Research Article Open Access

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

Manjula M1, Venkatesh1 and Venugopal K. R.1
  • 1 Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bengaluru, India


Recent advancements in e-business, e-healthcare, e-governance, and online digital transactions have brought valuable benefits. Unfortunately, it raises severe cyber-attacks. Cyberattacks disrupt normal operations, try to retrieve confidential information and defense secrets, and subvert the nation’s defense systems and Internet-connected devices. Cyber security solutions are required to detect, analyze, defend against threats and protect sensitive data from unauthorized access. This study gives a detailed survey of different cybersecurity attacks, like Denial-of-service attacks, Botnet Evasion Attacks, Malware invasions, Spam and phishing invasion, Spoofing, Domain Generation algorithms, Probing attacks, R2L, and U2R attacks. This research review emphasizes Machine Learning and Deep Learning-based approaches to Cybersecurity problems. This study’s key highlights are the research challenges, cybersecurity issues, cyber security domains, and tools for the Intrusion detection system. Data sets play a vital role in cybersecurity research; hence, Private and Publically available datasets are reviewed in this study. Various performance matrices are discussed in this survey which can be used to evaluate the effectiveness of cybersecurity solutions.

Journal of Computer Science
Volume 19 No. 1, 2023, 20-56


Submitted On: 16 September 2022 Published On: 4 January 2023

How to Cite: M, M., Venkatesh, . & R., V. K. (2023). Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey. Journal of Computer Science, 19(1), 20-56.

  • 3 Citations



  • Cyber Security Threats
  • Cyber Security
  • Machine Learning (ML)
  • Deep Feature Learning (DL)
  • Botnet Attacks
  • Malware Attacks
  • Evasion Attack
  • Adversarial Machine Learning Algorithms