Methodical Applications For Cybersecurity Using Deep Learning Techniques


  • Geetanjali R , Dr. S Jagannatha , Dr. Niranjanamurthy M , Dr. Dayananda.P


The research and advances in the field of Machine Learning (ML) have produ
algorithms and technologies for improving security solutions that help in identifying and
decisively dealing with security threats. On the contrary, the brighter side of all these
advancements is making it possible for cybercriminals to use the very same knowledge in
crafting and launching bigger and more sophisticated cyber-attacks. This research paper
focuses mostly on the literature survey of Machine Learning (ML) and data learning
techniques for cybersecurity. Machine Learning is performing a task with the given data as
input to make its performance progressively better over time. Some of the Machine
Learning and Deep Learning (DL) methods are explained and how they are relevant in the
field of Cyber Security. With increasing digitization globally, security concerns are also
growing at an alarming rate. The need for dynamic and progressed security technologies
and procedures to counter the complicated nature of cyber-attacks becomes imperative.
This paper is intended to sensitize researchers desiring to begin their work in the field of
ML or DL, and Cyber Security. Some references also have been made by citing distinct
works and some valuable examples are provided as to how cyber problems are often
tackled by ML. This paper specifically discusses both defensive and offensive behavior on
cyber-attacks targeted at ML models. Finally, applications like malicious code, behavior of
network traffic, malware system call sequence, and intrusion detection are also discussed.