Transactions on Emerging Telecommunications Technologies最新文献

Multimodal biometric systems offer several advantages over unimodal systems, including a lower error rate, greater accuracy and broader coverage of residents. However, the multimodal systems need to store multiple biometric traits associated with each user, which brings a higher need for integrity and privacy. This study describes a deep learning (DL) model for a feature-level coalition that utilizes the biographical data of the user's face and iris to create a secure multimodal template. To create a reliable, unique multimodal shareable latent image, a deep hashing (linearization) approach is used for the fusion architecture. Furthermore, a hybrid secure architecture that fuses secure sketching techniques with erasable biometric features and integrates them into a complete security framework is used in this work. The efficiency of the recommended method is demonstrated using the face and iris images from the multimodal database. The proposed method provides the ability to delete templates and better protect the biometric data. This method works with the “WVU” multimodal data store and the “hashing” method for “image retrieval.” The proposed improved VGG16 achieves a data accuracy of 99.85. The paper also provides information on the techniques for structuring modalities such as iris and face using deep hashing, multimodal fusion and biometric security techniques. However, further studies are needed to extend the proposed framework to other unrestricted biometric aspects.

Delay estimation aims to determine the distance between the signal source and the receiver by measuring the signal's arriving time, which is crucial for underwater positioning. Traditional delay estimation algorithms, such as Generalized Cross-Correlation (GCC), often perform poorly in low signal-to-noise ratio (SNR) or multipath channels. In response to this issue, this paper proposes an algorithm based on adaptive Singular Value Decomposition Reconstruction (SVDR). This method initially requires obtaining the cross-power spectrum between the transmitted and received signals. Subsequently, the inter-correlation results at different frequency bands are assembled into a Frequency-Sliding Generalized Cross-Correlation (FSGCC) matrix. Then, Singular Value Decomposition Reconstruction (SVDR) is applied to extract crucial delay information from the matrix, aiming to alleviate the impact of noise and multipath effects on delay estimation. However, the selection of singular values during the reconstruction process directly influences the degree of noise reduction in the signal. Therefore, this manuscript further calculates the matrix represented by each singular value obtained from the SVD operation. The similarity between each matrix and the low-noise FSGCC matrix is computed to select the most suitable singular values to retain. Through simulation experiments, this algorithm can overcome the influence of the multipath effects and achieve better delay estimation results compared to traditional GCC and SVD algorithms, and validates its effectiveness in low SNR multipath underwater acoustic channels.

Recently, Mobile Ad-hoc Networks (MANETs) have created great interest in wireless communication. Several vulnerabilities are present in these networks. Thus, the pre-existing techniques offered numerous solutions. However, improvement is still required for augmenting the Detection Rate (DR). In this research approach, a Frechet Distribution-based Ensemble Deep Learning FD-EDL with hybrid optimization for an Intrusion Detection System (IDS) in MANET is proposed for augmenting the DR. Primarily, the trust value is computed. After the trust evaluation, the cluster formation and the Cluster Head (CH) selection are done utilizing the Diagonal with Cosine Similarity based K-Means (DCS-KM) algorithm. Then, by utilizing the Ad-hoc On-demand Distance Vector (AODV) algorithm, the path is generated for data transmission. For avoiding packet loss, the split and share strategy is designed in the generated path. Next, by utilizing the Polynomial Structured with Nullified Coupled Markov Chain (PSNCMC) model, noise interference is estimated and mitigated. Subsequently, the data is aggregated. The features are extracted from the aggregated data, and by utilizing Gazelle with Weighted Entropy Functional Red Panda Optimization (G-WEFRPO), the significant features are chosen. Next, for detecting intrusion in the MANET environment, the chosen features are inputted to the classifier. Based on performance metrics, the proposed method's performance is analogized with the baseline techniques in experimental analysis. The proposed system obtains a higher DR than conventional models. Hence, it is highly beneficial for IDS in MANET.

As quantum technology advances, classical digital signatures exhibit vulnerabilities in preserving security properties during the transmission of information. Working toward a reliable communication protocol, we introduce a proxy blind signature scheme to teleport a single particle qubit state with a message to the receiver, employing a three qubit GHZ entangled state. The blindness property is utilized to secure the message information from the proxy signer. A trusted party, Trent, is introduced to supervise the communication process. Alice blinds the original message and sends the Bell measurements with her entangled particle to proxy signer Charlie. After receiving measurements from Alice and Charlie, Bob verifies the proxy blind signature and performs appropriate unitary operations on his particle. Thereafter, Trent verifies the security of the quantum teleportation setup by matching the output data with the original data sent by Alice. Security analysis results prove that the proposed scheme fulfils the basic security necessities, including undeniability, unforgeability, blindness, verifiability, and traceability.

Phishing is a tactical technique practiced by cyber-criminals, wherein the target systems are approached, made vulnerable, and exploited. A Phisher who does the act of phishing is always creative, calculative, and persistent. This potentially leads to the increase in the success rate of phishing and the individuals who are technically expertise even falls in phishing campaigns. This article discusses about the various web-based phishing techniques used by the modern day cyber criminals. Various mitigation techniques related to the state of the art machine learning and deep learning techniques are also studied. The article also extensively discusses about the features utilized for the detection. Additionally, a qualitative and quantitative comparison of different studies for mitigating the web phishing attacks is also examined.

The rise of digital technology and Artificial Intelligence (AI) has led to the increased use of smart robots in various sectors. However, security and trust are significant concerns about deploying robots in critical infrastructures. Therefore, a secure and reliable robotic command control system is essential for successful robot integration. None of the prevailing systems focused on attack prediction during cloud-based robot control and data processing. Hence, this paper proposes a secure model called RCA-assisted attack detection and robotic command verification using LADA-C-RNN and S-Fuzzy. The robot controller is initially registered using the user ID and password in the cloud application. During login, the SCTDA is used to verify the robot controller's authority. Then, the robot controller's task is subjected to the attack detection phase. In the attack detection phase, the dataset is initially gathered and preprocessed. Thereafter, the temporal pattern analysis is done, followed by feature extraction. Subsequently, the optimal features are selected via GMJFOA. Then, the selected features are inputted to the LADA-C-RNN, which performs attack detection. Next, the normal data is fed into the traffic prioritization. Then, the prioritized tasks are inputted to the robot command data verification, thus increasing the security level. Finally, the proposed approach had minimum latency with 98.42% accuracy.

Social networking is a significant notion that has emerged for effective communication among multiple users. Social media services are in high demand among users all around the world. Privacy is important on social networking sites, and privacy concerns are particularly sensitive. Social media has numerous applications, and ensuring multiparty privacy (MP) among various users is a critical requirement. Massive research has been undertaken to manage secured MP across network users. However, certain issues still come up, like authentication, co-ownership of data by third parties, surveillance, and data misuse. The privacy preferences of a certain user are the priority by which the user can adjust or edit their network settings. Conflicts between users can be avoided, high security for personal data can be achieved, and highly confidential information can be maintained with the help of user preferences. Some security flaws in social media allow for the misuse of private information and the emergence of user conflicts. Therefore, privacy preservation techniques are developed and put into practice in order to address privacy concerns and provide improved security during data transfer. These techniques serve as technical assistance in recognizing and resolving disputes inside the MP management. For the construction of privacy preservation methods, real-world empirical data, user-centered MP controls, privacy-improved party analysis, hypothetical privacy support, and privacy assurance in the case of multiparty agreement are required.

More vertical service areas than only data processing, storing, and communication are promised by fog-cloud computing. Due to its great efficiency and scalability, distributed deep learning (DDL) across fog-cloud computing environments is a widely used application among them. With training limited to sharing parameters, DDL can offer more privacy protection than centralized deep learning. Nevertheless, DDL still faces two significant security obstacles when it comes to fog-cloud computing are How to ensure that users' identities are not stolen by outside enemies, and How to prevent users' privacy from being disclosed to other internal participants in the process of training. In this manuscript, Interference Tolerant Fast Convergence Zeroing Neural Network for Security and Privacy Preservation with Reptile Search Optimization Algorithm in Fog-Cloud Computing environment (SPP-ITFCZNN-RSOA-FCC) is proposed. ITFCZNN is proposed for security and privacy preservation, Then Reptile Search Optimization Algorithm (RSOA) is proposed to optimize the ITFCZNN, and Effective Lightweight Homomorphic Cryptographic Algorithm (ELHCA) is used to encrypt and decrypt the local gradients. The proposed SPP-ITFCZNN-RSOA-FCC system attains a better security balance, efficiency, and functionality than existing efforts. The proposed SPP-ITFCZNN-RSOA-FCC is implemented using Python. The performance metrics like accuracy, resource overhead, computation overhead, and communication overhead are considered. The performance of the SPP-ITFCZNN-RSOA-FCC approach attains 29.16%, 20.14%, and 18.93% high accuracy, and 11.03%, 26.04%, and 23.51% lower Resource overhead compared with existing methods including FedSDM: Federated learning dependent smart decision making component for ECG data at internet of things incorporated Edge-Fog-Cloud computing (SPP-FSDM-FCC), A collaborative computation with offloading in dew-enabled vehicular fog computing to compute-intensive with latency-sensitive dependence-aware tasks: Federated deep Q-learning method (SPP-FDQL-FCC), and a fog-edge-enabled intrusion identification scheme for smart grids (SPP-FSVM-FCC) respectively.

Rapid advancements in mobile communication technologies have led to the progression of telecom scams that not only deplete individual fortunes but also affect social income. Hence, fraudulent call detection gains significance, which not only aims to proactively recognize the frauds, but also alleviate the fraudulent activities to manage external losses. Though the traditional methods, such as rule-based systems and supervised machine learning techniques, actively engage in detecting such fraudulent activities, they fail to adapt to the evolving fraud patterns. Therefore, this research introduces a sheepdog hunt optimization-enabled knowledge-enhanced optimal graph neural network classifier (SDHO-KGNN) approach for detecting fraudulent calls accurately. The effectiveness of the proposed SDHO-KGNN approach is achieved through the combination of the power of graph representation learning with expert insights, which allows the proposed SDHO-KGNN approach to capture complex relationships and patterns within telecom data. Additionally, the integration of the SDHO algorithm enhances model performance by optimizing the discrimination between legitimate and fraudulent calls. Moreover, the SDHO-KGNN classifier captures the intricate call patterns and relationships within dynamic call networks, thereby attaining a better accuracy, precision, and recall of 93.8%, 95.91%, and 95.53% for 90% of the training.