Author Info
Name
Affiliation
Papers
NIR NISSIM
Deutsche Telekom Laboratories at Ben-Gurion University, Be'er Sheva, 84105, Israel
44
Collaborators
Citations 
PageRank 
65
199
19.42
Referers 
Referees 
References 
387
653
505
Search Limit
100653
Title
Citations
PageRank
Year
The infinite race between steganography and steganalysis in images00.342022
Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms00.342022
A time-interval-based active learning framework for enhanced PE malware acquisition and detection00.342022
Time-interval temporal patterns can beat and explain the malware00.342022
Pay Attention: Improving Classification of PE Malware Using Attention Mechanisms Based on System Call Analysis00.342021
Cardio-ML: Detection of malicious clinical programmings aimed at cardiac implantable electronic devices based on machine learning and a missing values resemblance framework00.342021
Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments10.412021
Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments00.342021
Mind Your Mind: EEG-Based Brain-Computer Interfaces and Their Security in Cyber Space00.342020
Mind your privacy: Privacy leakage through BCI applications using machine learning methods00.342020
MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images.00.342020
Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments.10.352020
CardiWall: A Trusted Firewall for the Detection of Malicious Clinical Programming of Cardiac Implantable Electronic Devices.00.342020
ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks00.342020
Temporal Probabilistic Profiles for Sepsis Prediction in the ICU40.442019
Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems.00.342019
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework00.342019
Volatile memory analysis using the MinHash method for efficient and secured detection of malware in private cloud.10.362019
Dynamic Malware Analysis in the Modern Era—A State of the Art Survey60.612019
Malboard: A novel user keystroke impersonation attack and trusted detection framework based on side-channel analysis30.402019
TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning10.352019
Novel set of general descriptive features for enhanced detection of malicious emails using machine learning methods.00.342018
Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices.00.342018
Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory.110.602018
Trusted system-calls analysis methodology aimed at detection of compromised virtual machines using sequential mining.50.432018
Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods.60.452017
Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients.00.342017
ALDOCX: Detection of Unknown Malicious Microsoft Office Documents Using Designated Active Learning Methods Based on New Structural Feature Extraction Methodology.120.752017
USB-based attacks.00.342017
Scholarly Digital Libraries as a Platform for Malware Distribution.00.342017
ALDROID: efficient update of Android anti-virus software using designated active learning methods.110.562016
SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods.40.402016
Improving condition severity classification with an efficient active learning based framework.50.422016
Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework.80.452016
Boosting The Detection Of Malicious Documents Using Designated Active Learning Methods40.462015
Detection of malicious PDF files and directions for enhancements: A state-of-the art survey.40.402015
ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files90.472014
Novel active learning methods for enhanced PC malware detection in windows OS.140.622014
Detecting unknown computer worm activity via support vector machines and active learning270.852012
Unknown malcode detection and the imbalance problem290.982009
Malicious Code Detection Using Active Learning130.812008
Active learning to improve the detection of unknown computer worms activity.30.372008
Improving the Detection of Unknown Computer Worms Activity Using Active Learning60.402007
Malicious Code Detection and Acquisition Using Active Learning110.652007