Nir Nissim

Emailnirnibgu.acil

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Research interests

Cyber Security, Malware Detection, Machine Learning, Data Science , Bioemdical Informatics, Computational Neuroscience

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32 Article
12 Conference contribution
4 Review article
1 Paper
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- 1 Preprint

Improving malicious email detection through novel designated deep-learning architectures utilizing entire email
Muralidharan, T. & Nissim, N., 1 Jan 2023, In: Neural Networks. 157, p. 257-279 23 p.
Research output: Contribution to journal › Article › peer-review

Open Access
A time-interval-based active learning framework for enhanced PE malware acquisition and detection
Finder, I., Sheetrit, E. & Nissim, N., 1 Oct 2022, In: Computers and Security. 121, 102838.
Research output: Contribution to journal › Article › peer-review
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements
Muralidharan, T., Cohen, A., Gerson, N. & Nissim, N., 3 Dec 2022, In: ACM Computing Surveys. 55, 5, 108.
Research output: Contribution to journal › Article › peer-review
4 Scopus citations
File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements.
Muralidharan, T., Cohen, A., Gerson, N. & Nissim, N., Dec 2022, In: ACM Computing Surveys. 55, 5, p. 108:1-108:45 45 p., 108.
Research output: Contribution to journal › Article › peer-review
Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms
Levy-Loboda, T., Sheetrit, E., Liberty, I. F., Haim, A. & Nissim, N., 1 Aug 2022, In: Journal of Biomedical Informatics. 132, 104129.
Research output: Contribution to journal › Article › peer-review
The infinite race between steganography and steganalysis in images
Muralidharan, T., Cohen, A., Cohen, A. & Nissim, N., 1 Dec 2022, In: Signal Processing. 201, 108711.
Research output: Contribution to journal › Review article › peer-review
1 Scopus citations
Time-interval temporal patterns can beat and explain the malware
Finder, I., Sheetrit, E. & Nissim, N., 6 Apr 2022, In: Knowledge-Based Systems. 241, 108266.
Research output: Contribution to journal › Article › peer-review
2 Scopus citations
Cardio-ML: Detection of malicious clinical programmings aimed at cardiac implantable electronic devices based on machine learning and a missing values resemblance framework
Levy-Loboda, T., Rav-Acha, M., Katz, A. & Nissim, N., 1 Dec 2021, In: Artificial Intelligence in Medicine. 122, 102200.
Research output: Contribution to journal › Article › peer-review
Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments
Landman, T. & Nissim, N., 1 Dec 2021, In: Neural Networks. 144, p. 648-685 38 p.
Research output: Contribution to journal › Article › peer-review
4 Scopus citations
Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments
Panker, T. & Nissim, N., 17 Aug 2021, In: Knowledge-Based Systems. 226, 107095.
Research output: Contribution to journal › Article › peer-review
12 Scopus citations

An Active Learning Framework for Efficient Acquisition and Detection of Unknown Malware
Author: Nissim, N., 2016
Supervisor: Elovici, Y. (Supervisor)
Student thesis: Doctoral Thesis

Study Findings from Ben-Gurion University of the Negev Broaden Understanding of Machine Learning (A Time-interval-based Active Learning Framework for Enhanced Pe Malware Acquisition and Detection)
Nir Nissim & Eitam Sheetrit
9/12/22
1 item of Media coverage
Press/Media
Israel : BGU and ASU Consortium Awarded Prestigious Energy Cyber Center Grant
Dan Blumberg, Rami Puzis, Nir Nissim, Bracha Leah Shapira, Lior Rokach, Yisroel Mirsky, Yuval Elovici & David Tolpin
18/05/21
1 item of Media coverage
Press/Media

Nir Nissim

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Improving malicious email detection through novel designated deep-learning architectures utilizing entire email

A time-interval-based active learning framework for enhanced PE malware acquisition and detection

File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements

File Packing from the Malware Perspective: Techniques, Analysis Approaches, and Directions for Enhancements.

Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms

The infinite race between steganography and steganalysis in images

Time-interval temporal patterns can beat and explain the malware

Cardio-ML: Detection of malicious clinical programmings aimed at cardiac implantable electronic devices based on machine learning and a missing values resemblance framework

Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments

Leveraging malicious behavior traces from volatile memory using machine learning methods for trusted unknown malware detection in Linux cloud environments

An Active Learning Framework for Efficient Acquisition and Detection of Unknown Malware

Study Findings from Ben-Gurion University of the Negev Broaden Understanding of Machine Learning (A Time-interval-based Active Learning Framework for Enhanced Pe Malware Acquisition and Detection)

Israel : BGU and ASU Consortium Awarded Prestigious Energy Cyber Center Grant

Nir Nissim

Personal profile

Research interests

Fingerprint

Network

Research output

Thesis

Press / Media