Leveraging AI and Machine
Leveraging AI and Machine
Leveraging AI and Machine1. Critically analyze the findings of the research paper.
2. What type of ransomware the authors have explained?
3. Do you think the author’s claims are true?
4. What is your view of the paper?
Length: Between 675 to 800 words excluding references.
ScienceDirect
Available online at www.sciencedirect.com
Procedia Computer Science 168 (2020) 289–296
1877-0509 © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges 10.1016/j.procs.2020.02.249
10.1016/j.procs.2020.02.249 1877-0509
© 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
Corresponding author email: abdullahi.arabo@uwe.ac.uk
1877-0509 © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for
Societal Challenges
Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges, CAS 2019
Detecting Ransomware Using Process Behavior Analysis
Abdullahi Arabo 1,*, Remi Dijoux 1,2, Timothee Poulain 1,2, Gregoire Chevalier 1,2 1Computer Science Research centre, The University of the West of England, CSRC, Bristol, UK, BS10 5PD *abdullahi.arabo@uwe.ac.uk
2Institue Universitaire de Technologie De La Reunion, France
Abstract: Ransomware attacks are one of the biggest and attractive threats in cyber security today. Anti-virus software’s are often inefficient against zero-day malware and ransomware attacks, important network infections could result in a large amount of data loss. Such attacks are also becoming more dynamic and able to change their signatures – hence creating an arms race situation. This study investigates the relationship between a process behavior and its nature, in order to determine whether it is ransomware or not. The paper aim is to see if using this method will help the evading malicious software’s and use as a self-defense mechanism using machine learning that emulates the human immune system. The analysis was conducted on 7 ransomware, 41 benign software, and 34 malware samples. The results show that we are able to distinguish between ransomware and benign applications, with a low false-positive and false-negative rate.
© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Complex Adaptive Systems Conference with Theme: Leveraging AI and Machine Learning for Societal Challenges
Keywords: Ransomware, malware, cyber security, machine learning
