note = {Because of digital omni-connectivity and the ubiquitous
presence of small (e.g., smart-watches) to large computing
devices (e.g., smart metering systems), an enormous
amount of data scaled from public to classified by individuals and government organizations are being produced,processed, stored, and traded throughout cyber-enabled networks. Therefore, securing the data and cyber networks has become of paramount importance for small-to-large organizations as well as from individuals to nations.
Nowadays, the use of ML to secure cyber-space has shown
great improvement by ensuring the robustness of a network
as well as maintaining the integrity of the data. On the
other hand, attackers have also figured out the adversarial
way of using ML to twist the performance of cybersecurity
measures, for example, the malware detection mechanism,
IDS, cyber identity detection, etc.},
}