TY - GEN
T1 - Advanced Machine Learning Approach with Dynamic Analysis to Detect Malware in Cybersecurity Domain
AU - Gupta, Shubhang
AU - Khan, Shamim
AU - Yang, Tiansheng
AU - Rathore, Rajkumar Singh
AU - Das, Aniket
AU - Mishra, Nilamadhab
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/7/3
Y1 - 2025/7/3
N2 - This paper is about detecting malware using machine learning. As the fact that complex computer attacks are increasing rapidly, suggests that relying on old methods and techniques to identify them are insufficient. So to deal with this situation, machine learning has emerged as the most efficient approach in identification of malware. This paper starts with a brief introduction of different varieties of malware and how the danger is constantly evolving. Then, it explains basic principles and operations of ML models and how it learn from the existing data and mistakes. It discuss about several challenges associated with the use of ML technology in searching for malware. Finally, it talks about what could be done in the future to make it even better at detecting malware with the help of machine learning. This paper will also help other researchers, people who work in cyber security, and anyone else who wants to know more about using ML to look for malware.
AB - This paper is about detecting malware using machine learning. As the fact that complex computer attacks are increasing rapidly, suggests that relying on old methods and techniques to identify them are insufficient. So to deal with this situation, machine learning has emerged as the most efficient approach in identification of malware. This paper starts with a brief introduction of different varieties of malware and how the danger is constantly evolving. Then, it explains basic principles and operations of ML models and how it learn from the existing data and mistakes. It discuss about several challenges associated with the use of ML technology in searching for malware. Finally, it talks about what could be done in the future to make it even better at detecting malware with the help of machine learning. This paper will also help other researchers, people who work in cyber security, and anyone else who wants to know more about using ML to look for malware.
KW - Challenges in malware detection
KW - Machine learning
KW - Malware
KW - Malware detection using ML
UR - https://www.scopus.com/pages/publications/105010595977
U2 - 10.1007/978-981-96-3250-3_38
DO - 10.1007/978-981-96-3250-3_38
M3 - Conference contribution
AN - SCOPUS:105010595977
SN - 9789819632497
T3 - Lecture Notes in Networks and Systems
SP - 495
EP - 502
BT - Proceedings of 4th International Conference on Computing and Communication Networks, ICCCN 2024
A2 - Kumar, Akshi
A2 - Swaroop, Abhishek
A2 - Shukla, Pancham
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Computing and Communication Networks, ICCCN 2024
Y2 - 17 October 2024 through 18 October 2024
ER -