Nir Nissim

Orcid: 0000-0003-0652-8861

According to our database1, Nir Nissim authored at least 55 papers between 2007 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Online presence:

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Bibliography

2024
Speech emotion recognition systems and their security aspects.
Artif. Intell. Rev., June, 2024

CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods.
Eng. Appl. Artif. Intell., 2024

Patterns of time-interval based patterns for improved multivariate time series data classification.
Eng. Appl. Artif. Intell., 2024

Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls.
Comput. Secur., 2024

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
The infinite race between steganography and steganalysis in images.
Signal Process., 2022

Time-interval temporal patterns can beat and explain the malware.
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

SEC-C-U: The Security of Intensive Care Unit Medical Devices and Their Ecosystems.
IEEE Access, 2020

MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images.
IEEE Access, 2020

2019
Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems.
J. Biomed. Informatics, 2019

Dynamic Malware Analysis in the Modern Era - A State of the Art Survey.
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

Temporal Probabilistic Profiles for Sepsis Prediction in the ICU.
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

Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices.
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

Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients.
CoRR, 2017

USB-based attacks.
Comput. Secur., 2017

Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.
Artif. Intell. Medicine, 2017

Scholarly Digital Libraries as a Platform for Malware Distribution.
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

An Active Learning Framework for Efficient Condition Severity Classification.
Proceedings of the Artificial Intelligence in Medicine, 2015

2014
Novel active learning methods for enhanced PC malware detection in windows OS.
Expert Syst. Appl., 2014

ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files.
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
Unknown malcode detection and the imbalance problem.
J. Comput. Virol., 2009

2008
Malicious Code Detection Using Active Learning.
Proceedings of the Privacy, 2008

Unknown malcode detection via text categorization and the imbalance problem.
Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 2008

Active learning to improve the detection of unknown computer worms activity.
Proceedings of the 11th International Conference on Information Fusion, 2008

2007
Improving the Detection of Unknown Computer Worms Activity Using Active Learning.
Proceedings of the KI 2007: Advances in Artificial Intelligence, 2007

Malicious Code Detection and Acquisition Using Active Learning.
Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 2007


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