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Machine learning security and privacy: a review of threats and countermeasures EURASIP J. Info. Secur. Pub Date : 2024-04-23 Anum Paracha, Junaid Arshad, Mohamed Ben Farah, Khalid Ismail
Machine learning has become prevalent in transforming diverse aspects of our daily lives through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular systems, and automated threat detection and triage are some prominent use cases. Furthermore, the increasing use of machine learning in critical national infrastructures such as smart grids, transport, and natural resources
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Intelligent multi-agent model for energy-efficient communication in wireless sensor networks EURASIP J. Info. Secur. Pub Date : 2024-04-08 Kiran Saleem, Lei Wang, Salil Bharany, Khmaies Ouahada, Ateeq Ur Rehman, Habib Hamam
The research addresses energy consumption, latency, and network reliability challenges in wireless sensor network communication, especially in military security applications. A multi-agent context-aware model employing the belief-desire-intention (BDI) reasoning mechanism is proposed. This model utilizes a semantic knowledge-based intelligent reasoning network to monitor suspicious activities within
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FEDDBN-IDS: federated deep belief network-based wireless network intrusion detection system EURASIP J. Info. Secur. Pub Date : 2024-04-04 M. Nivaashini, E. Suganya, S. Sountharrajan, M. Prabu, Durga Prasad Bavirisetti
Over the last 20 years, Wi-Fi technology has advanced to the point where most modern devices are small and rely on Wi-Fi to access the internet. Wi-Fi network security is severely questioned since there is no physical barrier separating a wireless network from a wired network, and the security procedures in place are defenseless against a wide range of threats. This study set out to assess federated
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Cancelable templates for secure face verification based on deep learning and random projections EURASIP J. Info. Secur. Pub Date : 2024-03-08 Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli
Recently, biometric recognition has become a significant field of research. The concept of cancelable biometrics (CB) has been introduced to address security concerns related to the handling of sensitive data. In this paper, we address unconstrained face verification by proposing a deep cancelable framework called BiometricNet+ that employs random projections (RP) to conceal face images and compressive
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Network security threat detection technology based on EPSO-BP algorithm EURASIP J. Info. Secur. Pub Date : 2024-02-24 Zhu Lan
With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and
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RFID tag recognition model for Internet of Things for training room management EURASIP J. Info. Secur. Pub Date : 2024-02-24 Shengqi Wu
With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management
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Efficient identity security authentication method based on improved R-LWE algorithm in IoT environment EURASIP J. Info. Secur. Pub Date : 2024-02-22 Lin Yang
In recent years, various smart devices based on IoT technology, such as smart homes, healthcare, detection, and logistics systems, have emerged. However, as the number of IoT-connected devices increases, securing the IoT is becoming increasingly challenging. To tackle the increasing security challenges caused by the proliferation of IoT devices, this research proposes an innovative method for IoT identity
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Improved RFID mutual authentication protocol against exhaustive attack in the context of big data EURASIP J. Info. Secur. Pub Date : 2024-01-31 Kongze Li
The development of big data has epromoted the development of Internet technology, but it has brought more network security and privacy problems. Therefore, how to solve network security problems is the main research direction of current network technology development. In order to deal with the harm of network attacks to personal privacy security, this paper studies and proposes an RFID mutual authentication
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IoT devices and data availability optimization by ANN and KNN EURASIP J. Info. Secur. Pub Date : 2024-01-02 Zhiqiang Chen, Zhihua Song, Tao Zhang, Yong Wei
Extensive research has been conducted to enhance the availability of IoT devices and data by focusing on the rapid prediction of instantaneous fault rates and temperatures. Temperature plays a crucial role in device availability as it significantly impacts equipment performance and lifespan. It serves as a vital indicator for predicting equipment failure and enables the improvement of availability
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Research on privacy and secure storage protection of personalized medical data based on hybrid encryption EURASIP J. Info. Secur. Pub Date : 2024-01-02 Jialu Lv
Personalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on
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Node fault diagnosis algorithm for wireless sensor networks based on BN and WSN EURASIP J. Info. Secur. Pub Date : 2023-12-13 Ming Li
Wireless sensor networks, as an emerging information exchange technology, have been widely applied in many fields. However, nodes tend to become damaged in harsh and complex environmental conditions. In order to effectively diagnose node faults, a Bayesian model-based node fault diagnosis model was proposed. Firstly, a comprehensive analysis was conducted into the operative principles of wireless sensor
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Robust JPEG steganography based on the robustness classifier EURASIP J. Info. Secur. Pub Date : 2023-12-11 Jimin Zhang, Xianfeng Zhao, Xiaolei He
Because the JPEG recompression in social networks changes the DCT coefficients of uploaded images, applying image steganography in popular image-sharing social networks requires robustness. Currently, most robust steganography algorithms rely on the resistance of embedding to the general JPEG recompression process. The operations in a specific compression channel are usually ignored, which reduces
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The design of network security protection trust management system based on an improved hidden Markov model EURASIP J. Info. Secur. Pub Date : 2023-11-23 Shaojun Chen
With the growth of the Internet, network security issues have become increasingly complex, and the importance of node interaction security is also gradually becoming prominent. At present, research on network security protection mainly starts from the overall perspective, and some studies also start from the interaction between nodes. However, the trust management mechanisms in these studies do not
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Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks EURASIP J. Info. Secur. Pub Date : 2023-10-19 Dan Wang, Qing Wu, Ming Hu
Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected
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Correction: Mobile authentication of copy detection patterns EURASIP J. Info. Secur. Pub Date : 2023-10-03 Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy
Correction: EURASIP J Inf Secur 2023, 4 (2023) https://doi.org/10.1186/s13635-023-00140-5 The original publication of this articled [1] contained 2 issues: 1. The "hat" indicates were incorrectly shown in the equations 2. There were problems with the readability of the figures & tables The original publication has been updated to amend these errors. The publisher apologizes for the inconvenience caused
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User authentication and access control to blockchain-based forensic log data EURASIP J. Info. Secur. Pub Date : 2023-07-25 Md. Ezazul Islam, Md. Rafiqul Islam, Madhu Chetty, Suryani Lim, Mehmood Chadhar
For dispute resolution in daily life, tamper-proof data storage and retrieval of log data are important with the incorporation of trustworthy access control for the related users and devices, while giving access to confidential data to the relevant users and maintaining data persistency are two major challenges in information security. This research uses blockchain data structure to maintain data persistency
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Network traffic classification model based on attention mechanism and spatiotemporal features EURASIP J. Info. Secur. Pub Date : 2023-07-12 Feifei Hu, Situo Zhang, Xubin Lin, Liu Wu, Niandong Liao, Yanqi Song
Traffic classification is widely used in network security and network management. Early studies have mainly focused on mapping network traffic to different unencrypted applications, but little research has been done on network traffic classification of encrypted applications, especially the underlying traffic of encrypted applications. To address the above issues, this paper proposes a network encryption
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Network intrusion detection based on multi-domain data and ensemble-bidirectional LSTM EURASIP J. Info. Secur. Pub Date : 2023-06-26 Xiaoning Wang, Jia Liu, Chunjiong Zhang
Different types of network traffic can be treated as data originating from different domains with the same objectives of problem-solving. Previous work utilizing multi-domain machine learning has primarily assumed that data in different domains have the same distribution, which fails to effectively address the domain offset problem and may not achieve excellent performance in every domain. To address
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Mobile authentication of copy detection patterns EURASIP J. Info. Secur. Pub Date : 2023-06-06 Olga Taran, Joakim Tutt, Taras Holotyak, Roman Chaban, Slavi Bonev, Slava Voloshynovskiy
In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem
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Gaussian class-conditional simplex loss for accurate, adversarially robust deep classifier training EURASIP J. Info. Secur. Pub Date : 2023-03-10 Arslan Ali, Andrea Migliorati, Tiziano Bianchi, Enrico Magli
In this work, we present the Gaussian Class-Conditional Simplex (GCCS) loss: a novel approach for training deep robust multiclass classifiers that improves over the state-of-the-art in terms of classification accuracy and adversarial robustness, with little extra cost for network training. The proposed method learns a mapping of the input classes onto Gaussian target distributions in a latent space
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A multi-gateway authentication and key-agreement scheme on wireless sensor networks for IoT EURASIP J. Info. Secur. Pub Date : 2023-03-08 Jen-Ho Yang
The Internet of Things (IoT) is designed to let anything connect to the Internet, and the things can be people, computers, and things. On the IoT, the Wireless Sensor Network (WSN) plays an important role because it can be used in many applications such as smart home, intelligent transportation, and intelligent disaster prevention. Since the WSN transmits data in the wireless way, the security problem
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Image life trails based on contrast reduction models for face counter-spoofing EURASIP J. Info. Secur. Pub Date : 2023-01-16 Katika, Balaji Rao, Karthik, Kannan
Natural face images are both content and context-rich, in the sense that they carry significant immersive information via depth cues embedded in the form of self-shadows or a space varying blur. Images of planar face prints, on the other hand, tend to have lower contrast and also suppressed depth cues. In this work, a solution is proposed, to detect planar print spoofing by enhancing self-shadow patterns
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Improved scheme and evaluation method for progressive visual cryptography EURASIP J. Info. Secur. Pub Date : 2022-12-17 Le Thanh Thai, Binh, Tanaka, Hidema, Watanabe, Kohtaro
Visual cryptography (VC) is a powerful technique with high security and requires no PC or device to reconstruct the secret information. Progressive visual cryptography (PVC) is a variation of the VC scheme in which the quality of the reconstructed image is improved by increasing the number of shared images. The previous study focused directly on maximizing the value of the quality in the completely
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Human-artificial intelligence approaches for secure analysis in CAPTCHA codes EURASIP J. Info. Secur. Pub Date : 2022-12-12 Dinh, Nghia, Ogiela, Lidia
CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) has long been used to keep automated bots from misusing web services by leveraging human-artificial intelligence (HAI) interactions to distinguish whether the user is a human or a computer program. Various CAPTCHA schemes have been proposed over the years, principally to increase usability and security against emerging
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“Alexa, What’s a Phishing Email?”: Training users to spot phishing emails using a voice assistant EURASIP J. Info. Secur. Pub Date : 2022-11-22 Sharevski, Filipo, Jachim, Peter
This paper reports the findings from an empirical study investigating the effectiveness of using intelligent voice assistants, Amazon Alexa in our case, to deliver a phishing training to users. Because intelligent voice assistants can hardly utilize visual cues but provide for convenient interaction with users, we developed an interaction-based phishing training focused on the principles of persuasion
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Behavior-based user authentication on mobile devices in various usage contexts EURASIP J. Info. Secur. Pub Date : 2022-09-16 Progonov, Dmytro, Cherniakova, Valentyna, Kolesnichenko, Pavlo, Oliynyk, Andriy
Reliable and non-intrusive user identification and authentication on mobile devices, such as smartphones, are topical tasks today. The majority of state-of-the-art solutions in this domain are based on “device unlock” scenario—checking of information (authentication factors) provided by the user for unlocking a smartphone. As such factors, we may use either single strong authentication factor, for
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Presentation attack detection and biometric recognition in a challenge-response formalism EURASIP J. Info. Secur. Pub Date : 2022-09-05 Haasnoot, Erwin, Spreeuwers, Luuk J., Veldhuis, Raymond N. J.
Presentation attack detection (PAD) is used to mitigate the dangers of the weakest link problem in biometric recognition, in which failure modes of one application affect the security of all other applications. Strong PAD methods are therefore a must, and we believe biometric challenge-response protocols (BCRP) form an underestimated part of this ecosystem. In this paper, we conceptualize what BCRPs
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Robust video steganography for social media sharing based on principal component analysis EURASIP J. Info. Secur. Pub Date : 2022-06-20 Fan, Pingan, Zhang, Hong, Zhao, Xianfeng
Most social media channels are lossy where videos are transcoded to reduce transmission bandwidth or storage space, such as social networking sites and video sharing platforms. Video transcoding makes most video steganographic schemes unusable for hidden communication based on social media. This paper proposes robust video steganography against video transcoding to construct reliable hidden communication
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On the methodology of fingerprint template protection schemes conception : meditations on the reliability EURASIP J. Info. Secur. Pub Date : 2022-03-25 Lahmidi, Ayoub, Moujahdi, Chouaib, Minaoui, Khalid, Rziza, Mohammed
Among the most major potential attacks against fingerprint authentication systems are those that target the stored reference templates. These threats are extremely damaging as they can lead to the invasion of user privacy. The countermeasures to secure fingerprint templates are therefore an indisputable necessity. In literature, although there are so many approaches that address this kind of vulnerability
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DIPPAS: a deep image prior PRNU anonymization scheme EURASIP J. Info. Secur. Pub Date : 2022-02-14 Picetti, Francesco, Mandelli, Sara, Bestagini, Paolo, Lipari, Vincenzo, Tubaro, Stefano
Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counterpart is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the photo response non-uniformity (PRNU), a noise pattern left
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Secure machine learning against adversarial samples at test time EURASIP J. Info. Secur. Pub Date : 2022-01-12 Lin, Jing, Njilla, Laurent L., Xiong, Kaiqi
Deep neural networks (DNNs) are widely used to handle many difficult tasks, such as image classification and malware detection, and achieve outstanding performance. However, recent studies on adversarial examples, which have maliciously undetectable perturbations added to their original samples that are indistinguishable by human eyes but mislead the machine learning approaches, show that machine learning
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Feature partitioning for robust tree ensembles and their certification in adversarial scenarios EURASIP J. Info. Secur. Pub Date : 2021-12-11 Calzavara, Stefano, Lucchese, Claudio, Marcuzzi, Federico, Orlando, Salvatore
Machine learning algorithms, however effective, are known to be vulnerable in adversarial scenarios where a malicious user may inject manipulated instances. In this work, we focus on evasion attacks, where a model is trained in a safe environment and exposed to attacks at inference time. The attacker aims at finding a perturbation of an instance that changes the model outcome.We propose a model-agnostic
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Detection of illicit cryptomining using network metadata EURASIP J. Info. Secur. Pub Date : 2021-12-04 Russo, Michele, Šrndić, Nedim, Laskov, Pavel
Illicit cryptocurrency mining has become one of the prevalent methods for monetization of computer security incidents. In this attack, victims’ computing resources are abused to mine cryptocurrency for the benefit of attackers. The most popular illicitly mined digital coin is Monero as it provides strong anonymity and is efficiently mined on CPUs.Illicit mining crucially relies on communication between
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Multitask adversarial attack with dispersion amplification EURASIP J. Info. Secur. Pub Date : 2021-09-22 Haleta, Pavlo, Likhomanov, Dmytro, Sokol, Oleksandra
Recently, adversarial attacks have drawn the community’s attention as an effective tool to degrade the accuracy of neural networks. However, their actual usage in the world is limited. The main reason is that real-world machine learning systems, such as content filters or face detectors, often consist of multiple neural networks, each performing an individual task. To attack such a system, adversarial
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Potential advantages and limitations of using information fusion in media forensics—a discussion on the example of detecting face morphing attacks EURASIP J. Info. Secur. Pub Date : 2021-07-29 Kraetzer, Christian, Makrushin, Andrey, Dittmann, Jana, Hildebrandt, Mario
Information fusion, i.e., the combination of expert systems, has a huge potential to improve the accuracy of pattern recognition systems. During the last decades, various application fields started to use different fusion concepts extensively. The forensic sciences are still hesitant if it comes to blindly applying information fusion. Here, a potentially negative impact on the classification accuracy
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Estimating Previous Quantization Factors on Multiple JPEG Compressed Images EURASIP J. Info. Secur. Pub Date : 2021-06-28 Sebastiano Battiato, Oliver Giudice, Francesco Guarnera, Giovanni Puglisi
The JPEG compression algorithm has proven to be efficient in saving storage and preserving image quality thus becoming extremely popular. On the other hand, the overall process leaves traces into encoded signals which are typically exploited for forensic purposes: for instance, the compression parameters of the acquisition device (or editing software) could be inferred. To this aim, in this paper a
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Proposing a reliable method of securing and verifying the credentials of graduates through blockchain EURASIP J. Info. Secur. Pub Date : 2021-06-26 T. Rama Reddy, P. V. G. D. Prasad Reddy, Rayudu Srinivas, Ch. V. Raghavendran, R. V. S. Lalitha, B. Annapurna
Education acts as a soul in the overall societal development, in one way or the other. Aspirants, who gain their degrees genuinely, will help society with their knowledge and skills. But, on the other side of the coin, the problem of fake certificates is alarming and worrying. It has been prevalent in different forms from paper-based dummy certificates to replicas backed with database tampering and
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On the information leakage quantification of camera fingerprint estimates EURASIP J. Info. Secur. Pub Date : 2021-06-03 Samuel Fernández-Menduiña, Fernando Pérez-González
Camera fingerprints based on sensor PhotoResponse Non-Uniformity (PRNU) have gained broad popularity in forensic applications due to their ability to univocally identify the camera that captured a certain image. The fingerprint of a given sensor is extracted through some estimation method that requires a few images known to be taken with such sensor. In this paper, we show that the fingerprints extracted
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Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture EURASIP J. Info. Secur. Pub Date : 2021-05-17 Benedetta Tondi, Andrea Costanzo, Dequ Huang, Bin Li
Estimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across different image regions can be used to localize splicing in double JPEG tampered images. Traditional model-based
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Media forensics on social media platforms: a survey EURASIP J. Info. Secur. Pub Date : 2021-05-01 Cecilia Pasquini, Irene Amerini, Giulia Boato
The dependability of visual information on the web and the authenticity of digital media appearing virally in social media platforms has been raising unprecedented concerns. As a result, in the last years the multimedia forensics research community pursued the ambition to scale the forensic analysis to real-world web-based open systems. This survey aims at describing the work done so far on the analysis
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Research on gray correlation analysis and situation prediction of network information security EURASIP J. Info. Secur. Pub Date : 2021-04-20 Chengqiong Ye, Wenyu Shi, Rui Zhang
In order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result
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Synthetic speech detection through short-term and long-term prediction traces EURASIP J. Info. Secur. Pub Date : 2021-04-06 Clara Borrelli, Paolo Bestagini, Fabio Antonacci, Augusto Sarti, Stefano Tubaro
Several methods for synthetic audio speech generation have been developed in the literature through the years. With the great technological advances brought by deep learning, many novel synthetic speech techniques achieving incredible realistic results have been recently proposed. As these methods generate convincing fake human voices, they can be used in a malicious way to negatively impact on today’s
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Use of SHDM in commutative watermarking encryption EURASIP J. Info. Secur. Pub Date : 2021-01-05 Roland Schmitz
SHDM stands for Sphere-Hardening Dither Modulation and is a watermarking algorithm based on quantizing the norm of a vector extracted from the cover work. We show how SHDM can be integrated into a fully commutative watermarking-encryption scheme and investigate implementations in the spatial, DCT, and DWT domain with respect to their fidelity, robustness, capacity, and security of encryption. The watermarking
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Smooth adversarial examples EURASIP J. Info. Secur. Pub Date : 2020-11-17 Hanwei Zhang, Yannis Avrithis, Teddy Furon, Laurent Amsaleg
This paper investigates the visual quality of the adversarial examples. Recent papers propose to smooth the perturbations to get rid of high frequency artifacts. In this work, smoothing has a different meaning as it perceptually shapes the perturbation according to the visual content of the image to be attacked. The perturbation becomes locally smooth on the flat areas of the input image, but it may
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A novel technique to prevent SQL injection and cross-site scripting attacks using Knuth-Morris-Pratt string match algorithm EURASIP J. Info. Secur. Pub Date : 2020-08-18 Oluwakemi Christiana Abikoye, Abdullahi Abubakar, Ahmed Haruna Dokoro, Oluwatobi Noah Akande, Aderonke Anthonia Kayode
Structured Query Language (SQL) injection and cross-site scripting remain a major threat to data-driven web applications. Instances where hackers obtain unrestricted access to back-end database of web applications so as to steal, edit, and destroy confidential data are increasing. Therefore, measures must be put in place to curtail the growing threats of SQL injection and XSS attacks. This study presents
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Scalable, efficient, and secure RFID with elliptic curve cryptosystem for Internet of Things in healthcare environment EURASIP J. Info. Secur. Pub Date : 2020-07-29 Davood Noori, Hassan Shakeri, Masood Niazi Torshiz
The rapid development of IoT technology has led to the usage of various devices in our daily life. Along with the ever-increasing rise of the Internet of Things, the use of appropriate methods for establishing secure communications in health care systems is vital. The adoption of high-security optimal mechanisms for this purpose has been more effective regarding the efficiency of medical information
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Trembling triggers: exploring the sensitivity of backdoors in DNN-based face recognition EURASIP J. Info. Secur. Pub Date : 2020-06-23 Cecilia Pasquini, Rainer Böhme
Backdoor attacks against supervised machine learning methods seek to modify the training samples in such a way that, at inference time, the presence of a specific pattern (trigger) in the input data causes misclassifications to a target class chosen by the adversary. Successful backdoor attacks have been presented in particular for face recognition systems based on deep neural networks (DNNs). These
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Reversible data hiding for binary images based on adaptive overlapping pattern EURASIP J. Info. Secur. Pub Date : 2020-06-01 Keming Dong, Hyoung Joong Kim, Xiaohan Yu, Xiaoqing Feng
Pattern substitution (PS) method (Ho et al., Comput. Stand. Interfaces 31:787–794, 2009) is a recent reversible data hiding method for binary images. It generates one pattern pair, the patterns in which are called PM and PF, and substitutes between them to embed one bit. Two types of PS have been proposed: non-overlapping PS and overlapping PS. However, Dong et al. (ETRI J. 37:990–1000, 2015) states
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Machine learning through cryptographic glasses: combating adversarial attacks by key-based diversified aggregation. EURASIP J. Info. Secur. Pub Date : 2020-06-01 Olga Taran,Shideh Rezaeifar,Taras Holotyak,Slava Voloshynovskiy
In recent years, classification techniques based on deep neural networks (DNN) were widely used in many fields such as computer vision, natural language processing, and self-driving cars. However, the vulnerability of the DNN-based classification systems to adversarial attacks questions their usage in many critical applications. Therefore, the development of robust DNN-based classifiers is a critical
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ELSA: efficient long-term secure storage of large datasets (full version) ∗ EURASIP J. Info. Secur. Pub Date : 2020-05-27 Philipp Muth, Matthias Geihs, Tolga Arul, Johannes Buchmann, Stefan Katzenbeisser
An increasing amount of information today is generated, exchanged, and stored digitally. This also includes long-lived and highly sensitive information (e.g., electronic health records, governmental documents) whose integrity and confidentiality must be protected over decades or even centuries. While there is a vast amount of cryptography-based data protection schemes, only few are designed for long-term
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IoT cyber risk: a holistic analysis of cyber risk assessment frameworks, risk vectors, and risk ranking process EURASIP J. Info. Secur. Pub Date : 2020-05-26 Kamalanathan Kandasamy, Sethuraman Srinivas, Krishnashree Achuthan, Venkat P. Rangan
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 many governmental and commercial organizations. Extending
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Low-cost data partitioning and encrypted backup scheme for defending against co-resident attacks EURASIP J. Info. Secur. Pub Date : 2020-05-24 Junfeng Tian, Zilong Wang, Zhen Li
Aiming at preventing user data leakage and the damage that is caused by co-resident attacks in the cloud environment, a data partitioning and encryption backup (P&XE) scheme is proposed. After the data have been divided into blocks, the data are backed up using the XOR operation between the data. Then, the backup data are encrypted using a random string. Compared with the existing scheme, the proposed
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Swapped face detection using deep learning and subjective assessment EURASIP J. Info. Secur. Pub Date : 2020-05-19 Xinyi Ding, Zohreh Raziei, Eric C. Larson, Eli V. Olinick, Paul Krueger, Michael Hahsler
The tremendous success of deep learning for imaging applications has resulted in numerous beneficial advances. Unfortunately, this success has also been a catalyst for malicious uses such as photo-realistic face swapping of parties without consent. In this study, we use deep transfer learning for face swapping detection, showing true positive rates greater than 96% with very few false alarms. Distinguished
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Deep neural rejection against adversarial examples EURASIP J. Info. Secur. Pub Date : 2020-04-07 Angelo Sotgiu, Ambra Demontis, Marco Melis, Battista Biggio, Giorgio Fumera, Xiaoyi Feng, Fabio Roli
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time. In this work, we propose a deep neural rejection mechanism to detect adversarial examples, based on the idea of rejecting samples that exhibit anomalous
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Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device EURASIP J. Info. Secur. Pub Date : 2020-03-17 Elakkiya Ellavarason, Richard Guest, Farzin Deravi
User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life
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Understanding visual lip-based biometric authentication for mobile devices EURASIP J. Info. Secur. Pub Date : 2020-03-12 Carrie Wright, Darryl William Stewart
This paper explores the suitability of lip-based authentication as a behavioural biometric for mobile devices. Lip-based biometric authentication is the process of verifying an individual based on visual information taken from the lips while speaking. It is particularly suited to mobile devices because it contains unique information; its potential for liveness over existing popular biometrics such
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Keystroke biometrics in the encrypted domain: a first study on search suggestion functions of web search engines EURASIP J. Info. Secur. Pub Date : 2020-02-21 Nicholas Whiskerd, Nicklas Körtge, Kris Jürgens, Kevin Lamshöft, Salatiel Ezennaya-Gomez, Claus Vielhauer, Jana Dittmann, Mario Hildebrandt
A feature of search engines is prediction and suggestion to complete or extend input query phrases, i.e. search suggestion functions (SSF). Given the immediate temporal nature of this functionality, alongside the character submitted to trigger each suggestion, adequate data is provided to derive keystroke features. The potential of such biometric features to be used in identification and tracking poses
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Combining PRNU and noiseprint for robust and efficient device source identification EURASIP J. Info. Secur. Pub Date : 2020-02-12 Davide Cozzolino, Francesco Marra, Diego Gragnaniello, Giovanni Poggi, Luisa Verdoliva
PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images or estimating the camera
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Long-term integrity protection of genomic data EURASIP J. Info. Secur. Pub Date : 2019-10-29 Johannes Buchmann, Matthias Geihs, Kay Hamacher, Stefan Katzenbeisser, Sebastian Stammler
Genomic data is crucial in the understanding of many diseases and for the guidance of medical treatments. Pharmacogenomics and cancer genomics are just two areas in precision medicine of rapidly growing utilization. At the same time, whole-genome sequencing costs are plummeting below $ 1000, meaning that a rapid growth in full-genome data storage requirements is foreseeable. While privacy protection
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Multi-layer intrusion detection system with ExtraTrees feature selection, extreme learning machine ensemble, and softmax aggregation EURASIP J. Info. Secur. Pub Date : 2019-10-22 Jivitesh Sharma, Charul Giri, Ole-Christoffer Granmo, Morten Goodwin
Recent advances in intrusion detection systems based on machine learning have indeed outperformed other techniques, but struggle with detecting multiple classes of attacks with high accuracy. We propose a method that works in three stages. First, the ExtraTrees classifier is used to select relevant features for each type of attack individually for each (ELM). Then, an ensemble of ELMs is used to detect