Aviad Cohen
Orcid: 0000-0001-9976-0525Affiliations:
- Ben-Gurion University of the Negev, Beer Sheva, Israel
According to our database1,
Aviad Cohen
authored at least 24 papers
between 2014 and 2024.
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Bibliography
2024
MinCloud: Trusted and transferable MinHash-based framework for unknown malware detection for Linux cloud environments.
J. Inf. Secur. Appl., 2024
2023
Efficient feature extraction methodologies for unknown MP4-Malware detection using Machine learning algorithms.
Expert Syst. Appl., June, 2023
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements.
ACM Comput. Surv., 2023
2022
Signal Process., 2022
2021
Pay Attention: Improving Classification of PE Malware Using Attention Mechanisms Based on System Call Analysis.
Proceedings of the International Joint Conference on Neural Networks, 2021
2020
Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments.
Neural Networks, 2020
ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks.
Neural Networks, 2020
Mind your privacy: Privacy leakage through BCI applications using machine learning methods.
Knowl. Based Syst., 2020
CardiWall: A Trusted Firewall for the Detection of Malicious Clinical Programming of Cardiac Implantable Electronic Devices.
IEEE Access, 2020
IEEE Access, 2020
2019
Volatile memory analysis using the MinHash method for efficient and secured detection of malware in private cloud.
Comput. Secur., 2019
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework.
IEEE Access, 2019
TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning.
Proceedings of the International Joint Conference on Neural Networks, 2019
2018
Trusted system-calls analysis methodology aimed at detection of compromised virtual machines using sequential mining.
Knowl. Based Syst., 2018
Novel set of general descriptive features for enhanced detection of malicious emails using machine learning methods.
Expert Syst. Appl., 2018
Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory.
Expert Syst. Appl., 2018
2017
ALDOCX: Detection of Unknown Malicious Microsoft Office Documents Using Designated Active Learning Methods Based on New Structural Feature Extraction Methodology.
IEEE Trans. Inf. Forensics Secur., 2017
Proceedings of the A Systems Approach to Cyber Security, 2017
2016
Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework.
Secur. Informatics, 2016
SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods.
Expert Syst. Appl., 2016
2015
Detection of malicious PDF files and directions for enhancements: A state-of-the art survey.
Comput. Secur., 2015
Search Problems in the Domain of Multiplication: Case Study on Anomaly Detection Using Markov Chains.
Proceedings of the Eighth Annual Symposium on Combinatorial Search, 2015
Boosting the Detection of Malicious Documents Using Designated Active Learning Methods.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015
2014
Proceedings of the IEEE Joint Intelligence and Security Informatics Conference, 2014