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Digital Threats: Research and Practice is a peer-reviewed journal published by ACM, covering Advanced Malware Detection Techniques, Network Security and Intrusion Detection, Information and Cyber Security. Over its lifetime it has published 257 papers, which have received 2,569 citations.
| Publisher | ACM |
|---|---|
| Reference style | Numbered (ACM) Numbered — [1], [2] in the text [1] Ada Smith, Ben Jones, and Cara Lee. 2023. A representative article title. Digital Threats: Research and Practice 12, 3, Article 45 (2023).
Formats any DOI in Digital Threats: Research and Practice style. No sign-up. |
| Publishes research in | Advanced Malware Detection Techniques Network Security and Intrusion Detection Information and Cyber Security Digital and Cyber Forensics Spam and Phishing Detection |
| ISSN | 2576-5337 |
| Citation impact (2-yr) | 3.17 |
| h-index | 25 |
| i10-index | 72 |
| Open access | Yes |
| Top institutions publishing here | Friedrich-Alexander-Universität Erlangen-Nürnberg |
| Journal website | dl.acm.org |
| 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.
Machine Learning (ML) represents a pivotal technology for current and future information systems, and many domains already leverage the capabilities of ML. However, deployment of ML in cybersecurity is still at an early stage, revealing a significant discrepancy between research and practice. Such a discrepancy has its root cause in the current state of the…
Massive dissemination of fake news and its potential to erode democracy has increased the demand for accurate fake news detection. Recent advancements in this area have proposed novel techniques that aim to detect fake news by exploring how it propagates on social networks. Nevertheless, to detect fake news at an early stage, i.e., when it…
The incremental diffusion of machine learning algorithms in supporting cybersecurity is creating novel defensive opportunities but also new types of risks. Multiple researches have shown that machine learning methods are vulnerable to adversarial attacks that create tiny perturbations aimed at decreasing the effectiveness of detecting threats. We observe that existing literature assumes threat models that…
In an effort to assist factcheckers in the process of factchecking, we tackle the claim detection task, one of the necessary stages prior to determining the veracity of a claim. It consists of identifying the set of sentences, out of a long text, deemed capable of being factchecked. This article is a collaborative work between…
Threats to devices that are part of the Internet of Things (IoT) are on the rise. Owing to the overwhelming diversity of IoT hardware and software, as well as its variants, conventional anti-virus techniques based on the Windows paradigm cannot be applied directly to counter threats to the IoT devices. In this article, we propose…
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