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Antreas Dionysiou

Bio: Antreas Dionysiou is an academic researcher from University of Cyprus. The author has contributed to research in topics: Deep learning & Password. The author has an hindex of 2, co-authored 5 publications receiving 11 citations.

Papers
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Journal ArticleDOI
TL;DR: Overall, the study suggests that fundamentally different ways of conducting reverse Turing test, that will be painless for legitimate users but at the same time challenging for automated systems, should be considered for ensuring the healthy operation of current Internet services.

15 citations

Proceedings ArticleDOI
24 May 2021
TL;DR: In this article, the authors propose HoneyGen, a practical and highly robust HGT that produces realistic looking honeywords by leveraging representation learning techniques to learn useful and explanatory representations from a massive collection of unstructured data, i.e., each operator's password database.
Abstract: Honeywords are false passwords injected in a database for detecting password leakage. Generating honeywords is a challenging problem due to the various assumptions about the adversary's knowledge as well as users' password-selection behaviour. The success of a Honeywords Generation Technique (HGT) lies on the resulting honeywords; the method fails if an adversary can easily distinguish the real password. In this paper, we propose HoneyGen, a practical and highly robust HGT that produces realistic looking honeywords. We do this by leveraging representation learning techniques to learn useful and explanatory representations from a massive collection of unstructured data, i.e., each operator's password database. We perform both a quantitative and qualitative evaluation of our framework using the state-of-the-art metrics. Our results suggest that HoneyGen generates high-quality honeywords that cause sophisticated attackers to achieve low distinguishing success rates.

12 citations

Book ChapterDOI
04 Oct 2018
TL;DR: This paper presents the development and implementation of an innovative by design combination of CNNs with SVMs as a solution to the Protein Secondary Structure Prediction problem, with a novel two dimensional input representation method, where Multiple Sequence Alignment profile vectors are placed one under another.
Abstract: Trying to extract features from complex sequential data for classification and prediction problems is an extremely difficult task. Deep Machine Learning techniques, such as Convolutional Neural Networks (CNNs), have been exclusively designed to face this class of problems. Support Vector Machines (SVMs) are a powerful technique for general classification problems, regression, and outlier detection. In this paper we present the development and implementation of an innovative by design combination of CNNs with SVMs as a solution to the Protein Secondary Structure Prediction problem, with a novel two dimensional (2D) input representation method, where Multiple Sequence Alignment profile vectors are placed one under another. This 2D input is used to train the CNNs achieving preliminary results of 80.40% per residue accuracy (Q3), which are expected to increase with the use of larger training datasets and more sophisticated ensemble methods.

7 citations

Proceedings ArticleDOI
15 Nov 2021
TL;DR: Zhang et al. as mentioned in this paper proposed EvilText, a general adversarial text generation framework that successfully evades some of the most popular NLP machines by perturbing a given legitimate text, preserving the original text's semantics as well as human readability.
Abstract: Adversarial Text Generation Frameworks (ATGFs) aim at causing a Natural Language Processing (NLP) machine to misbehave, i.e., misclassify a given input. In this paper, we propose EvilText, a general ATGF that successfully evades some of the most popular NLP machines by (efficiently) perturbing a given legitimate text, preserving at the same time the original text's semantics as well as human readability. Perturbations are based on visually similar classes of characters appearing in the unicode set. EvilText can be utilized from NLP services' operators for evaluating their systems security and robustness. Furthermore, EvilText outperforms the state-of-the-art ATGFs, in terms of: (a) effectiveness, (b) efficiency and (c) original text's semantics and human readability preservation. We evaluate EvilText on some of the most popular NLP systems used for sentiment analysis and toxic content detection. We further expand on the generality and transferability of our ATGF, while also exploring possible countermeasures for defending against our attacks. Surprisingly, naive defence mechanisms fail to mitigate our attacks; the only promising one being the restriction of unicode characters use. However, we argue that restricting the use of unicode characters imposes a significant trade-off between security and usability as almost all websites are heavily based on unicode support.

4 citations

Proceedings ArticleDOI
05 Aug 2021
TL;DR: In this paper, the authors present the preliminary results of the maritime transport security services demonstrator, developed under the CyberSecurityForEurope (CS4EU) pilot project, and set up a demonstrator to integrate, extend and validate four maritime-specific security services, covering risk and threat management, system hardening, trust management and secure communications.
Abstract: Maritime transport is a characteristic example of a collaborative and complex cyber-physical environment, involving various stakeholders and actors, with different goals and requirements. Securing such a complex ecosystem is a challenging task and has recently attracted various research efforts in different areas including, threat management, system hardening, trust management and communication security. However, the integration and validation of such targeted maritime transport security services is a complex task that has its own challenges. In this paper we present the preliminary results of the maritime transport security services demonstrator, developed under the CyberSecurityForEurope (CS4EU) pilot project. We have set up a demonstrator to integrate, extend and validate four maritime-specific security services, covering risk and threat management, system hardening, trust management and secure communications. Our goal is to enhance the provisioning of these services and to identify possible research and implementation gaps.

3 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, the authors provide an updated and categorized overview of computer vision applications in construction by examining the recent developments in the field and identifying the opportunities and challenges that future research needs to address to fully leverage the potential benefits of Computer Vision.

33 citations

Journal ArticleDOI
TL;DR: Overall, the study suggests that fundamentally different ways of conducting reverse Turing test, that will be painless for legitimate users but at the same time challenging for automated systems, should be considered for ensuring the healthy operation of current Internet services.

15 citations

Proceedings ArticleDOI
24 May 2021
TL;DR: In this article, the authors propose HoneyGen, a practical and highly robust HGT that produces realistic looking honeywords by leveraging representation learning techniques to learn useful and explanatory representations from a massive collection of unstructured data, i.e., each operator's password database.
Abstract: Honeywords are false passwords injected in a database for detecting password leakage. Generating honeywords is a challenging problem due to the various assumptions about the adversary's knowledge as well as users' password-selection behaviour. The success of a Honeywords Generation Technique (HGT) lies on the resulting honeywords; the method fails if an adversary can easily distinguish the real password. In this paper, we propose HoneyGen, a practical and highly robust HGT that produces realistic looking honeywords. We do this by leveraging representation learning techniques to learn useful and explanatory representations from a massive collection of unstructured data, i.e., each operator's password database. We perform both a quantitative and qualitative evaluation of our framework using the state-of-the-art metrics. Our results suggest that HoneyGen generates high-quality honeywords that cause sophisticated attackers to achieve low distinguishing success rates.

12 citations

Journal ArticleDOI
TL;DR: A comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP) is provided in this article , where a pre-trained language models are used as input features for PSSP studies.
Abstract: This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.

6 citations

Journal ArticleDOI
TL;DR: In this article, a novel cloud endpoint coordinating CAPTCHA based on multi-view stacking ensemble (MVSE) is proposed to make most use of the computing power of endpoint devices and reduce the calculation pressure of cloud system.

5 citations