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Nir Nissim
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2020 – today
- 2024
- [j42]Itzik Gurowiec, Nir Nissim:
Speech emotion recognition systems and their security aspects. Artif. Intell. Rev. 57(6): 148 (2024) - [j41]Gil Shenderovitz
, Nir Nissim
Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls. Comput. Secur. 142: 103862 (2024) - [j40]Gil Shenderovitz
Patterns of time-interval based patterns for improved multivariate time series data classification. Eng. Appl. Artif. Intell. 133: 108171 (2024) - [j39]Alexander Yevsikov
CADefender: Detection of unknown malicious AutoLISP computer-aided design files using designated feature extraction and machine learning methods. Eng. Appl. Artif. Intell. 138: 109414 (2024) - [j38]Tomer Panker, Aviad Cohen, Tom Landman, Chen Bery, Nir Nissim:
MinCloud: Trusted and transferable MinHash-based framework for unknown malware detection for Linux cloud environments. J. Inf. Secur. Appl. 87: 103907 (2024) - 2023
- [j37]Trivikram Muralidharan
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements. ACM Comput. Surv. 55(5): 108:1-108:45 (2023) - [j36]Dor Zazon, Lior Fink, Shirley Gordon, Nir Nissim:
Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods. Decis. Support Syst. 168: 113930 (2023) - [j35]Tal Tsafrir, Aviad Cohen
Efficient feature extraction methodologies for unknown MP4-Malware detection using Machine learning algorithms. Expert Syst. Appl. 219: 119615 (2023) - [j34]Trivikram Muralidharan
Improving malicious email detection through novel designated deep-learning architectures utilizing entire email. Neural Networks 157: 257-279 (2023) - 2022
- [j33]Ido Finder, Eitam Sheetrit, Nir Nissim
A time-interval-based active learning framework for enhanced PE malware acquisition and detection. Comput. Secur. 121: 102838 (2022) - [j32]Tamar Levy-Loboda
Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms. J. Biomed. Informatics 132: 104129 (2022) - [j31]Ido Finder
Time-interval temporal patterns can beat and explain the malware. Knowl. Based Syst. 241: 108266 (2022) - [j30]Trivikram Muralidharan
The infinite race between steganography and steganalysis in images. Signal Process. 201: 108711 (2022) - 2021
- [j29]Tamar Levy-Loboda, Moshe Rav-Acha, Amos Katz, Nir Nissim:
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 122: 102200 (2021) - [j28]Ofir Landau, Rami Puzis, Nir Nissim
Mind Your Mind: EEG-Based Brain-Computer Interfaces and Their Security in Cyber Space. ACM Comput. Surv. 53(1): 17:1-17:38 (2021) - [j27]Tomer Panker
Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments. Knowl. Based Syst. 226: 107095 (2021) - [j26]Tom Landman
Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments. Neural Networks 144: 648-685 (2021) - [c12]Ori Or-Meir, Aviad Cohen, Yuval Elovici, Lior Rokach, Nir Nissim:
Pay Attention: Improving Classification of PE Malware Using Attention Mechanisms Based on System Call Analysis. IJCNN 2021: 1-8 - 2020
- [j25]Aviad Cohen
MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images. IEEE Access 8: 19997-20011 (2020) - [j24]Matan Kintzlinger
CardiWall: A Trusted Firewall for the Detection of Malicious Clinical Programming of Cardiac Implantable Electronic Devices. IEEE Access 8: 48123-48140 (2020) - [j23]Carmel Eliash
SEC-C-U: The Security of Intensive Care Unit Medical Devices and Their Ecosystems. IEEE Access 8: 64193-64224 (2020) - [j22]Ofir Landau, Aviad Cohen, Shirley Gordon, Nir Nissim:
Mind your privacy: Privacy leakage through BCI applications using machine learning methods. Knowl. Based Syst. 198: 105932 (2020) - [j21]Daniel Nahmias
Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments. Neural Networks 124: 243-257 (2020) - [j20]Assaf Cohen
ASSAF: Advanced and Slim StegAnalysis Detection Framework for JPEG images based on deep convolutional denoising autoencoder and Siamese networks. Neural Networks 131: 64-77 (2020)
2010 – 2019
- 2019
- [j19]Nir Nissim
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework. IEEE Access 7: 110050-110073 (2019) - [j18]Nitzan Farhi, Nir Nissim, Yuval Elovici:
Malboard: A novel user keystroke impersonation attack and trusted detection framework based on side-channel analysis. Comput. Secur. 85: 240-269 (2019) - [j17]Nir Nissim, Omri Lahav, Aviad Cohen
Volatile memory analysis using the MinHash method for efficient and secured detection of malware in private cloud. Comput. Secur. 87 (2019) - [j16]Ori Or-Meir, Nir Nissim
Dynamic Malware Analysis in the Modern Era - A State of the Art Survey. ACM Comput. Surv. 52(5): 88:1-88:48 (2019) - [j15]Matan Kintzlinger, Nir Nissim
Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems. J. Biomed. Informatics 95 (2019) - [c11]Daniel Nahmias, Aviad Cohen
TrustSign: Trusted Malware Signature Generation in Private Clouds Using Deep Feature Transfer Learning. IJCNN 2019: 1-8 - [c10]Eitam Sheetrit, Nir Nissim, Denis Klimov, Yuval Shahar:
Temporal Probabilistic Profiles for Sepsis Prediction in the ICU. KDD 2019: 2961-2969 - 2018
- [j14]Aviad Cohen
Trusted detection of ransomware in a private cloud using machine learning methods leveraging meta-features from volatile memory. Expert Syst. Appl. 102: 158-178 (2018) - [j13]Aviad Cohen
Novel set of general descriptive features for enhanced detection of malicious emails using machine learning methods. Expert Syst. Appl. 110: 143-169 (2018) - [j12]Nir Nissim
Trusted system-calls analysis methodology aimed at detection of compromised virtual machines using sequential mining. Knowl. Based Syst. 153: 147-175 (2018) - [i2]Tom Mahler
Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices. CoRR abs/1801.05583 (2018) - 2017
- [j11]Nir Nissim, Yuval Shahar
Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods. Artif. Intell. Medicine 81: 12-32 (2017) - [j10]Nir Nissim
USB-based attacks. Comput. Secur. 70: 675-688 (2017) - [j9]Nir Nissim
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. 12(3): 631-646 (2017) - [c9]Nir Nissim, Aviad Cohen, Jian Wu, Andrea Lanzi, Lior Rokach, Yuval Elovici, C. Lee Giles
Scholarly Digital Libraries as a Platform for Malware Distribution. SG-CRC 2017: 107-128 - [i1]Eitam Sheetrit, Nir Nissim, Denis Klimov, Lior Fuchs, Yuval Elovici, Yuval Shahar:
Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients. CoRR abs/1709.01720 (2017) - 2016
- [j8]Aviad Cohen, Nir Nissim, Lior Rokach, Yuval Elovici:
SFEM: Structural feature extraction methodology for the detection of malicious office documents using machine learning methods. Expert Syst. Appl. 63: 324-343 (2016) - [j7]Nir Nissim, Mary Regina Boland
Improving condition severity classification with an efficient active learning based framework. J. Biomed. Informatics 61: 44-54 (2016) - [j6]Nir Nissim, Robert Moskovitch, Oren Bar-Ad, Lior Rokach, Yuval Elovici:
ALDROID: efficient update of Android anti-virus software using designated active learning methods. Knowl. Inf. Syst. 49(3): 795-833 (2016) - [j5]Nir Nissim, Aviad Cohen, Robert Moskovitch, Asaf Shabtai, Matan Edri, Oren Bar-Ad, Yuval Elovici:
Keeping pace with the creation of new malicious PDF files using an active-learning based detection framework. Secur. Informatics 5(1): 1 (2016) - 2015
- [j4]Nir Nissim, Aviad Cohen, Chanan Glezer
Detection of malicious PDF files and directions for enhancements: A state-of-the art survey. Comput. Secur. 48: 246-266 (2015) - [c8]Nir Nissim, Mary Regina Boland
An Active Learning Framework for Efficient Condition Severity Classification. AIME 2015: 13-24 - [c7]Nir Nissim, Aviad Cohen, Yuval Elovici:
Boosting the Detection of Malicious Documents Using Designated Active Learning Methods. ICMLA 2015: 760-765 - 2014
- [j3]Nir Nissim, Robert Moskovitch, Lior Rokach, Yuval Elovici:
Novel active learning methods for enhanced PC malware detection in windows OS. Expert Syst. Appl. 41(13): 5843-5857 (2014) - [c6]Nir Nissim, Aviad Cohen, Robert Moskovitch, Asaf Shabtai, Mattan Edry, Oren Bar-Ad, Yuval Elovici:
ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files. JISIC 2014: 91-98 - 2012
- [j2]Nir Nissim, Robert Moskovitch, Lior Rokach, Yuval Elovici:
Detecting unknown computer worm activity via support vector machines and active learning. Pattern Anal. Appl. 15(4): 459-475 (2012)
2000 – 2009
- 2009
- [j1]Robert Moskovitch, Dima Stopel, Clint Feher, Nir Nissim, Nathalie Japkowicz
Unknown malcode detection and the imbalance problem. J. Comput. Virol. 5(4): 295-308 (2009) - 2008
- [c5]Robert Moskovitch, Nir Nissim, Roman Englert, Yuval Elovici:
Active learning to improve the detection of unknown computer worms activity. FUSION 2008: 1-8 - [c4]Robert Moskovitch, Dima Stopel, Clint Feher, Nir Nissim, Yuval Elovici:
Unknown malcode detection via text categorization and the imbalance problem. ISI 2008: 156-161 - [c3]Robert Moskovitch, Nir Nissim, Yuval Elovici:
Malicious Code Detection Using Active Learning. PinKDD 2008: 74-91 - 2007
- [c2]Robert Moskovitch, Nir Nissim, Yuval Elovici:
Malicious Code Detection and Acquisition Using Active Learning. ISI 2007: 371 - [c1]Robert Moskovitch, Nir Nissim, Dima Stopel, Clint Feher, Roman Englert, Yuval Elovici:
Improving the Detection of Unknown Computer Worms Activity Using Active Learning. KI 2007: 489-493
Coauthor Index
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last updated on 2024-11-18 21:44 CET by the dblp team
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