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1.
Comput Intell Neurosci ; 2022: 6294058, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35498213

RESUMEN

The most often reported danger to computer security is malware. Antivirus company AV-Test Institute reports that more than 5 million malware samples are created each day. A malware classification method is frequently required to prioritize these occurrences because security teams cannot address all of that malware at once. Malware's variety, volume, and sophistication are all growing at an alarming rate. Hackers and attackers routinely design systems that can automatically rearrange and encrypt their code to escape discovery. Traditional machine learning approaches, in which classifiers learn based on a hand-crafted feature vector, are ineffective for classifying malware. Recently, deep convolutional neural networks (CNNs) successfully identified and classified malware. To categorize malware, a smart system has been suggested in this research. A novel model of deep learning is introduced to categorize malware families and multiclassification. The malware file is converted to a grayscale picture, and the image is then classified using a convolutional neural network. To evaluate the performance of our technique, we used a Microsoft malware dataset of 10,000 samples with nine distinct classifications. The findings stood out among the deep learning models with 99.97% accuracy for nine malware types.


Asunto(s)
Seguridad Computacional , Mano , Humanos , Aprendizaje Automático , Redes Neurales de la Computación , Extremidad Superior
2.
PLoS One ; 17(5): e0267908, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35511912

RESUMEN

With the development of cloud computing, interest in database outsourcing has recently increased. In cloud computing, it is necessary to protect the sensitive information of data owners and authorized users. For this, data mining techniques over encrypted data have been studied to protect the original database, user queries and data access patterns. The typical data mining technique is kNN classification which is widely used for data analysis and artificial intelligence. However, existing works do not provide a sufficient level of efficiency for a large amount of encrypted data. To solve this problem, in this paper, we propose a privacy-preserving parallel kNN classification algorithm. To reduce the computation cost for encryption, we propose an improved secure protocol by using an encrypted random value pool. To reduce the query processing time, we not only design a parallel algorithm, but also adopt a garbled circuit. In addition, the security analysis of the proposed algorithm is performed to prove its data protection, query protection, and access pattern protection. Through our performance evaluation, the proposed algorithm shows about 2∼25 times better performance compared with existing algorithms.


Asunto(s)
Nube Computacional , Privacidad , Algoritmos , Inteligencia Artificial , Seguridad Computacional
3.
Comput Intell Neurosci ; 2022: 7016554, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35510050

RESUMEN

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients' confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.


Asunto(s)
Seguridad Computacional , Telemedicina , Algoritmos , Nube Computacional , Humanos , Proyectos de Investigación
4.
Comput Intell Neurosci ; 2022: 6173185, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35510052

RESUMEN

The Internet of Things has become the third wave of the information industry and cloud computing, big data, and Internet technologies. Among the many identification technologies used in the Internet of Things, radiofrequency identification technology is undoubtedly one of the most popular methods today. It is replacing the traditional contact IC card and becoming a new trend of smart cards. At the same time, a large amount of data is generated in the IoT environment. A lot of data involve user privacy, and users do not have good control over these data. Collecting and utilizing these data on the basis of protecting user privacy have become an important problem to be solved urgently. With the implementation of the strategy of rejuvenating the country through science and education, major colleges and universities are developing rapidly through enrollment and expansion, which also brings inconvenience to campus security management. Although the traditional campus all-in-one card system can guarantee the security identity of people entering and leaving, it does not reasonably integrate and utilize this information, resulting in waste of information resources and, to a certain extent, the problem of user privacy leakage. To solve the above problems, a new system was developed to integrate resources to identify users. To protect the privacy data of Internet of Things users, a specific solution using blockchain technology is proposed; for the identity authentication problem of Internet of Things users, the identity authentication based on the public key address of the blockchain is used on the chain, and the group signature is used off the chain. The identity authentication method solves the contradiction between anonymity and traceability in blockchain application scenarios. The simulation results show that the system not only considers user privacy but also has extremely important practical significance for the promotion of Internet of Things and RF applications.


Asunto(s)
Cadena de Bloques , Internet de las Cosas , Algoritmos , Seguridad Computacional , Humanos , Internet , Privacidad
5.
Comput Intell Neurosci ; 2022: 1509000, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35535188

RESUMEN

In light of the continuous development of Internet technology, many problems involving network security based on the blockchain have also been observed gradually. In this paper, the existence forms of network security hazards based on the blockchain are explored to establish a network blockchain security sharing model based on fuzzy logic. In security sharing, network blockchains are often maintained by many parties. This poses new potential threats and challenges to privacy protection in these multiparty network blockchains. This paper proposes a research scheme for a network blockchain security sharing model based on fuzzy logic. The tampering of the protected network blockchain is prevented by blockchain, and the fuzzy logic algorithm is used to ensure its confidentiality. This solution allows the exchange in the protected network blockchain and the security of transaction information based on the fuzzy logic algorithm. According to the results of the experiments, this method can ensure security sharing of the network blockchain with high practicality and achieve the expected design effect.


Asunto(s)
Cadena de Bloques , Seguridad Computacional , Confidencialidad , Lógica Difusa , Privacidad
6.
Comput Intell Neurosci ; 2022: 3406228, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35535195

RESUMEN

To ensure the security of data transmission and recording in Internet environment monitoring systems, this paper proposes a study of a secure method of blockchain data transfer based on homomorphic encryption. Blockchain data transmission is realized through homomorphic encryption. Homomorphic encryption can not only encrypt the original data, but also ensure that the data result after decrypting the data is the same as the original data. The asymmetric encrypted public key is collected by Internet of things (IoT) equipment to realize the design of blockchain data secure transmission method based on homomorphic encryption. The experimental results show that the accuracy of the first transmission is as high as 88% when using the transmission method in this paper. After several experiments, the transmission accuracy is high by using the design method in this paper. In the last test, the transmission accuracy is still 88%, and the data transmission effect is relatively stable. At the same time, compared to the management method used in this article, the transfer method used in this paper is more reliable than the original transfer method and is not prone to data distortion. It can be seen that this method has high transmission accuracy and short transmission time, which effectively avoids the data tampering caused by too long time in the transmission process.


Asunto(s)
Cadena de Bloques , Seguridad Computacional
7.
JMIR Mhealth Uhealth ; 10(5): e33735, 2022 May 06.
Artículo en Inglés | MEDLINE | ID: mdl-35522465

RESUMEN

BACKGROUND: Women's mobile health (mHealth) is a growing phenomenon in the mobile app global market. An increasing number of women worldwide use apps geared to female audiences (female technology). Given the often private and sensitive nature of the data collected by such apps, an ethical assessment from the perspective of data privacy, sharing, and security policies is warranted. OBJECTIVE: The purpose of this scoping review and content analysis was to assess the privacy policies, data sharing, and security policies of women's mHealth apps on the current international market (the App Store on the Apple operating system [iOS] and Google Play on the Android system). METHODS: We reviewed the 23 most popular women's mHealth apps on the market by focusing on publicly available apps on the App Store and Google Play. The 23 downloaded apps were assessed manually by 2 independent reviewers against a variety of user data privacy, data sharing, and security assessment criteria. RESULTS: All 23 apps collected personal health-related data. All apps allowed behavioral tracking, and 61% (14/23) of the apps allowed location tracking. Of the 23 apps, only 16 (70%) displayed a privacy policy, 12 (52%) requested consent from users, and 1 (4%) had a pseudoconsent. In addition, 13% (3/23) of the apps collected data before obtaining consent. Most apps (20/23, 87%) shared user data with third parties, and data sharing information could not be obtained for the 13% (3/23) remaining apps. Of the 23 apps, only 13 (57%) provided users with information on data security. CONCLUSIONS: Many of the most popular women's mHealth apps on the market have poor data privacy, sharing, and security standards. Although regulations exist, such as the European Union General Data Protection Regulation, current practices do not follow them. The failure of the assessed women's mHealth apps to meet basic data privacy, sharing, and security standards is not ethically or legally acceptable.


Asunto(s)
Aplicaciones Móviles , Telemedicina , Seguridad Computacional , Femenino , Humanos , Difusión de la Información , Políticas , Privacidad
8.
Comput Intell Neurosci ; 2022: 2254411, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35528363

RESUMEN

Adding the adequate level of security of information systems dealing with sensitive data, privacy, or defense systems involves some form of access control. The audits performed are dealing with the determination of the allowed activities of the legal users, when attempting to access resources of the system. Usually, full access is provided after the user has been successfully authenticated through an authentication mechanism (e.g., password), while the corresponding authorization control is based on the confidentiality level of the respective resources and the authorization level assigned to each user. A very important diversification occurring in modern digital technologies is related to the identification based on blockchain technology, which is presented as a public, distributed data series, unable to modify its history and grouped in time-numbered blocks. In this work, a blockchain-based verifiable user data access control policy for secured cloud data storage is suggested for a version associated with big data in health care. It is an innovative system of applying classified access policies to secure resources in the cloud, which operates based on blockchain technology. System evaluation is carried out by studying a case in its resilience to Eclipse attack under different malicious user capabilities for routing table poisoning.


Asunto(s)
Cadena de Bloques , Nube Computacional , Seguridad Computacional , Almacenamiento y Recuperación de la Información , Políticas
9.
PLoS One ; 17(4): e0266916, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35421184

RESUMEN

The lack of data outsourcing in healthcare management systems slows down the intercommunication and information sharing between different entities. A standard solution is outsourcing the electronic health record (EHR) to a cloud service provider (CSP). The outsourcing of the EHR should be performed securely without compromising the CSP functionalities. Searchable encryption would be a viable approach to ensure the confidentiality of the data without compromising searchability and accessibility. However, most existing searchable encryption solutions use centralised architecture. These systems have trust issues as not all the CSPs are fully trusted or honest. To address these problems, we explore blockchain technology with smart contract applications to construct a decentralised system with auditable yet immutable data storage and access. First, we propose a blockchain-based searchable encryption scheme for EHR storage and updates in a decentralised fashion. The proposed scheme supports confidentiality of the outsourced EHR, keyword search functionalities, verifiability of the user and the server, storage immutability, and dynamic updates of EHRs. Next, we implement a prototype using JavaScript and Solidity on the Ethereum platform to demonstrate the practicality of the proposed solution. Finally, we compare the performance and security of the proposed scheme against existing solutions. The result indicates that the proposed scheme is practical while providing the desired security features and functional requirements.


Asunto(s)
Cadena de Bloques , Nube Computacional , Seguridad Computacional , Confidencialidad , Atención a la Salud , Registros Electrónicos de Salud
10.
PLoS One ; 17(4): e0266462, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35404955

RESUMEN

Blockchain technology (BCT) has emerged in the last decade and added a lot of interest in the healthcare sector. The purpose of this systematic literature review (SLR) is to explore the potential paradigm shift in healthcare utilizing BCT. The study is compiled by reviewing research articles published in nine well-reputed venues such as IEEE Xplore, ACM Digital Library, Springs Link, Scopus, Taylor & Francis, Science Direct, PsycINFO, Ovid Medline, and MDPI between January 2016 to August 2021. A total of 1,192 research studies were identified out of which 51 articles were selected based on inclusion criteria for this SLR that presents the modern information on the recent implications and gaps in the use of BCT for enhancing the healthcare procedures. According to the outcomes, BCT is being applied to design the novel and advanced interventions to enrich the current protocol of managing, distributing, and processing clinical records and personal medical information. BCT is enduring the conceptual development in the healthcare domain, where it has summed up the substantial elements through better and enhanced efficiency, technological innovation, access control, data privacy, and security. A framework is developed to address the probable field where future researchers can add considerable value, such as data protection, system architecture, and regulatory compliance. Finally, this SLR concludes that the upcoming research can support the pervasive implementation of BCT to address the critical dilemmas related to health diagnostics, enhancing the patient healthcare process in remote monitoring or emergencies, data integrity, and avoiding fraud.


Asunto(s)
Cadena de Bloques , Seguridad Computacional , Atención a la Salud , Instituciones de Salud , Humanos , Tecnología
11.
Comput Intell Neurosci ; 2022: 5866922, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35463229

RESUMEN

This paper discusses the development of new hardware and software for protecting access to HMI/SCADA systems via Unprotected Internet Networks (UPN), mainly when working remotely with confidential information. Based on the analysis carried out, it is shown that the existing vulnerabilities can be exploited by cybercriminals to steal passwords and user authentication logins. Modern protection technologies based on the OTP method have been investigated. Moreover, a new concept of information security for user authentication in UPNs when working with information remotely is proposed. The structure of the electronic key and the connection diagram based on the selected hardware modules have been developed. In addition, the two-level user identification algorithms and the firmware program code for the ATmega32U4 microcontroller are considered. Finally, to show the reliability and stability of the of the developed electronic user authentication key against any unexpected software hacking, a number of experiments have been performed.


Asunto(s)
Seguridad Computacional , Confidencialidad , Electrónica , Internet , Reproducibilidad de los Resultados
12.
Comput Intell Neurosci ; 2022: 4788031, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35463282

RESUMEN

The recent advent of cloud computing provides a flexible way to effectively share data among multiple users. Cloud computing and cryptographic primitives are changing the way of healthcare unprecedentedly by providing real-time data sharing cost-effectively. Sharing various data items from different users to multiple sets of legitimate subscribers in the cloud environment is a challenging issue. The online electronic healthcare system requires multiple data items to be shared by different users for various purposes. In the present scenario, COVID-19 data is sensitive and must be encrypted to ensure data privacy. Secure sharing of such information is crucial. The standard broadcast encryption system is inefficient for this purpose. Multichannel broadcast encryption is a mechanism that enables secure sharing of different messages to different set of users efficiently. We propose an efficient and secure data sharing method with shorter ciphertext in public key setting using asymmetric (Type-III) pairings. The Type-III setting is the most efficient form among all pairing types regarding operations required and security. The semantic security of this method is proven under decisional BDHE complexity assumption without random oracle model.


Asunto(s)
COVID-19 , Seguridad Computacional , Nube Computacional , Atención a la Salud , Humanos , Difusión de la Información
13.
Comput Intell Neurosci ; 2022: 7280695, 2022.
Artículo en Inglés | MEDLINE | ID: mdl-35463284

RESUMEN

In the context of the Internet of Things, user privacy leads to the lack of information security and the obscuration of traditional privacy concepts. Therefore, how to ensure information security and protect user privacy is a key issue that enterprises must solve in the process of using the Internet of Things technology. At present, the research on corporate employees' protection of user privacy in the context of the Internet of Things is mostly focused on the technical level, while the legal and management levels are relatively lacking. Based on the definition of the corporate concept in the context of the Internet of Things, this paper uses management and psychology as the research perspective, and based on theory of persuasion, adjustment orientation theory, and reinforcement theory, it discusses the attitudes of corporate employees to protect user privacy in the context of the Internet of Things and behaviour mechanism, constructing a new theoretical model. This experiment uses 0.001 as the step size to change the corresponding threshold size. The interval range is [0.001, 10], and there are a total of 10,000 points in the interval, which is equivalent to 100 million sensor attack tests. According to the above method, 10,000 points of the ROC curve can be obtained by using 10,000 thresholds, and the corresponding ROC curve can be drawn in the coordinate graph, which can intuitively reflect the performance of the VRADS vehicle anomaly real-time detection system. The challenge of data information protection is analyzed, trying to clarify the ideas for the protection of personal data and information in the Internet of Things environment and even lead to employees' rebellious psychology. This article proves that the pertinence and effectiveness of the persuasive content have a positive impact on employees' attitudes towards privacy protection, and it has been further deepened in the context of the Internet of Things. The balance point is to leave enough room for the long-term sustainable development of the Internet of Things industry on the basis of protecting the personal rights and interests of users.


Asunto(s)
Internet de las Cosas , Privacidad , Macrodatos , Seguridad Computacional , Recolección de Datos/métodos , Internet
14.
Lancet Digit Health ; 4(5): e297-e298, 2022 05.
Artículo en Inglés | MEDLINE | ID: mdl-35367192
16.
Hautarzt ; 73(5): 391-397, 2022 May.
Artículo en Alemán | MEDLINE | ID: mdl-35471235

RESUMEN

Digital health applications represent a new form of care. The basis for the approval of digital health applications is the Digital Healthcare Act. In order to be included in the directory, the digital health applications must undergo an extensive evaluation process by the Federal Institute for Drugs and Medical Devices. The focus is on proving added value for care, but also on the technical aspects. This strictly differentiates the digital health applications from the health apps. Cutting-edge apps enable a simple output of collected data to make doctor-patient interactions efficient. Appropriate remuneration and education could increase the acceptance by the medical profession and thus accelerate implementation; however, such instruments and incentives are not currently provided for in the system.


Asunto(s)
Aplicaciones Móviles , Seguridad Computacional , Atención a la Salud , Humanos
17.
Med Biol Eng Comput ; 60(6): 1585-1594, 2022 Jun.
Artículo en Inglés | MEDLINE | ID: mdl-35389195

RESUMEN

It is important to ensure the privacy and security of the medical images that are produced with electronic health records. Security is ensured by encrypting and transmitting the electronic health records, and privacy is provided according to the integrity of the data and the decryption of data with the user role. Both the security and privacy of medical images are provided with the innovative use of lightweight cryptology (LWC) and Walsh-Hadamard transform (WHT) in this study. Unlike the light cryptology algorithm used in encryption, the hex key in the algorithm is obtained in two parts. The first part is used as the public key and the second part as the user-specific private key. This eliminated the disadvantage of the symmetric encryption algorithm. After the encryption was performed with a two-part hex key, the Walsh-Hadamard transform was applied to the encrypted image. In the Walsh-Hadamard transform, the Hadamard matrix was rotated with certain angles according to the user role. This allowed the encoded medical image to be obtained as a vector. The proposed method was verified with the results of the number of pixel change rates and unified average changing intensity measurement parameters and histogram analysis. The results showed that the method is more successful than the lightweight cryptology method and the proposed methods in the literature to solve security and privacy of the data in medical applications with user roles.


Asunto(s)
Seguridad Computacional , Privacidad , Algoritmos
18.
BMC Genomics ; 23(1): 284, 2022 Apr 09.
Artículo en Inglés | MEDLINE | ID: mdl-35395714

RESUMEN

BACKGROUND: Disclosure of patients' genetic information in the process of applying machine learning techniques for tumor classification hinders the privacy of personal information. Homomorphic Encryption (HE), which supports operations between encrypted data, can be used as one of the tools to perform such computation without information leakage, but it brings great challenges for directly applying general machine learning algorithms due to the limitations of operations supported by HE. In particular, non-polynomial activation functions, including softmax functions, are difficult to implement with HE and require a suitable approximation method to minimize the loss of accuracy. In the secure genome analysis competition called iDASH 2020, it is presented as a competition task that a multi-label tumor classification method that predicts the class of samples based on genetic information using HE. METHODS: We develop a secure multi-label tumor classification method using HE to ensure privacy during all the computations of the model inference process. Our solution is based on a 1-layer neural network with the softmax activation function model and uses the approximate HE scheme. We present an approximation method that enables softmax activation in the model using HE and a technique for efficiently encoding data to reduce computational costs. In addition, we propose a HE-friendly data filtering method to reduce the size of large-scale genetic data. RESULTS: We aim to analyze the dataset from The Cancer Genome Atlas (TCGA) dataset, which consists of 3,622 samples from 11 types of cancers, genetic features from 25,128 genes. Our preprocessing method reduces the number of genes to 4,096 or less and achieves a microAUC value of 0.9882 (85% accuracy) with a 1-layer shallow neural network. Using our model, we successfully compute the tumor classification inference steps on the encrypted test data in 3.75 minutes. As a result of exceptionally high microAUC values, our solution was awarded co-first place in iDASH 2020 Track 1: "Secure multi-label Tumor classification using Homomorphic Encryption". CONCLUSIONS: Our solution is the first result of implementing a neural network model with softmax activation using HE. Also, HE optimization methods presented in this work enable machine learning implementation using HE or other challenging HE applications.


Asunto(s)
Seguridad Computacional , Privacidad , Algoritmos , Estudio de Asociación del Genoma Completo , Humanos , Redes Neurales de la Computación
19.
Sensors (Basel) ; 22(7)2022 Mar 29.
Artículo en Inglés | MEDLINE | ID: mdl-35408219

RESUMEN

Security has always been the main concern for the internet of things (IoT)-based systems. Blockchain, with its decentralized and distributed design, prevents the risks of the existing centralized methodologies. Conventional security and privacy architectures are inapplicable in the spectrum of IoT due to its resource constraints. To overcome this problem, this paper presents a Blockchain-based security mechanism that enables secure authorized access to smart city resources. The presented mechanism comprises the ACE (Authentication and Authorization for Constrained Environments) framework-based authorization Blockchain and the OSCAR (Object Security Architecture for the Internet of Things) object security model. The Blockchain lays out a flexible and trustless authorization mechanism, while OSCAR makes use of a public ledger to structure multicast groups for authorized clients. Moreover, a meteor-based application is developed to provide a user-friendly interface for heterogeneous technologies belonging to the smart city. The users would be able to interact with and control their smart city resources such as traffic lights, smart electric meters, surveillance cameras, etc., through this application. To evaluate the performance and feasibility of the proposed mechanism, the authorization Blockchain is implemented on top of the Ethereum network. The authentication mechanism is developed in the node.js server and a smart city is simulated with the help of Raspberry Pi B+. Furthermore, mocha and chai frameworks are used to assess the performance of the system. Experimental results reveal that the authentication response time is less than 100 ms even if the average hand-shaking time increases with the number of clients.


Asunto(s)
Cadena de Bloques , Internet de las Cosas , Ciudades , Seguridad Computacional , Humanos , Confianza
20.
Sensors (Basel) ; 22(8)2022 Apr 09.
Artículo en Inglés | MEDLINE | ID: mdl-35458871

RESUMEN

Heterogeneous cyberattacks against industrial control systems (ICSs) have had a strong impact on the physical world in recent decades. Connecting devices to the internet enables new attack surfaces for attackers. The intrusion of ICSs, such as the manipulation of industrial sensory or actuator data, can be the cause for anomalous ICS behaviors. This poses a threat to the infrastructure that is critical for the operation of a modern city. Nowadays, the best techniques for detecting anomalies in ICSs are based on machine learning and, more recently, deep learning. Cybersecurity in ICSs is still an emerging field, and industrial datasets that can be used to develop anomaly detection techniques are rare. In this paper, we propose an unsupervised deep learning methodology for anomaly detection in ICSs, specifically, a lightweight long short-term memory variational auto-encoder (LW-LSTM-VAE) architecture. We successfully demonstrate our solution under two ICS applications, namely, water purification and water distribution plants. Our proposed method proves to be efficient in detecting anomalies in these applications and improves upon reconstruction-based anomaly detection methods presented in previous work. For example, we successfully detected 82.16% of the anomalies in the scenario of the widely used Secure Water Treatment (SWaT) benchmark. The deep learning architecture we propose has the added advantage of being extremely lightweight.


Asunto(s)
Aprendizaje Automático , Memoria a Corto Plazo , Seguridad Computacional , Memoria a Largo Plazo , Factores de Tiempo
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