Nir Nissim
Orcid: 0000-0003-0652-8861
According to our database1,
Nir Nissim
authored at least 51 papers
between 2007 and 2023.
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Bibliography
2023
Efficient feature extraction methodologies for unknown MP4-Malware detection using Machine learning algorithms.
Expert Syst. Appl., June, 2023
Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods.
Decis. Support Syst., May, 2023
Improving malicious email detection through novel designated deep-learning architectures utilizing entire email.
Neural Networks, 2023
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements.
ACM Comput. Surv., 2023
2022
Signal Process., 2022
Knowl. Based Syst., 2022
Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms.
J. Biomed. Informatics, 2022
A time-interval-based active learning framework for enhanced PE malware acquisition and detection.
Comput. Secur., 2022
2021
Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments.
Neural Networks, 2021
Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments.
Knowl. Based Syst., 2021
Mind Your Mind: EEG-Based Brain-Computer Interfaces and Their Security in Cyber Space.
ACM Comput. Surv., 2021
Cardio-ML: Detection of malicious clinical programmings aimed at cardiac implantable electronic devices based on machine learning and a missing values resemblance framework.
Artif. Intell. Medicine, 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
IEEE Access, 2020
2019
Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems.
J. Biomed. Informatics, 2019
ACM Comput. Surv., 2019
Volatile memory analysis using the MinHash method for efficient and secured detection of malware in private cloud.
Comput. Secur., 2019
Malboard: A novel user keystroke impersonation attack and trusted detection framework based on side-channel analysis.
Comput. Secur., 2019
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework.
IEEE Access, 2019
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 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
CoRR, 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
Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.
Artif. Intell. Medicine, 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
ALDROID: efficient update of Android anti-virus software using designated active learning methods.
Knowl. Inf. Syst., 2016
Improving condition severity classification with an efficient active learning based framework.
J. Biomed. 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
Boosting the Detection of Malicious Documents Using Designated Active Learning Methods.
Proceedings of the 14th IEEE International Conference on Machine Learning and Applications, 2015
Proceedings of the Artificial Intelligence in Medicine, 2015
2014
Expert Syst. Appl., 2014
Proceedings of the IEEE Joint Intelligence and Security Informatics Conference, 2014
2012
Detecting unknown computer worm activity via support vector machines and active learning.
Pattern Anal. Appl., 2012
2009
2008
Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 2008
Proceedings of the 11th International Conference on Information Fusion, 2008
2007
Proceedings of the KI 2007: Advances in Artificial Intelligence, 2007
Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 2007