Nir Nissim

2.4k total citations · 1 hit paper
51 papers, 1.5k citations indexed

About

Nir Nissim is a scholar working on Signal Processing, Computer Networks and Communications and Information Systems. According to data from OpenAlex, Nir Nissim has authored 51 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Signal Processing, 30 papers in Computer Networks and Communications and 23 papers in Information Systems. Recurrent topics in Nir Nissim's work include Advanced Malware Detection Techniques (36 papers), Network Security and Intrusion Detection (30 papers) and Spam and Phishing Detection (12 papers). Nir Nissim is often cited by papers focused on Advanced Malware Detection Techniques (36 papers), Network Security and Intrusion Detection (30 papers) and Spam and Phishing Detection (12 papers). Nir Nissim collaborates with scholars based in Israel, United States and United Kingdom. Nir Nissim's co-authors include Yuval Elovici, Aviad Cohen, Lior Rokach, Robert Moskovitch, Clint Feher, Yuval Shaḥar, Chanan Glezer, Rami Puzis, Denis Klimov and Nathalie Japkowicz and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and ACM Computing Surveys.

In The Last Decade

Nir Nissim

48 papers receiving 1.5k citations

Hit Papers

Dynamic Malware Analysis in the Modern Era—A State of the... 2019 2026 2021 2023 2019 50 100 150 200
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Dynamic Malware Analysis in the Modern Era—A State of the Art Survey
    2019 208 citations Nir Nissim, Yuval Elovici et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Nir Nissim Israel 25 1.2k 984 777 556 127 51 1.5k
Robert K. Cunningham United States 19 774 0.7× 1.4k 1.5× 650 0.8× 1.0k 1.9× 66 0.5× 40 1.9k
Jack W. Stokes United States 15 863 0.7× 749 0.8× 474 0.6× 538 1.0× 84 0.7× 53 1.2k
Golden G. Richard United States 20 815 0.7× 660 0.7× 1.0k 1.3× 356 0.6× 349 2.7× 90 1.5k
Marco Cova United States 15 1.1k 0.9× 875 0.9× 1.2k 1.5× 817 1.5× 46 0.4× 23 1.7k
Christopher Jermaine United States 21 669 0.6× 794 0.8× 300 0.4× 763 1.4× 173 1.4× 57 1.4k
Massimiliano Albanese United States 20 363 0.3× 627 0.6× 515 0.7× 326 0.6× 254 2.0× 79 1.1k
Aurélien Francillon France 21 1.2k 1.1× 637 0.6× 584 0.8× 1.1k 1.9× 110 0.9× 55 1.8k
Nayeem Islam United States 14 427 0.4× 634 0.6× 404 0.5× 538 1.0× 56 0.4× 31 1.1k
N. Cercone Canada 15 422 0.4× 279 0.3× 574 0.7× 573 1.0× 73 0.6× 42 1.2k

Countries citing papers authored by Nir Nissim

Since Specialization
Citations

This map shows the geographic impact of Nir Nissim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nir Nissim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nir Nissim more than expected).

Fields of papers citing papers by Nir Nissim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nir Nissim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nir Nissim. The network helps show where Nir Nissim may publish in the future.

Co-authorship network of co-authors of Nir Nissim

This figure shows the co-authorship network connecting the top 25 collaborators of Nir Nissim. A scholar is included among the top collaborators of Nir Nissim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Nir Nissim. Nir Nissim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
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Nissim, Nir, et al.. (2025). Improving speech emotion recognition capabilities in the short and long term using temporal bucketing and active learning. Computers in Biology and Medicine. 196(Pt C). 110912–110912.
3.
Cohen, Aviad, et al.. (2024). MinCloud: Trusted and transferable MinHash-based framework for unknown malware detection for Linux cloud environments. Journal of Information Security and Applications. 87. 103907–103907. 1 indexed citations
4.
Nissim, Nir, et al.. (2024). Bon-APT: Detection, attribution, and explainability of APT malware using temporal segmentation of API calls. Computers & Security. 142. 103862–103862. 14 indexed citations
5.
Nissim, Nir, et al.. (2024). Patterns of time-interval based patterns for improved multivariate time series data classification. Engineering Applications of Artificial Intelligence. 133. 108171–108171.
6.
Cohen, Aviad, et al.. (2023). Efficient feature extraction methodologies for unknown MP4-Malware detection using Machine learning algorithms. Expert Systems with Applications. 219. 119615–119615. 6 indexed citations
7.
Liberty, Idit F., et al.. (2022). Personalized insulin dose manipulation attack and its detection using interval-based temporal patterns and machine learning algorithms. Journal of Biomedical Informatics. 132. 104129–104129. 16 indexed citations
8.
Nissim, Nir, et al.. (2022). Time-interval temporal patterns can beat and explain the malware. Knowledge-Based Systems. 241. 108266–108266. 19 indexed citations
9.
Nissim, Nir, et al.. (2022). Improving malicious email detection through novel designated deep-learning architectures utilizing entire email. Neural Networks. 157. 257–279. 24 indexed citations
10.
11.
Nissim, Nir, et al.. (2021). Deep-Hook: A trusted deep learning-based framework for unknown malware detection and classification in Linux cloud environments. Neural Networks. 144. 648–685. 29 indexed citations
12.
Cohen, Aviad, Nir Nissim, Moshe Rav, et al.. (2020). CardiWall: A Trusted Firewall for the Detection of Malicious Clinical Programming of Cardiac Implantable Electronic Devices. IEEE Access. 8. 48123–48140. 13 indexed citations
13.
Cohen, Aviad, Nir Nissim, & Yuval Elovici. (2020). MalJPEG: Machine Learning Based Solution for the Detection of Malicious JPEG Images. IEEE Access. 8. 19997–20011. 24 indexed citations
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Cohen, Aviad, et al.. (2020). Deep feature transfer learning for trusted and automated malware signature generation in private cloud environments. Neural Networks. 124. 243–257. 39 indexed citations
16.
Nissim, Nir, et al.. (2019). Keep an eye on your personal belongings! The security of personal medical devices and their ecosystems. Journal of Biomedical Informatics. 95. 103233–103233. 24 indexed citations
17.
Nissim, Nir, Yuval Shaḥar, Yuval Elovici, George Hripcsak, & Robert Moskovitch. (2017). Inter-labeler and intra-labeler variability of condition severity classification models using active and passive learning methods. Artificial Intelligence in Medicine. 81. 12–32. 13 indexed citations
18.
Nissim, Nir, et al.. (2014). ALPD: Active Learning Framework for Enhancing the Detection of Malicious PDF Files. 91–98. 29 indexed citations
19.
Moskovitch, Robert, et al.. (2009). Unknown malcode detection and the imbalance problem. 5(4). 295–308. 45 indexed citations
20.
Moskovitch, Robert, Nir Nissim, Roman Englert, & Yuval Elovici. (2008). Active learning to improve the detection of unknown computer worms activity. International Conference on Information Fusion. 1–8. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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