Title:Exploring Deep Learning Approaches for Ransomware Detection: A Comprehensive Survey
Volume: 18
Issue: 2
Author(s): Prasanna Kumar Gurumallu*, Rajeshwari Dembala, Darshini Yoge Gowda, Ajay Kumara Makanahalli Annaiah, Manoj Kumar Muttyal Vasanth Kumar and Hardik Gohel
Affiliation:
- Cachetech Education and Research Foundation, Mysuru, Karnataka, India
Keywords:
Ransomware, CNN, RNN, GAN, malware, machine learning, deep learning.
Abstract: Ransomware, a form of malicious software originating from cryptovirology, poses a
serious threat by coercing victims to pay a ransom under the risk of exposing their data or permanently
restricting access. While basic ransomware may lock a system without causing harm
to files, more sophisticated variants utilize cryptoviral extortion techniques. The danger of ransomware
is significant, with ongoing discoveries of new strains and families on the internet
and dark web. Recovering from ransomware infections is challenging due to the complex encryption
schemes employed. The exploration of machine learning and deep learning methods
for ransomware detection is crucial, as these technologies can identify zero-day threats. This
survey delves into research contributions on the detection of ransomware using deep learning
algorithms. With deep learning gaining prominence in cybersecurity, we aimed to explore
techniques for ransomware detection, assess weaknesses in existing deep learning approaches,
and propose enhancements using those deep learning algorithms. Machine learning algorithms
can be employed to tackle worldwide computer security challenges, encompassing the detection
of malware, recognition of ransomware, detection of fraud, and identification of spoofing
attempts. Machine learning algorithms play a crucial role in assessing prevalent forms of cyber
security risks. They are instrumental in identifying and mitigating attacks, conducting vulnerability
scans, and evaluating the risks associated with the public internet. By leveraging machine
learning, computer defense mechanisms can effectively identify and respond to various cyber
threats. These techniques aid in fortifying systems against potential vulnerabilities and enhance
the overall security posture. Research in this field investigates the utilization of cyber training
in both defensive and offensive contexts, offering insights into the intersection of cyber threats
and machine learning techniques.
