 Subjects -> ELECTRONICS (Total: 207 journals)
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- Retracted: Research on Moral Education Function of Music Art in College
Students Based on Bayesian Learning Algorithm-
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PubDate: Mon, 06 Mar 2023 14:50:01 +000
- Fed-DNN-Debugger: Automatically Debugging Deep Neural Network Models in
Federated Learning-
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Abstract: Federated learning is a distributed machine learning framework that has been widely applied in scenarios that require data privacy. To obtain a neural network model that performs well, when the model falls into a bug, existing solutions retrain it on a larger training dataset or the carefully selected samples from model diagnosis. To overcome this challenge, this paper presents Fed-DNN-Debugger, which can automatically and efficiently fix DNN models in federated learning. Fed-DNN-Debugger fixes the federated model by fixing each client model. Fed-DNN-Debugger consists of two modules for debugging a client model: nonintrusive metadata capture (NIMC) and automated neural network model debugging (ANNMD). NIMC collects the metadata with deep learning software syntax automatically. It does not insert any code for metadata collection into modeling scripts. ANNMD scores samples according to metadata and searches for high-quality samples. Models are retrained on the selected samples to repair their weights. Our experiments with popular federated models show that Fed-DNN-Debugger can improve the test accuracy by 8% by automatically fixing models.
PubDate: Thu, 23 Feb 2023 08:20:00 +000
- Enhanced Multiset Consensus Protocol Based on PBFT for Logistics
Information Traceability-
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Abstract: In the recent years, the global logistics industry has greatly driven the development of the world economy. At the same time, a large amount of data information is generated. Due to the frequent occurrence of logistics information leakage and forgery, it is necessary to find solutions that can accurately trace logistics information and ensure the security and authenticity of logistics information. The birth of blockchain technology has transformed the logistics industry from quantitative change to qualitative change. The technical characteristics of blockchain technology, such as distributed storage ideas, decentralization, immutability, and complex encryption consensus algorithm, endow it with a wide range of application prospects in the logistics industry. This paper proposes an enhanced multiset consensus algorithm based on PBFT (practical Byzantine fault tolerance) for logistics information traceability and storage on the logistics blockchain. The application of the proposed multi-set consensus algorithm in the topology structure composed of multiple sets can improve the consensus efficiency of logistics information in the blockchain. We improve consensus capability and transaction speed, avoid redundant consensus message packets occupying a large bandwidth, and efficiently process logistics information generated at any time. We ensure the traceability of logistics information and achieve efficient and accurate traceability, and the efficiency and security of the proposed algorithm are analyzed. This paper aims to solve the problems of traceability, trustworthiness, and efficient processing of blockchain applications in logistics information to operate the logistics network efficiently. This paper compares the proposed algorithm with the PBFT-related expansion algorithm regarding bandwidth occupation, delay, and throughput. The results show that the MPBFT consensus algorithm significantly improves the efficiency of the logistics blockchain network.
PubDate: Wed, 22 Feb 2023 15:20:01 +000
- Toward High Capacity and Robust JPEG Steganography Based on Adversarial
Training-
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Abstract: JPEG steganography has become a research hotspot in the field of information hiding. However, the capacity of conventional JPEG steganography methods is hard to meet the requirements in high-capacity application scenarios and also can not extract secret messages accurately after JPEG compression. To mitigate these problems, we propose a high-capacity and robust JPEG steganography based on adversarial training called HRJS, which implements an end-to-end framework in the JPEG domain for the first time. The encoder is responsible for embedding the secret message while the decoder can reconstruct the original secret message. To enhance robustness, an attack module forces the neural network to automatically learn how to correctly recover the secret message after an attack. Experimental results show that our method achieves near 100 decoding accuracy against JPEG_50 compression at 1/3 bits per channel (bpc) payload while preserving the imperceptibility of the stego image. Compared with conventional JPEG steganography methods, the proposed method is feasible with high capacity (e.g., 1 bpc) and has an obvious advantage in terms of robustness against JPEG compression at the same time.
PubDate: Tue, 21 Feb 2023 15:35:00 +000
- FHAAPS: Efficient Anonymous Authentication with Privacy Preservation
Scheme for Farm-to-Home Communication-
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Abstract: With the advancement in information and communication technology (ICT), secure farm-to-home communication has become an emerging concept. Food is the most basic essential commodity for the survival of human beings which is produced by farmers. However, because of the presence of intermediaries, farmers/producers do not make a sufficient profit and also the consumers have to pay more money to buy food items from these mediators. As a result, in this work, we proposed an efficient farm-to-home anonymous authentication privacy-preserving scheme in which the storehouse will buy goods from farmers and sell them to the consumers at the base rate. Moreover, in our scheme, an unprofitable trusted delivery agent assists in the transfer of food commodities between end users and storehouses to provide maximum profit for the farmers and consumers. Further, essential security parameters are provided to the end users and it provides conditional privacy to the delivery agent; i.e., if any mishap occurs, then the malicious delivery agent’s privacy is revoked to avoid further damage to the system. The performance analysis section shows that our scheme supports the transfer of food commodities with minimum computational and communication costs.
PubDate: Tue, 21 Feb 2023 15:20:00 +000
- Retracted: Study on Fault Diagnosis Method and Application of Automobile
Power Supply Based on Fault Tree-Bayesian Network-
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PubDate: Sun, 19 Feb 2023 09:05:00 +000
- Cyber Security against Intrusion Detection Using Ensemble-Based Approaches
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Abstract: The attacks of cyber are rapidly increasing due to advanced techniques applied by hackers. Furthermore, cyber security is demanding day by day, as cybercriminals are performing cyberattacks in this digital world. So, designing privacy and security measurements for IoT-based systems is necessary for secure network. Although various techniques of machine learning are applied to achieve the goal of cyber security, but still a lot of work is needed against intrusion detection. Recently, the concept of hybrid learning gives more attention to information security specialists for further improvement against cyber threats. In the proposed framework, a hybrid method of swarm intelligence and evolutionary for feature selection, namely, PSO-GA (PSO-based GA) is applied on dataset named CICIDS-2017 before training the model. The model is evaluated using ELM-BA based on bootstrap resampling to increase the reliability of ELM. This work achieved highest accuracy of 100% on PortScan, Sql injection, and brute force attack, which shows that the proposed model can be employed effectively in cybersecurity applications.
PubDate: Sat, 18 Feb 2023 06:50:00 +000
- Performance Analysis of IEEE 802.11p Protocol in IoV under Error-Prone
Channel Conditions-
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Abstract: The complexity of the channel condition in the Internet of vehicles (IoV) may increase the bit error rate (BER) of the intelligent vehicle terminals, resulting in data transmission failures or errors. Therefore, it is necessary to improve the performance of communication protocols under error-prone channel conditions. This article investigates the influence of the access categories’ (ACs) performance with channel errors by using four to cause service differentiation based on the enhanced distributed channel access (EDCA) mechanism of the IEEE 802.11p. To address the error-prone characteristics of the channel and unsaturated traffic conditions, a three-dimensional Markov model is developed first, followed by an analysis of the characteristics and mutual transition probabilities of the seven classes of states in the model, and then, the steady-state equations of the system are developed to derive the steady-state distribution to study system performance. The model considers the backoff phase, the freezing of the backoff counter, the retransmission limit, the probability of collisions occurring, the size of the maximum and minimum contention window, and the number of interframe intervals. These parameters are chosen to meet the requirements for the protocol to operate, while also preventing overestimation of throughput and avoiding having packets being served all the time. We derive expressions for the throughput and delay of under the conditions of error channels and unsaturated traffic. The impact of channel errors on the throughput and delay of is evaluated by numerical simulation. Numerical results show that too many stations in the system will increase the average access delay and decrease throughput. The increase in BER will seriously decrease the performance of high-priority . The throughput of the is also modeled to vary with frame length and BER, and the variation curves of the optimal frame lengths for and were obtained.
PubDate: Wed, 15 Feb 2023 06:50:00 +000
- Traffic Safety Oriented Multi-Intersection Flow Prediction Based on
Transformer and CNN-
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Abstract: Intelligent traffic signal control is one of the important means to ensure traffic safety. Effective signal control can make traffic flow fast and smooth, which first needs current and future traffic information. As the control of one intersection may affect adjacent intersections, this paper proposes to predict future traffic flow with consideration of multi-intersections. It can dynamically improve traffic conditions and reduce traffic congestion. Based on various nonlinear spatial relationships at correlated multi-intersections and the potential time-dependent relationship in traffic volume, a traffic flow prediction method named CNNformer which combines transformer with CNN, is proposed. The convolution neural network (CNN) and transformer are used to extract the spatial and temporal features of correlated multiple intersections. The learnable time code is embedded into transformer’s location code, and the location information and time information are injected into the model to help it learn the time characteristics of traffic volume. This study also considers the impact of cyclical traffic flow pattern and proposes CNNformer+. In the method, all of the data from the previous time window, as well as the data from the prior week and month, are correspondingly entered into the network. Finally, the output is generated through average pooling, realizing the learning of cyclical traffic flow characteristics. In the experiment, CNNformer+ and the state-of-the-art traffic flow prediction methods are compared using simulated data. The results show that the proposed model outperforms the existing models in prediction accuracy and efficiency.
PubDate: Wed, 15 Feb 2023 02:50:00 +000
- Retracted: An Intelligent Security Classification Model of Driver’s
Driving Behavior Based on V2X in IoT Networks-
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PubDate: Tue, 14 Feb 2023 10:50:01 +000
- Blockchain-Based Cyber Threat Intelligence Sharing Using Proof-of-Quality
Consensus-
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Abstract: Cyber threat intelligence (CTI) is contextualised knowledge, built on information that is collected, processed, analysed, and disseminated to the right audience, in order to comprehend a malicious threat actor’s motivation, goals, objectives, targets, and attack behaviours. The CTI value increases by the ability to be shared, consumed, and actioned timely, by the right stakeholders, based always on quality standards and parameters, which boost the cyber security community to understand how adversaries act and to counter the constantly emerging sophisticated cyber threats. In this article, along with the identification of research gaps, after a comparison between existing research studies in the similar scope of CTI evaluation and sharing mechanisms, we propose a blockchain-based cyber threat intelligence system architecture, which collects, evaluates, stores, and shares CTI, enabling tamper-proof data and exclusion of untrustworthy evaluation peers, while evaluating, at the same time, the quality of CTI Feeds against a defined set of quality standards. The evaluation of the data is performed utilising a reputation and trust-based mechanism for selecting validators, who further rate the CTI feeds using quality-based CTI parameters, while the consensus for preserving the fairness of the results and their final storage is performed via the recently introduced proof-of-quality (PoQ) consensus algorithm. The data, which are stored in the proposed ledger, constitute a reliable, distributed, and secure repository of CTI Feeds and contain their objective evaluation, as well as the performance of the validators who participated in each evaluation, while these data can be further used for assessing the reputation of CTI Sources. Finally, in order to assess the proposed system’s reliability, integrity, and tolerance against malicious activities, the model is subject to a theoretical analysis using a probabilistic simulation, taking into account various aspects and features of the integrated mechanisms. The results show that the tolerance against malicious validators is acceptable, even when the ratio between legitimately vs. maliciously behaving validators is 1 : 50.
PubDate: Mon, 13 Feb 2023 06:05:00 +000
- Retracted: Multiobjective Optimization of Airport Ferry Vehicle Scheduling
during Peak Hours Based on NSGA-II-
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PubDate: Sun, 12 Feb 2023 12:20:00 +000
- Retracted: Computational Technologies for Pakistani Consumers’
Understanding of the Country-of-Origin Label for Fruit and Vegetable
Products in Social Networks-
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PubDate: Thu, 09 Feb 2023 17:20:01 +000
- Retracted: Construction Principles of Physical Fitness Training Objective
System Based on Machine Learning and Data Mining-
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PubDate: Thu, 09 Feb 2023 16:20:01 +000
- Retracted: Description of Quantum Mechanics as a Branch of Mathematical
Physics That Deals with the Emission and Absorption of Energy by Matter-
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PubDate: Thu, 09 Feb 2023 10:20:00 +000
- Retracted: Fuzzy Testing Method of CAN Bus of Charging Pile Based on
Genetic Algorithm-
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PubDate: Wed, 08 Feb 2023 19:50:00 +000
- Retracted: Research on Digital Representation of Xiaopi Kiln Ceramic Art
Design Based on Computer-Aided Technology and IoT Network-
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PubDate: Wed, 08 Feb 2023 11:35:00 +000
- Retracted: Hierarchical Network Security Measurement and Optimal Proactive
Defense in Cloud Computing Environments-
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PubDate: Wed, 08 Feb 2023 10:05:01 +000
- A Privacy-Preserving Authentication Scheme for VANETs with Exculpability
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Abstract: Message authentication and conditional privacy preservation are two critical security issues in VANETs (vehicular ad hoc networks). To achieve the corresponding security goals, many security technologies have been proposed so far. Identity-based pseudonyms and group signature-based schemes are two of the main technologies in recently published literature. However, the key escrow is difficult to achieve and pseudonym identities may reveal the physical location of the vehicle in the identity-based scheme. The global manager TA of VANETs knows the full keys given to the vehicles and can forge signatures under the vehicle’s key. Therefore, the exculpability cannot be satisfied in the group signature scheme. To address these security issues, a privacy-preserving authentication scheme for VANETs with exculpability is proposed in this paper, which applies double key approach to realize the trusted communication between vehicle and road side units and TA by combining the advantage of group-based methods and identity-based methods. Security analysis shows that the security of our scheme can resist stronger attacks than previous schemes.
PubDate: Tue, 07 Feb 2023 10:50:00 +000
- Retracted: Blockchain-Based Dangerous Driving Map Data Cognitive Model in
5G-V2X for Smart City Security-
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PubDate: Tue, 07 Feb 2023 10:05:01 +000
- Erratum to “Research on the Teaching Method of Environmental Landscape
Design Practice Based on IoT Network and Digital VR Technology”-
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PubDate: Tue, 07 Feb 2023 07:35:00 +000
- A Generalized Blockchain-Based Government Data Sharing Protocol
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Abstract: In order to catch the express train of the digital age and seize the opportunities brought by the development of blockchain technology, many government departments have begun to build blockchain-based data sharing protocols. Most existing data sharing protocols are built on different blockchains with different specific features. The interaction between them is not trivial, leading to the phenomenon of “data islands.” Therefore, we consider building a data sharing protocol compatible with various blockchains. In this work, we propose a generalized blockchain-based data sharing protocol, which takes fairness, privacy, auditability, and generality into account simultaneously. With adaptor signature and zero-knowledge techniques, the proposed protocol ensures a secure and fair data sharing process and is compatible with various blockchains since it only requires the underlying blockchain to perform signature verification. Finally, we implement our construction on an Ethereum test network and conduct a series of experiments. The results demonstrate the practicality of our construction while remaining good functionalities.
PubDate: Mon, 06 Feb 2023 08:20:00 +000
- Retracted: Application of Data Mining Algorithm in Agricultural Products
Logistics Network Planning-
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PubDate: Sun, 05 Feb 2023 14:35:00 +000
- Retracted: Application of Grammar Error Detection Method for English
Composition Based on Machine Learning-
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PubDate: Sun, 05 Feb 2023 11:20:00 +000
- Retracted: Research on Intelligent Recommendation Model of E-Commerce
Commodity Based on Feature Selection and Deep Belief Network-
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PubDate: Thu, 02 Feb 2023 21:05:00 +000
- Retracted: Data Integration Method Design of Decision Spatial Information
System-
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PubDate: Thu, 02 Feb 2023 14:20:01 +000
- Retracted: Analysis of Stock Market Opening and Environment Protecting
Information with Computational Technologies: Evidence from China-
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PubDate: Thu, 02 Feb 2023 14:20:00 +000
- SEMDA: Secure and Efficient Multidimensional Data Aggregation in Smart
Grid without a Trusted Third Party-
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Abstract: Smart grids are a combination of traditional power system engineering as well as information and communications technology. Smart grid terminals provide convenient services to users by aggregating their data in real time. However, terminals can derive user privacy information from real-time data on smart devices. Therefore, security data aggregation has been widely studied in the field of smart grid. Most existing schemes are one-dimensional data aggregation or rely on a trusted third party. In reality, multidimensional data (such as a user’s electricity consumption or user’s main usage time, etc.) makes sense for terminals to flexibly adjust supply and demand strategies. In this paper, we propose an efficient and secure multidimensional data aggregation scheme that supports batch validation without a trusted third party. Firstly, we apply the Chinese remainder theorem to encode the user’s multidimensional data and realize the independence of each dimension in terminal decryption. Secondly, we adopt a secure key negotiation protocol that does not require a trusted third party. Finally, based on paillier homomorphic encryption and bilinear pairing, we construct an encryption scheme that can reuse the key and blind factor and support batch verification. The analysis results show that our scheme is secure for users’ privacy protection. Experimental results show that, compared with existing 1 dimensional aggregation schemes, our scheme has almost no growth in computational overhead for terminal decryption.
PubDate: Wed, 01 Feb 2023 09:05:00 +000
- TZEAMM: An Efficient and Secure Active Measurement Method Based on
TrustZone-
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Abstract: With the rapid development of computer and communication technology, embedded systems are widely used in smart devices. The increasing connectivity of these systems and the difficulties in providing comprehensive security have made such devices vulnerable to malicious attacks. Passive defense technologies and traffic-based intrusion detection technologies are not fully effective against such attacks. Trusted execution environment (TEE) technology can ensure system security against unknown attacks to some extent. Most researchers use TrustZone to implement TEE. However, the problem is that the API interface of the TEE module which provides the service is not secure. Therefore, to actively defend against attacks, we developed a trusted computing active measurement architecture based on TrustZone. To overcome the serious problem that modules in the trusted execution environment need to be passively invoked to provide services, we have proposed an active measurement closed-loop immune mechanism. To reduce the trusted computing base and reduce the performance overhead, we removed certain functional modules from the trusted execution environment. In addition, based on this architecture, we developed a trust chain and dynamic measurement method to ensure the security of the target applications. We changed the traditional attack response method, which requires the entire system to be restarted after an attack, by developing a fallback mechanism that is more suitable for the system. Finally, we verified the effectiveness of the architecture by developing an attack model. Performance testing and analysis showed that the architecture reduced the impact of the security mechanisms on the system. In the future, we will extend our research to more fine-grained measurements.
PubDate: Tue, 31 Jan 2023 01:35:00 +000
- pMATE: A Privacy-Preserving Map Retrieval Task Assignment Scheme in
Spatial Crowdsourcing-
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Abstract: Spatial crowdsourcing (SC) task assignment is to find the optimal worker for the task from abundant alternative workers based on the information of the task and workers, such as location, time, and ability. This information will undoubtedly reveal the privacy of both the task and workers. The disclosure of private information is a crucial issue constraining the development of SC. To this end, various privacy-preserving task assignments have been proposed to protect privacy by obfuscating or encrypting information. Fuzzy processing will limit matching accuracy, while encrypted information will increase the time cost of data computation. Therefore, this paper proposes a privacy-preserving map retrieval task assignment scheme (pMATE), which can divide the map and accurately retrieve the optimal workers according to this division. In pMATE, relevant information about tasks and workers is encrypted, and neighboring workers are searched based on the task presence partition. The task location can also be hidden in that partition. Partitioned retrieval reduces the amount of encrypted data needed to be matched. Furthermore, to reduce the problem of multiple communications during encrypted data comparison, we propose the Find MinNumber (FMN) algorithm, which can determine the optimal worker or top-k optimal workers need only two communications. Experimental evaluations of real-world data show that pMATE is efficient and accurate.
PubDate: Mon, 30 Jan 2023 01:35:01 +000
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Hybrid journal (It can contain Open Access articles)
