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Defending against model poisoning attack in federated learning: A variance-minimization approach J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-11 Hairuo Xu, Tao Shu
The distributed nature of federated learning (FL) renders the learning process susceptible to model poisoning attacks, whereby local workers in FL report fabricated and false local training outcomes to the FL server with the intention to compromise/degrade the global model, or even derail the global learning process so that it can no longer converge. Existing defense mechanisms typically consider the
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Multimodal biometric user authentication using improved decentralized fuzzy vault scheme based on Blockchain network J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-11 Shreyansh Sharma, Anil Saini, Santanu Chaudhury
Biometric security has emerged as a major concern in the field of Biometric Systems. The existing centralized Biometric Cryptosystems approach still suffers from privacy issues and network-based attacks. In particular, the Biometric Cryptosystems are susceptible to Attack via Record Multiplicity, brute force and collusion attacks. To address these issues, this paper proposes a multimodal biometric
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Improving the robustness of steganalysis in the adversarial environment with Generative Adversarial Network J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-10 Ye Peng, Qi Yu, Guobin Fu, WenWen Zhang, ChaoFan Duan
As technology advances, the steganalysis methods based on Convolutional Neural Network (CNN)can more precisely detect steganography behavior. However, the existing CNN-based steganalysis methods have the problem of insufficient robustness in adversarial environments. Specifically, the steganography methods take advantage of adversarial examples’ capacity to deceive deep learning models, adding small
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Multi-keyword ranked search with access control for multiple data owners in the cloud J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-08 Jiaqi Guo, Cong Tian, Xu Lu, Liang Zhao, Zhenhua Duan
Secure search is important for promoting the widespread use of cloud computing. As a common situation, when data users search for certain data from multiple data owners, searchable encryption raises the possibility of secure searches over encrypted cloud data. However, most of the existing schemes for the multi-owner model are based on asymmetric searchable encryption, which is inefficient for searching
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S-Cred: An accountable anonymous credential scheme with decentralized verification and flexible revocation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-07 Haotian Cheng, Xiaofeng Li, He Zhao, Tong Zhou, Bin Yu, Nianzu Sheng
The popularization of digital credentials brings convenience to people, but it also increases the risks of privacy leakage and capability abuse. Designing an accountable anonymous credential scheme offers a promising solution to this problem. However, most existing schemes of accountable anonymous credentials cannot effectively support both decentralized verification and flexible revocation. In this
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Automatic decision tree-based NIDPS ruleset generation for DoS/DDoS attacks J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-06 Antonio Coscia, Vincenzo Dentamaro, Stefano Galantucci, Antonio Maci, Giuseppe Pirlo
As the occurrence of Denial of Service and Distributed Denial of Service (DoS/DDoS) attacks increases, the demand for effective defense mechanisms increases. Recognition of such anomalies in the computer network is commonly performed through network-based intrusion detection and prevention systems (NIDPSs). Although NIDPSs allow the interception of all known attacks, they are not robust to the continuing
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On the security of JPEG image encryption with RS pairs permutation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-06 Yuan Yuan, Hongjie He, Fan Chen, Lingfeng Qu
Recently, a JPEG encryption scheme with RS (run/size) pairs multi-permutation and block permutation has been proposed, which mainly improves security by extracting the expected number of RS pairs for global permutations that need overflow processing. However, it still has the risk of information leakage under the chosen plaintext attack. This is because the limited embedding capacity in overflow processing
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Defendroid: Real-time Android code vulnerability detection via blockchain federated neural network with XAI J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-05 Janaka Senanayake, Harsha Kalutarage, Andrei Petrovski, Luca Piras, Mhd Omar Al-Kadri
Ensuring strict adherence to security during the phases of Android app development is essential, primarily due to the prevalent issue of apps being released without adequate security measures in place. While a few automated tools are employed to reduce potential vulnerabilities during development, their effectiveness in detecting vulnerabilities may fall short. To address this, “Defendroid”, a blockchain-based
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Homomorphic encrypted Yara rules evaluation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-04 Diana-Elena Petrean, Rodica Potolea
Malware signatures represent a powerful tool for malware detection and classification, widely used by security researchers and security solution providers. Yara rules describe malware based on string patterns that are evaluated on targeted files. Generally, the security provider sends signatures to the client endpoints and the rule evaluation is performed locally, such that the scanned files do not
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VPPLR: Privacy-preserving logistic regression on vertically partitioned data using vectorization sharing J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-04 Yuhao Zhang, Min Tang
The construction of high-precision machine learning models relies on large-scale data collection from IoT devices. Since the training data involves sensitive user information, it is essential to design a privacy-preserving machine learning (PPML) paradigm to obtain reliable models without leaking data. Due to the diversity of IoT devices, the collected data from multiple sources often has different
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Attack-model-agnostic defense against model poisonings in distributed learning J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-03-02 Hairuo Xu, Tao Shu
The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its
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Physics-aware targeted attacks against maritime industrial control systems J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-26 Giacomo Longo, Francesco Lupia, Andrea Pugliese, Enrico Russo
The advancement of the maritime industry towards technologically integrated and automated systems has significantly increased the complexity of onboard Industrial Control Systems (ICS), raising concerns about cybersecurity risks. In this paper, we examine typical onboard ICS configurations through an adversarial lens. We introduce a threat model that leverages domain-specific peculiarities, e.g., maritime
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Android malware detection based on a novel mixed bytecode image combined with attention mechanism J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-26 Junwei Tang, Wei Xu, Tao Peng, Sijie Zhou, Qiaosen Pi, Ruhan He, Xinrong Hu
Mobile applications have been deeply integrated into the daily life of ordinary users. Android malware seriously threatens the privacy and property security of users. However, code obfuscation and other technologies have reduced the effectiveness of traditional static analysis methods, and more and more studies are extracting grayscale image features for malware detection. We propose a classification
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An explainable deepfake of speech detection method with spectrograms and waveforms J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-15 Ning Yu, Long Chen, Tao Leng, Zigang Chen, Xiaoyin Yi
Research on deepfake techniques for speech is crucial for combatting the spread of fake information, safeguarding public privacy, and advancing forensic techniques. However, the lack of transparency and explainability of spoofed speech detection models raises concerns about their reliability. In this paper, we suggest using raw waveform signals and spectrograms as fused features of the spoofed speech
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On the feasibility of E2E verifiable online voting – A case study from Durga Puja trial J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-15 Horia Druliac, Matthew Bardsley, Chris Riches, Christian Dunn, Luke Harrison, Bimal Roy, Feng Hao
India is the largest democracy by population and has one of the largest deployments of e-voting in the world for national elections. However, the e-voting machines used in India are not end-to-end (E2E) verifiable. The inability to verify the tallying integrity of an election by the public leaves the outcome open to disputes. E2E verifiable e-voting systems are commonly regarded as the most promising
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An efficient quantum-resistant undeniable signature protocol for the E-voting system J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-15 Quanrun Li, Debiao He, Yitao Chen, Jiaming Wen, Zhichao Yang
The Internet technology’s rapid development makes electronic voting become an important way to ensure democracy and fairness during the election process. Compared with manual voting, electronic voting mainly relies on mobile terminals and Internet technology. Therefore, it can effectively avoid manual errors and guarantee the authenticity of the voting result. Although lots of electronic voting systems
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Software vulnerable functions discovery based on code composite feature J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-14 Xue Yuan, Guanjun Lin, Huan Mei, Yonghang Tai, Jun Zhang
Vulnerability identification is crucial to protecting software systems from attacks. Although numerous learning-based solutions have been suggested to assist in vulnerability identification, these approaches often face challenges due to the scarcity of real-world vulnerability data. To extract as much vulnerability information as possible from limited data, we consider obtaining the characteristics
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SRIJAN: Secure Randomized Internally Joined Adjustable Network for one-way hashing J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-13 Abhilash Chakraborty, Anupam Biswas, Ajoy Kumar Khan
One of the most significant issues with existing hashing techniques is the transparency and limited customizability. Most of the existing hashing techniques use fixed parameters, which can be easily exploited by attackers who are familiar with the algorithm’s internal workings. Another issue with existing hashing techniques is the lack of randomness in their operations and the process flow. Attackers
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A comprehensive analysis combining structural features for detection of new ransomware families J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-07 Caio C. Moreira, Davi C. Moreira, Claudomiro Sales
This study presents a comprehensive static analysis method that combines multiple structural features extracted from Windows executable files. The method employs an ensemble soft voting model that comprises three machine learning techniques: Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGB). Our proposed model aims to identify newly emerged ransomware families by analyzing
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Approach based on STPA extended with STRIDE and LINDDUN, and blockchain to develop a mission-critical e-voting system J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-02-06 Júlio César Leitão Albuquerque de Farias, Andrei Carniel, Juliana de Melo Bezerra, Celso Massaki Hirata
Voting is essential to assure democracy. The voting process is supported by mission-critical systems that have among others functional, cybersecurity, and data privacy requirements. Comprehensive approaches are required to identify the requirements and technologies needed to design the solution. STPA is a method for identifying system safety requirements that have been extended to identify cybersecurity
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The robustness of behavior-verification-based slider CAPTCHAs J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-30 Guoqin Chang, Haichang Gao, Ge Pei, Sainan Luo, Yang Zhang, Nuo Cheng, Yiwen Tang, Qianwen Guo
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Designing energy-aware collaborative intrusion detection in IoT networks J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-29 Wenjuan Li, Philip Rosenberg, Mads Glisby, Michael Han
The Internet of Things (IoT) with its evolution brings many benefits to people’s routine life, while at the same time posing various security challenges, due to the lack of software updates and access control policies. For protection, collaborative/distributed detection networks are one essential security solution, which can enhance the detection capability of a separate detection node by governing
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An efficient cheating-detectable secret image sharing scheme with smaller share sizes J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-30 Zuquan Liu, Guopu Zhu, Yu Zhang, Hongli Zhang, Sam Kwong
As a new approach to image protection, polynomial-based secret image sharing (PSIS) has attracted a lot of attention from many researchers in recent decades. When SIS technology is applied in practice, it is inevitably subject to various types of attacks, with cheating being the most likely to occur. To deal with the cheating problem in the process of secret image reconstruction, several effective
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Object-attentional untargeted adversarial attack J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-29 Chao Zhou, Yuan-Gen Wang, Guopu Zhu
Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool the target model by generating either global perturbations or local patches. However, both global perturbations and local patches easily cause annoying visual artifacts in the adversarial example. Compared with some smooth regions of an image, the object region generally has more edges and
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Encryption-based sub-string matching for privacy-preserving record linkage J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-25 Sirintra Vaiwsri, Thilina Ranbaduge, Peter Christen
Accurate and secure string matching in record linkage is increasingly important in application domains such as bioinformatics, healthcare, and crime detection. Most existing privacy-preserving string matching techniques provide an overall similarity between a pair of strings. As a result, these techniques cannot identify the longest common sub-string between the strings in a pair leading to lower linkage
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A privacy-preserving multi-factor authentication scheme for cloud-assisted IoMT with post-quantum security J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-23 Xiao Chen, BaoCheng Wang, Haibin Li
The Internet of Medical Things (IoMT) has found widespread application in the healthcare system, leveraging the integration of wireless communication and cloud computing to revolutionize modern healthcare practices. This paradigm, however, poses security vulnerabilities since malicious actors might exploit the public channel to impersonate legitimate devices or services and steal sensitive data. Before
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PARGMF: A provenance-enabled automated rule generation and matching framework with multi-level attack description model J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-18 Michael Zipperle, Yu Zhang, Elizabeth Chang, Tharam Dillon
With the rapidly increasing volume of cyber-attacks over the past years due to the new working-from-home paradigm, protecting hosts, networks, and individuals from cyber threats is in higher demand than ever. One promising solution are Provenance-based Intrusion Detection Systems (PIDS), which correlate host-based security logs to generate provenance graphs that describe the causal relationship between
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MILP-based differential cryptanalysis on full-round shadow J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-17 Yanjun Li, Hao Lin, Xinjie Bi, Shanshan Huo, Yiyi Han
Shadow (Guo et al., 2021) is a lightweight block cipher based on a new logical combination method of AND-RX operation and the generalized Feistel structure with high diffusion and excellent performance in hardware implementation. In this paper, the components and structure of Shadow cipher are researched, and based on MILP automatic search algorithms for differential trails the 2-round iterative differential
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A reversible data hiding method based on bitmap prediction for AMBTC compressed hyperspectral images J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-13 Xiaoran Zhang, Zhibin Pan, Quan Zhou, Guojun Fan, Jing Dong
In the transmission of hyperspectral images that have been compressed using absolute moment block truncation coding (AMBTC), confidentiality and security of crucial information is often a concern. Although many data hiding (DH) methods based on AMBTC work well in guaranteeing a large amount of secret information can be embedded, the requirements of actual user scenarios, such as reversibility and imperceptibility
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Quantum image encryption algorithm via optimized quantum circuit and parity bit-plane permutation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-13 Jinwen He, Hegui Zhu, Xv Zhou
Quantum image encryption technology employs the unique features of superposition, entanglement, and quantum state instability, offering advantages like high efficiency, parallelism, and robust resistance to decryption attempts. In this work, we propose a quantum image encryption algorithm via an optimized quantum circuit and parity bit-plane permutation named OCPBP. Our research commences with applying
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Network IDS alert classification with active learning techniques J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-09 Risto Vaarandi, Alejandro Guerra-Manzanares
A Network Intrusion Detection System (NIDS) is a widely used security monitoring technology for detecting attacks against network services, beaconing activity of infected end user nodes, malware propagation, and other types of malicious network traffic. Unfortunately, NIDS technologies are known to generate a large number of alerts, with a significant proportion of them having low importance. During
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CVTStego-Net: A convolutional vision transformer architecture for spatial image steganalysis J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-08 Mario Alejandro Bravo-Ortiz, Esteban Mercado-Ruiz, Juan Pablo Villa-Pulgarin, Carlos Angel Hormaza-Cardona, Sebastian Quiñones-Arredondo, Harold Brayan Arteaga-Arteaga, Simon Orozco-Arias, Oscar Cardona-Morales, Reinel Tabares-Soto
The principal investigations in image steganalysis in the spatial domain have concentrated on convolutional neural network (CNN) designs. However, existing CNNs increase the local receptive field of steganographic noise without considering global steganographic noise. This study introduces CVTStego-Net, a convolutional vision transformer for spatial domain image steganalysis that merges the strengths
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A hybrid-trust-based emergency message dissemination model for vehicular ad hoc networks J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-09 Jianxiang Qi, Ning Zheng, Ming Xu, Ping Chen, Wenqiang Li
The security of data transmission is one of the focus issues in vehicular ad hoc networks (VANETs). The timely and effective dissemination of emergency messages can help drivers make intelligent decisions and improve road safety and traffic efficiency. However, malicious nodes in the network seriously interfere with the proper judgement of vehicles via data tampering or spreading false messages, which
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Cryptanalysis of an image encryption scheme based on two-point diffusion strategy and Henon map J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-06 Kuan-Wai Wong, Wun-She Yap, Bok-Min Goi, Denis C.-K. Wong, Guodong Ye
In recent year, an image encryption scheme was proposed based on Henon map and two-point diffusion strategy. Two-point diffusion method improved the conventional permutation–diffusion architecture by allowing two cryptographic primitives, i.e. permutation and diffusion intermingling with each other in every single round. In this paper, we show the dynamical degradation of digital Henon map using state-mapping
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A fast NTRU software implementation based on 5-way TMVP J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-06 Neslihan Yaman Gökce, Anıl Burak Gökce, Murat Cenk
The fast and efficient operation of post-quantum cryptographic algorithms has become an important research topic, especially with the recent developments and the process of the PQC Standardization Project by NIST. Various algorithms based on lattices are chosen to be finalists in this project. Number Theoretic Transform (NTT), Toom–Cook, Karatsuba, and Toeplitz matrix–vector product (TMVP) are considered
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CNN based image resizing forensics for double compressed JPEG images J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-05 Nikhil Reddy Billa, Bibhash Pran Das, Mrutyunjay Biswal, Manish Okade
This paper investigates a novel CNN-based architecture for image resizing forensics in the presence of Double-JPEG compression. Two sub-problems are addressed as part of this paper: first, the detection of resizing in DJPEG images, and second, determining the factor used to resize the image before the second JPEG compression. The image resizing technique used in this paper is image scaling. The proposed
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Enhancing android malware detection explainability through function call graph APIs J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-02 Diego Soi, Alessandro Sanna, Davide Maiorca, Giorgio Giacinto
Nowadays, mobile devices are massively used in everyday activities. Thus, they contain sensitive data targeted by threat actors like bank accounts and personal information. Through the years, Machine Learning approaches have been proposed to identify malicious Android applications, but recent research highlights the need for better explanations for model decisions, as existing ones may not be related
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Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-02 Siamak Layeghy, Marcus Gallagher, Marius Portmann
Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. Over the past years, a lot of research efforts have aimed at leveraging the increasingly powerful models of Machine Learning (ML) for this purpose. A number of labelled synthetic datasets have been generated and made publicly available by researchers, and they have become
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Cryptanalysis of cross-coupled chaotic maps multi-image encryption scheme J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-03 Laiphrakpam Dolendro Singh, Rohit Thingbaijam, Ripon Patgiri, Khoirom Motilal Singh
Recently, Patro et al. proposed a multiple gray-scale image encryption scheme based on cross-coupled chaotic maps. The authors claimed the scheme could withstand known plain-text attacks and chosen-plaintext attacks. In this paper, we have thoroughly analysed the Patro et al. algorithm and reported the crucial loopholes. Patro et al. algorithm uses a row-wise and column-wise scrambling operation based
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Leveraging zero knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges and opportunities J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-03 Abebe Diro, Lu Zhou, Akanksha Saini, Shahriar Kaisar, Pham Cong Hiep
Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced
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A novel compression-based 2D-chaotic sine map for enhancing privacy and security of biometric identification systems J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2024-01-02 Mobashshirur Rahman, Anita Murmu, Piyush Kumar, Nageswara Rao Moparthi, Suyel Namasudra
Human biometric images are utilized for cell phone authentication, airport security, and biometric passports. To improve the biometric identification process, this should be protected from cyber attackers because it is sensitive to any minor changes. Thus, security is a major concern in biometric images. Traditional encryption and compression methods are ineffective for encrypting biometric images
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The reality of backdoored S-Boxes—An eye opener J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-30 Shah Fahd, Mehreen Afzal, Waseem Iqbal, Dawood Shah, Ijaz Khalid
The real-life incidents researched in academia have revealed that (possibly) state-level efforts are made to camouflage the intentional flaws in the mathematical layer of an S-Box for exploiting the information-theoretic properties, i.e., Kuznyechik. To investigate the common features in the intentionally weakened mappings, this research thoroughly examines the backdoored structures from the perspective
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Verifiable (t, n) Secret Image Sharing Scheme Based on Slim Turtle Shell Matrix J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-24 Yijie Lin, Chia-Chen Lin, Jui‐Chuan Liu, Chin‐Chen Chang
As Internet technology advances rapidly, safeguarding confidential messages has become an increasingly pressing concern. Among the various schemes available, secret image sharing (SIS) has gained considerable attention. Particularly, the (t, n) secret sharing scheme offers robust protection for hidden secret messages. However, it remains challenging to expand the quality of t and n while maintaining
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SeMalBERT: Semantic-based malware detection with bidirectional encoder representations from transformers J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-26 Junming Liu, Yuntao Zhao, Yongxin Feng, Yutao Hu, Xiangyu Ma
Machine learning models are widely used for identifying malicious software. However, existing models suffer from issues such as imprecise polysemous representations and a lack of contextual semantic representations, leading to the failure to recognize certain types of malicious software. In this paper, we propose a semantic-based intelligent malware detection model called SeMalBERT for identifying
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Ciphertext face recognition system based on secure inner product protocol J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-23 Xuelian Li, Zhuohao Chen, Juntao Gao
User privacy data leakage is a major weakness of current plaintext face recognition systems. This article combines Pallier homomorphic encryption algorithm with inner product protocol and face recognition to construct a ciphertext face recognition system based on inner product protocol. Firstly, we integrate the Pallier homomorphic encryption algorithm into the inner product protocol process and design
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Batch image encryption using cross image permutation and diffusion J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-26 Wei Song, Chong Fu, Yu Zheng, Yanfeng Zhang, Junxin Chen, Peipei Wang
Chaotic encryption mostly has been proposed to protect a single image or batch images of identical size. Typically, it splices these images into a huge one, and later performs batch image encryption. Yet, this solution may not be feasible given arbitrary-size images, which could not be directly connected one by one. To address it, we propose an arbitrary-size encryption scheme via chaos, for efficiently
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An effective iris biometric privacy protection scheme with renewability J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-26 Wiraj Udara Wickramaarachchi, Dongdong Zhao, Junwei Zhou, Jianwen Xiang
Iris is considered a unique, robust biometric that can be used as an authentication factor. Privacy protection of individuals’ iris templates is an essential requirement. The essential attributes needed to ensure privacy of biometrics are irreversibility, renewability, and unlinkability. Information distortion is an effective way to achieve the privacy. However, this may lead to some degradation of
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An efficient feature selection and explainable classification method for EEG-based epileptic seizure detection J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-20 Ijaz Ahmad, Chen Yao, Lin Li, Yan Chen, Zhenzhen Liu, Inam Ullah, Mohammad Shabaz, Xin Wang, Kaiyang Huang, Guanglin Li, Guoru Zhao, Oluwarotimi Williams Samuel, Shixiong Chen
Epilepsy is a prevalent neurological disorder that poses life-threatening emergencies. Early electroencephalogram (EEG) seizure detection can mitigate the risks and aid in the treatment of patients with epilepsy. EEG based epileptic seizure (ES) detection has significant applications in epilepsy treatment and medical diagnosis. Therefore, this paper presents an innovative framework for efficient ES
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Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-22 Danial Shiraly, Ziba Eslami, Nasrollah Pakniat
The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In
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Residual networks for text-independent speaker identification: Unleashing the power of residual learning J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-18 Pooja Gambhir, Amita Dev, Poonam Bansal, Deepak Kumar Sharma, Deepak Gupta
The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication
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Autoencoder-based joint image compression and encryption J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-20 Benxuan Wang, Kwok-Tung Lo
Recently, learning-based methods have developed rapidly, with promising results in many areas. And deep learning models are expected to be the next-generation optimal image compression solutions. This paper proposes a new image compression and encryption framework that integrates encryption algorithms with a deep-learning compression network. Our work employs Auto-Encoder (AE) based compression network
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Enhancing logistic chaotic map for improved cryptographic security in random number generation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-22 Moatsum Alawida
The use of chaotic maps for cryptography is advantageous due to their desirable characteristics, including complexity, unpredictability, sensitivity to initial values and control parameters, and ergodicity. However, most chaos-based systems have limitations that make them impractical, such as low efficiency, insufficient security, and small keyspace. To overcome these challenges, this paper proposes
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Efficient public auditing scheme for non-administrator group with secure user revocation J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-18 Jinliang Chen, Tianqi Zhou, Sai Ji, Haowen Tan, Wenying Zheng
Cloud auditing, as an important part of cloud computing, ensures file security for users. To this end, many academic authors have conducted an enormous amount of research on cloud auditing. Unfortunately, existing schemes usually involve administrators who have greater power, and malicious administrators may abuse their authority. Thus, a large number of schemes have been designed to ensure users’
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Synergistic compensation for RGB-based blind color image watermarking to withstand JPEG compression J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-16 Hwai-Tsu Hu
Color image watermarking has achieved great success in applications such as copyright protection and content authentication. Nonetheless, color image watermarking individually implemented in RGB channels cannot survive JPEG compression attacks. This study introduces an auxiliary scheme that enables RGB-based watermarking methods to overcome this difficulty. The reason why a promising grayscale watermarking
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HSB based reversible data hiding using sorting and pairwise expansion J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-13 Ankit Kumar Saini, Samayveer Singh
Reversible data hiding (RDH) continues to advance in its applicability across diverse fields, including medical imaging, military applications, and cloud computing. In recent times, there has been a proliferation of RDH methods that focus on embedding in the higher significant bit (HSB) plane, as opposed to the least significant bit (LSB), in order to leverage the greater correlation, thereby achieving
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Approximate DBSCAN on obfuscated data J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-11 Payel Sadhukhan, Labani Halder, Sarbani Palit
With the emergence of remote storage, computation facilities, and the availability of high-speed data connectivity — cloud computation has become the call for the day. In this scenario, security and computability of data have emerged as two crucial aspects which often conflict with each other. An efficient solution is to build a trade-off between the two. In this work, we propose a novel Hilbert-Curve-based
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Trustworthy adaptive adversarial perturbations in social networks J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-11 Jiawei Zhang, Jinwei Wang, Hao Wang, Xiangyang Luo, Bin Ma
Deep neural networks have achieved excellent performance in various research and applications, but they have proven to be susceptible to adversarial examples. Generating adversarial examples can help identify the vulnerability of the deep neural networks and further enhance the robustness and reliability of these models. However, the existing adversarial attacks can hardly achieve the balance between
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Spatio-temporal representation learning enhanced source cell-phone recognition from speech recordings J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-08 Chunyan Zeng, Shixiong Feng, Zhifeng Wang, Xiangkui Wan, Yunfan Chen, Nan Zhao
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Toward identifying malicious encrypted traffic with a causality detection system J. Inf. Secur. Appl. (IF 5.6) Pub Date : 2023-12-05 ZengRi Zeng, Peng Xun, Wei Peng, BaoKang Zhao
The main methods for protecting user privacy and addressing cybersecurity problems caused by encrypted traffic are non-decryption detection approaches. However, these methods face problems such as the small number of trainable features and imbalanced training datasets, which seriously affect the robustness of existing malicious encrypted traffic detection systems (TDSs). To address these issues, we