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As an open network architecture, Wireless Computing Power Networks (WCPN) pose new challenges for achieving efficient and secure resource management in networks, because of issues such as insecure communication channels and untrusted device terminals. Blockchain, as a shared, immutable distributed ledger, provides a secure resource management solution for WCPN. However, integrating blockchain into WCPN faces challenges like device heterogeneity, monitoring communication states, and dynamic network nature. Whereas Digital Twins (DT) can accurately maintain digital models of physical entities through real-time data updates and self-learning, enabling continuous optimization of WCPN, improving synchronization performance, ensuring real-time accuracy, and supporting smooth operation of WCPN services. In this paper, we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models. We adopt an enumeration-based optimal placement algorithm (EOPA) and an improved simulated annealing-based near-optimal placement algorithm (ISAPA) to achieve minimum average DT synchronization latency under the constraint of DT error. Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency. [ABSTRACT FROM AUTHOR]

The automatic collection of power grid situation information, along with real-time multimedia interaction between the front and back ends during the accident handling process, has generated a massive amount of power grid data. While wireless communication offers a convenient channel for grid terminal access and data transmission, it is important to note that the bandwidth of wireless communication is limited. Additionally, the broadcast nature of wireless transmission raises concerns about the potential for unauthorized eavesdropping during data transmission. To address these challenges and achieve reliable, secure, and real-time transmission of power grid data, an intelligent security transmission strategy with sensor-transmission-computing linkage is proposed in this paper. The primary objective of this strategy is to maximize the confidentiality capacity of the system. To tackle this, an optimization problem is formulated, taking into consideration interruption probability and interception probability as constraints. To efficiently solve this optimization problem, a low-complexity algorithm rooted in deep reinforcement learning is designed, which aims to derive a suboptimal solution for the problem at hand. Ultimately, through simulation results, the validity of the proposed strategy in guaranteed communication security, stability, and timeliness is substantiated. The results confirm that the proposed intelligent security transmission strategy significantly contributes to the safeguarding of communication integrity, system stability, and timely data delivery. [ABSTRACT FROM AUTHOR]

This paper examines the performance of Full-Duplex Cooperative Rate Splitting (FD-CRS) with Simultaneous Wireless Information and Power Transfer (SWIPT) support in Multiple Input Single Output (MISO) networks. In a Rate Splitting Multiple Access (RSMA) multicast system with two local users and one remote user, the common data stream contains the needs of all users, and all users can decode the common data stream. Therefore, each user can receive some information that other users need, and local users with better channel conditions can use this information to further enhance the reception reliability and data rate of users with poor channel quality. Even using Cell-Center-Users (CCUs) as a cooperative relay to assist the transmission of common data can improve the average system speed. To maximize the minimum achievable rate, we optimize the beamforming vector of Base Station (BS), the common stream splitting vector, the cooperative distributed beam vector and the strong user transmission power under the power budget constraints of BS and relay devices and the service quality requirements constraints of users. Since the whole problem is not convex, we cannot solve it directly. Therefore, we propose a low complexity algorithm based on Successive Convex Approximation (SCA) technology to find the optimal solution to the problem under consideration. The simulation results show that FD C-RSMA has better gain and more powerful than FD C-NOMA, HD C-RSMA, RSMA and NOMA. [ABSTRACT FROM AUTHOR]

Effective data communication is a crucial aspect of the Social Internet of Things (SIoT) and continues to be a significant research focus. This paper proposes a data forwarding algorithm based on Multidimensional Social Relations (MSRR) in SIoT to solve this problem. The proposed algorithm separates message forwarding into intra- and cross-community forwarding by analyzing interest traits and social connections among nodes. Three new metrics are defined: the intensity of node social relationships, node activity, and community connectivity. Within the community, messages are sent by determining which node is most similar to the sender by weighing the strength of social connections and node activity. When a node performs cross-community forwarding, the message is forwarded to the most reasonable relay community by measuring the node activity and the connection between communities. The proposed algorithm was compared to three existing routing algorithms in simulation experiments. Results indicate that the proposed algorithmsubstantially improves message delivery efficiency while lessening network overhead and enhancing connectivity and coordination in the SIoT context. [ABSTRACT FROM AUTHOR]

The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super and normal nodes based on their residual energies. The protocol considers multi-criteria attributes, i.e. energy, traffic load, and distance parameters to select the next hop for data delivery towards the base station. The performance of the proposed protocol is evaluated through the network simulator NS3.40. For different traffic rates, number of nodes, and different packet sizes, the proposed protocol outperformed LoRaWAN in terms of end-to-end packet delivery ratio, energy consumption, end-to-end delay, and network lifetime. For 100 nodes, the proposed protocol achieved a 13% improvement in packet delivery ratio, 10 ms improvement in delay, and 10 mJ improvement in average energy consumption over LoRaWAN. [ABSTRACT FROM AUTHOR]

Large-scale wireless sensor networks (WSNs) play a critical role in monitoring dangerous scenarios and responding to medical emergencies. However, the inherent instability and error-prone nature of wireless links present significant challenges, necessitating efficient data collection and reliable transmission services. This paper addresses the limitations of existing data transmission and recovery protocols by proposing a systematic end-to-end design tailored for medical event-driven cluster-based large-scale WSNs. The primary goal is to enhance the reliability of data collection and transmission services, ensuring a comprehensive and practical approach. Our approach focuses on refining the hop-count-based routing scheme to achieve fairness in forwarding reliability. Additionally, it emphasizes reliable data collection within clusters and establishes robust data transmission over multiple hops. These systematic improvements are designed to optimize the overall performance of the WSN in real-world scenarios. Simulation results of the proposed protocol validate its exceptional performance compared to other prominent data transmission schemes. The evaluation spans varying sensor densities, wireless channel conditions, and packet transmission rates, showcasing the protocol's superiority in ensuring reliable and efficient data transfer. Our systematic end-to-end design successfully addresses the challenges posed by the instability of wireless links in large-scaleWSNs. By prioritizing fairness, reliability, and efficiency, the proposed protocol demonstrates its efficacy in enhancing data collection and transmission services, thereby offering a valuable contribution to the field of medical event-drivenWSNs. [ABSTRACT FROM AUTHOR]

By the analysis of vulnerabilities of Android native system services, we find that some vulnerabilities are caused by inconsistent data transmission and inconsistent data processing logic between client and server. The existing research cannot find the above two types of vulnerabilities and the test cases of them face the problem of low coverage. In this paper, we propose an extraction method of test cases based on the native system services of the client and design a case construction method that supports multi-parameter mutation based on genetic algorithm and priority strategy. Based on the above method, we implement a detection tool-BArcherFuzzer to detect vulnerabilities of Android native system services. The experiment results show that BArcherFuzzer found four vulnerabilities of hundreds of exception messages, all of them were confirmed by Google and one was assigned a Common Vulnerabilities and Exposures (CVE) number (CVE-2020-0363). [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is vulnerable to data-tampering (DT) attacks. Due to resource limitations, many anomaly detection systems (ADSs) for IoT have high false positive rates when detecting DT attacks. This leads to the misreporting of normal data, which will impact the normal operation of IoT. To mitigate the impact caused by the high false positive rate of ADS, this paper proposes an ADS management scheme for clustered IoT. First, we model the data transmission and anomaly detection in clustered IoT. Then, the operation strategy of the clustered IoT is formulated as the running probabilities of all ADSs deployed on every IoT device. In the presence of a high false positive rate in ADSs, to deal with the trade-off between the security and availability of data, we develop a linear programming model referred to as a security trade-off (ST) model. Next, we develop an analysis framework for the ST model, and solve the ST model on an IoT simulation platform. Last, we reveal the effect of some factors on the maximum combined detection rate through theoretical analysis. Simulations show that the ADS management scheme can mitigate the data unavailability loss caused by the high false positive rates in ADS. [ABSTRACT FROM AUTHOR]

Steganography is a technique for hiding secret messages while sending and receiving communications through a cover item. From ancient times to the present, the security of secret or vital information has always been a significant problem. The development of secure communication methods that keep recipient-only data transmissions secret has always been an area of interest. Therefore, several approaches, including steganography, have been developed by researchers over time to enable safe data transit. In this review, we have discussed image steganography based on Discrete Cosine Transform (DCT) algorithm, etc. We have also discussed image steganography based on multiple hashing algorithms like the Rivest–Shamir–Adleman (RSA) method, the Blowfish technique, and the hash-least significant bit (LSB) approach. In this review, a novel method of hiding information in images has been developed with minimal variance in image bits, making our method secure and effective. A cryptography mechanism was also used in this strategy. Before encoding the data and embedding it into a carry image, this review verifies that it has been encrypted. Usually, embedded text in photos conveys crucial signals about the content. This review employs hash table encryption on the message before hiding it within the picture to provide a more secure method of data transport. If the message is ever intercepted by a third party, there are several ways to stop this operation. A second level of security process implementation involves encrypting and decrypting steganography images using different hashing algorithms. [ABSTRACT FROM AUTHOR]

The use of the Internet of Things (IoT) is expanding at an unprecedented scale in many critical applications due to the ability to interconnect and utilize a plethora of wide range of devices. In critical infrastructure domains like oil and gas supply, intelligent transportation, power grids, and autonomous agriculture, it is essential to guarantee the confidentiality, integrity, and authenticity of data collected and exchanged. However, the limited resources coupled with the heterogeneity of IoT devices make it inefficient or sometimes infeasible to achieve secure data transmission using traditional cryptographic techniques. Consequently, designing a lightweight secure data transmission scheme is becoming essential. In this article, we propose lightweight secure data transmission (LSDT) scheme for IoT environments. LSDT consists of three phases and utilizes an effective combination of symmetric keys and the Elliptic Curve Menezes-Qu-Vanstone asymmetric key agreement protocol. We design the simulation environment and experiments to evaluate the performance of the LSDT scheme in terms of communication and computation costs. Security and performance analysis indicates that the LSDT scheme is secure, suitable for IoT applications, and performs better in comparison to other related security schemes. [ABSTRACT FROM AUTHOR]

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has become a core key technology in network supervision. In recent years, many different solutions have emerged in this field. Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-level features of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites, temporal features can exhibit significant variations due to changes in communication links and transmission quality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission. Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantly difficult. To address this, we propose a universal packet-level encrypted traffic identification method, Combo Packet. This method utilizes convolutional neural networks to extract deep features of the current packet and its contextual information and employs spatial and channel attention mechanisms to select and locate effective features. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic service categories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g., BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0% and 97.1% for service and application categories, respectively. It also provides shorter training times and higher recognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior to the existing classification methods mentioned. [ABSTRACT FROM AUTHOR]

Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)- based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the data and devices becomes challenging due to factors such as dynamic operational environments, remoteness, harsh conditions, and areas where human physical access is difficult. One of the crucial parts of securing MTCDs and data is authentication, where each devicemust be verified before data transmission. SeveralM2Mauthentication schemes have been proposed in the literature, however, the literature lacks a comprehensive overview of current M2M authentication techniques and the challenges associated with them. To utilize a suitable authentication scheme for specific scenarios, it is important to understand the challenges associated with it. Therefore, this article fills this gap by reviewing the state-of-the-art research on authentication schemes in MTCDs specifically concerning application categories, security provisions, and performance efficiency. [ABSTRACT FROM AUTHOR]

Owing to the rapid increase in the interchange of text information through internet networks, the reliability and security of digital content are becoming a major research problem. Tampering detection, Content authentication, and integrity verification of digital content interchanged through the Internet were utilized to solve a major concern in information and communication technologies. The authors' difficulties were tampering detection, authentication, and integrity verification of the digital contents. This study develops an Automated Data Mining based Digital Text Document Watermarking for Tampering Attack Detection (ADMDTW-TAD) via the Internet. The DM concept is exploited in the presented ADMDTW-TAD technique to identify the document's appropriate characteristics to embed larger watermark information. The presented secure watermarking scheme intends to transmit digital text documents over the Internet securely. Once the watermark is embedded with no damage to the original document, it is then shared with the destination. The watermark extraction process is performed to get the original document securely. The experimental validation of the ADMDTW-TAD technique is carried out under varying levels of attack volumes, and the outcomes were inspected in terms of different measures. The simulation values indicated that the ADMDTW-TAD technique improved performance over other models. [ABSTRACT FROM AUTHOR]

Wireless Network security management is difficult because of the ever-increasing number of wireless network malfunctions, vulnerabilities, and assaults. Complex security systems, such as Intrusion Detection Systems (IDS), are essential due to the limitations of simpler security measures, such as cryptography and firewalls. Due to their compact nature and low energy reserves, wireless networks present a significant challenge for security procedures. The features of small cells can cause threats to the network. Network Coding (NC) enabled small cells are vulnerable to various types of attacks. Avoiding attacks and performing secure "peer" to "peer" data transmission is a challenging task in small cells. Due to the low power and memory requirements of the proposed model, it is well suited to use with constrained small cells. An attacker cannot change the contents of data and generate a new Hashed Homomorphic Message Authentication Code (HHMAC) hash between transmissions since the HMAC function is generated using the shared secret. In this research, a chaotic sequence mapping based low overhead 1D Improved Logistic Map is used to secure "peer" to "peer" data transmission model using lightweight H-MAC (1D-LM-P2P-LHHMAC) is proposed with accurate intrusion detection. The proposed model is evaluated with the traditional models by considering various evaluation metrics like Vector Set Generation Accuracy Levels, Key Pair Generation Time Levels, Chaotic Map Accuracy Levels, Intrusion Detection Accuracy Levels, and the results represent that the proposed model performance in chaotic map accuracy level is 98% and intrusion detection is 98.2%. The proposed model is compared with the traditional models and the results represent that the proposed model secure data transmission levels are high. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often amismatch between the upperlayer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely utilized in point cloud image processing. In this work, we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritizemessage forwarding so that messages reach the network edge quickly and are evenly distributed. Moreover, we compare ourmodel with theKadcast transmissionmodel, which is a classic improvementmodel for blockchain P2P transmission networks, the experimental findings show that the FPSblo model reduces 34.8% of transmission redundancy and reduces the overload rate by 37.6%. By conducting experimental analysis, the FPS-BTmodel enhances the transmission capabilities of the P2P network in blockchain. [ABSTRACT FROM AUTHOR]