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EURASIP Journal on Information Security is a peer-reviewed journal published by Springer Nature, covering Network Security and Intrusion Detection, Advanced Malware Detection Techniques, Digital Media Forensic Detection.
| Publisher | Springer Nature |
|---|---|
| Reference style | Superscript numbered (Nature) Superscript — small raised numerals in the text 1. Smith, A., Jones, B. & Lee, C. A representative article title. EURASIP Journal on Information Security 12, 45–58 (2023).
Formats any DOI in EURASIP Journal on Information Security style. No sign-up. |
| Publishes research in | Network Security and Intrusion Detection Advanced Malware Detection Techniques Digital Media Forensic Detection Advanced Steganography and Watermarking Techniques Internet Traffic Analysis and Secure E-voting |
| ISSN | 2510-523X |
| Citation impact (2-yr) | 4.68 |
| h-index | 24 |
| i10-index | 61 |
| Total citations | 2,591 |
| Article processing charge | $809 |
| Open access | Yes |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | jis-eurasipjournals.springeropen.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and…
Abstract Security vulnerabilities of the modern Internet of Things (IoT) systems are unique, mainly due to the complexity and heterogeneity of the technology and data. The risks born out of these IoT systems cannot easily fit into an existing risk framework. There are many cybersecurity risk assessment approaches and frameworks that are under deployment in…
Network attacks are serious concerns in today’s increasingly interconnected society. Recent studies have applied conventional machine learning to network attack detection by learning the patterns of the network behaviors and training a classification model. These models usually require large labeled datasets; however, the rapid pace and unpredictability of cyber attacks make this labeling impossible in…
Blockchains are proposed for many application domains apart from financial transactions. While there are generic blockchains that can be molded for specific use cases, they often lack a lightweight and easy-to-customize implementation. In this paper, we introduce the core concepts of blockchain technology and investigate a real-world use case from the energy domain, where customers…
Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized…
