Results 151 to 160 of about 2,609 (164)

Malware classification with recurrent networks

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2015
Attackers often create systems that automatically rewrite and reorder their malware to avoid detection. Typical machine learning approaches, which learn a classifier based on a handcrafted feature vector, are not sufficiently robust to such reorderings. We propose a different approach, which, similar to natural language modeling, learns the language of
Razvan Pascanu, Jack W. Stokes, Hermineh Sanossian, Mady Marinescu, Anil Thomas   +4 more
openaire   +1 more source

Malware classification using instruction frequencies

Proceedings of the 2011 ACM Symposium on Research in Applied Computation, 2011
Developing variants of malware is a common and effective method to avoid the signature detection of antivirus programs. Malware analysis and signature abstraction are essential technologies to update the detection signature DB for malware detection. Since most malware binary analysis processes are performed manually, malware binary analysis is a time ...
Kyoung Soo Han, Boojoong Kang, Eul Gyu Im   +2 more
openaire   +1 more source

Malware Classification using Malware Visualization and Deep Learning

2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2023
Prabhpreet Singh, null Priyanshu, Aruna Bhat   +2 more
openaire   +1 more source

Malware Classification Using Image Representation

2019
In the recent years, there has been a rapid rise in the number of files submitted to anti-virus companies for analysis. It has become very difficult to analyse the functionality of each file manually. Malware developers have been highly successful in evading signature-based detection techniques. Most of the prevailing static analysis techniques involve
Ajay Singh, Anand Handa, Nitesh Kumar, Sandeep Kumar Shukla   +3 more
openaire   +1 more source

Malware Classification using Image Analysis

Abstract—Malware detection and classification have evolved significantly with the integration of pattern recognition and image classification techniques. A pioneering study by Nataraj et al. (2011) [1] demonstrated that malware binaries could be visualized as grayscale images, revealing structural and textural similarities among malware families ...
Syam Gopi, Evelyn Susan Jacob, Joel John, Raynell Rajeev, Steve Alex   +4 more
openaire   +1 more source

BERT for Malware Classification

2022
Joel Alvares, Fabio Di Troia
openaire   +1 more source

Malware Classification using Deep Learning

SSRN Electronic Journal, 2022
Ameena K Nazeer, Thara RJ
openaire   +1 more source

Detection and Classification of Malware

2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021
D Chandrakala, Armaan Sait, J Kiruthika, R Nivetha   +3 more
openaire   +1 more source

Malware classification using neural network

2022
Deeptanshu Singh Rathore, Ashwini Sapkal, Geeta Patil, Rahul Desai, Aparna Joshi   +4 more
openaire   +1 more source

Malware Attacks Classification Model

International Journal of Science and Engineering Applications
openaire   +1 more source