Secure Communications In Networks Research Articles

Implementing end-to-end encryption strategist for strengthening data security across network communication

In order to improve data security in network communication, this project suggests using End-to-End Encryption (E2EE). E2EE protects against unwanted access by guaranteeing that private information is kept secret for the duration of its travel. In order to mitigate potential vulnerabilities at intermediate points, cryptographic procedures are implemented to safeguard data from the point of origin to its final destination. The method makes use of strong encryption algorithms and key management procedures in order to strengthen the confidentiality and integrity of data that is transferred. This method tackles the growing worries about data breaches, privacy violations, and cyberthreats. By emphasizing E2EE, communication networks’ security posture is strengthened and user and stakeholder trust is increased. By implementing this proactive security solution, organizations may protect sensitive data, comply with changing regulatory requirements, and provide a strong basis for safe data sharing in the digital sphere.

Learning Empirical Inherited Intelligent MPC for Switched Systems With Network Security Communication

Learning Empirical Inherited Intelligent MPC for Switched Systems With Network Security Communication

Secrecy Performance Analysis of Energy Harvesting Untrusted Relay Networks with Hardware Impairments

In this work, we focus on the issue of secure communication in energy harvesting untrusted relay networks taking into account the impact of hardware impairments, where an energy‐constrained relay, powered by received radio frequency signals, attempts to decode sensitive data from the source without authorization. To achieve secure communication, we employ destination‐assisted jamming to prevent the untrusted relay from decoding sensitive data while serving as a useful energy source. We derive the interception probability (IP), connection outage probability (COP), and effective secrecy throughput (EST) in closed form for time switching relaying (TSR) strategy. Subsequently, we conduct an asymptotic analysis in the high signal‐to‐noise ratio (SNR) regime to provide practical insights into how various system parameters affect security, reliability, and efficiency, including time switching factor and the level of hardware impairments. Our findings demonstrate that the presence of hardware impairments improves security performance but compromises reliability performance, which does more harm than good.

Design of Intelligent City Communication Network Based on Internet of Things

Abstract In response to the advancements in Internet of Things (IoT) technology and the growing demands for data transmission, there is a crucial need for further integration of communication network technology with IoT to enhance efficiency. This paper presents the design of a multi-node communication network model utilizing the Orthogonal Frequency-Division Multiplexing (OFDM) algorithm. In the realm of physical layer parameter design, uplink, and downlink carriers are allocated based on OFDM symbols, employing the Zadoff-Chu (ZC) sequence as the pilot frequency sequence. Additionally, the Cyclic Redundancy Check (CRC) error-checking code is implemented to ensure the accuracy of network transmissions. The model proposed in this study is applied to the operational dynamics of traffic systems within a smart city, focusing on the security and operational efficiency of the traffic communication network. Empirical testing and analysis demonstrate that the communication network, as designed, is susceptible to malware attacks by 25.68%, representing the least vulnerability among the three algorithms assessed. Moreover, in examining the bus trip time distribution with the implementation of this model, the average running time is predominantly around 11.76 minutes, which is a reduction of 0.74 minutes compared to previous configurations, thereby indicating an improvement in operational efficiency.

Securing Large-Scale D2D Networks Using Covert Communication and Friendly Jamming

We exploit both covert communication and friendly jamming to propose a friendly jamming-assisted covert communication and use it to doubly secure a large-scale device-to-device (D2D) network against eavesdroppers (i.e., wardens). The D2D transmitters defend against the wardens by: 1) hiding their transmissions with enhanced covert communication, and 2) leveraging friendly jamming to ensure information secrecy even if the D2D transmissions are detected. We model the combat between the wardens and the D2D network (the transmitters and the friendly jammers) as a two-stage Stackelberg game. Therein, the wardens are the followers at the lower stage aiming to minimize their detection errors, and the D2D network is the leader at the upper stage aiming to maximize its utility (in terms of link reliability and communication security) subject to the constraint on communication covertness. We apply stochastic geometry to model the network spatial configuration so as to conduct a system-level study. We develop a bi-level optimization algorithm to search for the equilibrium of the proposed Stackelberg game based on the successive convex approximation (SCA) method and Rosenbrock method. Numerical results reveal interesting insights. We observe that without the assistance from the jammers, it is difficult to achieve covert communication on D2D transmission. Moreover, we illustrate the advantages of the proposed friendly jamming-assisted covert communication by comparing it with the information-theoretical secrecy approach in terms of the secure communication probability and network utility.

Explosive Cyber Security Threats During COVID-19 Pandemic and a Novel Tree-Based Broad Learning System to Overcome

The rapid spread of the COVID-19 has not only affected personal health and economy, but also revolutionized people's lifestyles. As more people turn to work and socialize online, the development of unmanned technologies based on the Internet of Vehicles (IoV), such as unmanned delivery, unmanned vehicles, unmanned transportation, etc., will become an inevitable trend. However, all kinds of intelligent terminals for unmanned equipment require a large amount of data interaction with devices such as cloud servers, mobile terminals, and roadside terminals, which poses cyber security risks. Furthermore, the outbreak of COVID-19 has prompted people to put forward higher demands for the security of network communications. Unfortunately, the current intrusion detection methods based on machine learning still have weaknesses such as low accuracy and low efficiency when faced with unbalanced data distribution. To solve the above problems, we propose a novel Tree-based BLS (TBLS) intrusion detection method according to the idea of ensemble learning and decision tree (CART and J48). The performance of TBLS was tested on the NSL-KDD dataset and the UNSW-NB15 dataset respectively, which contain a variety of malicious traffic types for attacks on the IoV. The results show that our proposed method can achieve higher accuracy rate and lower false alarm rate, compared with the existing 16 solutions.

Virtual Private Network—Firewall Integration for Wireless Local Area Network Improvement against Jammers

Wireless networks are susceptible to many security problems since the wireless medium is open. Jammer (jamming attack) is the most important problems in wireless networks. It is denial-of-service attack in which attackers send malicious signals or messages on a channel that is intended for normal traffic by intentionally interfering with the network. One of the solutions is the development of tunneling technology as an attempt to secure communication networks. Virtual Private Network (VPN) tunneling technology when integrated with firewall would provide a suitable security. In this study, number of Jammers would interfere with the normal operation of the network resulting in performance degradation of network. These Jammers decrease throughput and increase delay and data dropped. The aim of this study is to improve the performance of Wireless Local Area Network (WLAN) by integrating Firewall with VPN in number of different Riverbed Modeler simulation scenarios which it is utilized in this study for video and data applications. Firewall is a technology employed to regulate the level of network connectivity. It can block unauthorized access from an external network to an internal network’s resources. In this study, the tunneling technology of VPN was integrated with firewall to improve the performance by increasing the throughput and decreasing delay and data dropped. The firewall would block any access to the server from the workstations and the VPN would filter the packets to allow a specific access to the specific server. The results showed that VPN when integrated with firewall increased the throughput from 1,350,000 to 1,500,000 bits/sec and decreased the delay from 0.0054 sec to 0.0047 sec and data dropped from 7,800 to 5,000 bits/sec so that this study proved that VPN incorporated with firewall provides a good throughput and delay improvement for wireless local area network.

Communication network security situation analysis based on time series data mining technology

Communication network security situation analysis based on time series data mining technology

A machine learning based approach for 5G network security monitoring

Abstract This paper constructs a 5G network security detection system based on the functional requirements of network security detection and the P2DR process model. The structure can be broken down into three layers from the bottom to the top: acquisition layer, analysis layer, and display layer. The design focuses on writing the SVM algorithm into the software of the communication network security vulnerability monitoring system, and in order to solve the defect of the long training time of the model of this machine learning algorithm, the incremental learning vector machine model is used, which is combined into the CSV-KKT-ISVM model. Test datasets that cover system performance, effectiveness, and leakage are used to test the system after it is completed. The test data was analyzed to prove that the system’s memory usage was maintained at 46M, CPU usage was 5% to 10%, and the response time was no later than 1 s. The monitoring accuracy was 98.5% at the highest but decreased with the increase of the percentage of vulnerability data, and the accuracy dropped to 93.9% at 50%, the minimum was not lower than 90%, and the error rate was no less than 0.8%. To achieve the best outcome, the system threshold should be set to 5, and there should be no false alarms or misreporting.

Heralded and robust W-state generation for distant superconducting qubits with practical microwave pulse scattering

Nonlocal multipartite entanglement holds significant potential for both fundamental physics and practical applications. We present a robust protocol for generating remote entanglement, specifically W states, among multiple nonlocal superconducting qubits. Our protocol employs practical microwave pulse scattering through a microwave cavity, which is coupled to a far-detuned superconducting transmon qubit, facilitating a CNOT-like operation. Our approach can be implemented without the need for single microwave-photon sources and its completion is heralded by microwave photon measurements. Furthermore, our protocol can generate faithful nonlocal W states of distant superconducting qubits with practical detector units, each cascading two quantum non-demolition detectors, rather than ideal single-photon detectors. These distinct features make our protocol particularly useful for large quantum secure communication networks.

Source‐Replacement Model for Phase‐Matching Quantum Key Distribution

AbstractQuantum key distribution has emerged as a promising solution for constructing secure communication networks, with its information‐theoretic security rooted in quantum mechanics. One of the recent quantum key distribution protocols, the phase‐matching protocol, can have a quadratic key‐rate improvement. Its security was initially established using an abstract method known as symmetry‐protected privacy. In this study, this security is reevaluated under the more intuitive source‐replacement model, arriving at the same conclusions as the original proof. This model provides a fresh perspective on the protocol's security. As an application of this approach, a beam‐splitting attack is introduced. Leveraging the source‐replacement model, a lower bound on the phase error rate under this attack is derived, further underscoring the robustness of our security analysis method.

Key security measurement method of authentication based on mobile edge computing in urban rail transit communication network

In the rail transit industry, the communication network is also widely used in various professions. Rail transit stations have the characteristics of narrow space, high degree of airtightness, limited sight lines, and uneven distribution of personnel in various periods. Although the security degree of rail transit entrances and exits is relatively reliable compared with that of stations, the anti-risk ability is fragile, and the consequences are often disastrous. To solve the problems of low-security access rate, long response time and ineffective identification of attack components of urban rail transit communication platform users, a security measurement method based on mobile edge computing (MEC) for the authentication key of urban rail transit communication network is proposed. Firstly, the wireless communication service of the urban rail transit platform is analyzed, and the data key protocol is constructed to obtain the hyper-chaos key pair of wireless communication information. Then, the double chaotic mapping model is applied to determine the key form and input parameters, and the bit key stream is obtained, which is subjected to exclusive operation with the plaintext for key decryption. Next, the block matching structure is used to analyze the associated eigenvalues that affect the security risk of urban rail transit communication networks. Meanwhile, the MEC server is used to accelerate the extraction of its feature values. Finally, in the environment of MEC, the paper constructs a risk identification model, evaluates the security strength of the authentication key, controls the threshold value, analyzes the impact factors and weights of communication network security, and completes the traceability measurement of communication network security risk. Experimental results show that the security access times of the MEC platform are senior, the maximum attack component obtained is 23500, which is consistent with the actual value, and the global maximum response time is 0.30 ms.

A Decision Model to Support the Selection of SENKOM Personnel Using the Profile Matching Method with the Capability of Cyber Security

The very rapid development of information technology has brought tactical and strategic advantages, but it can also be a potential attack from opposing parties on the information and communication systems and networks used, thus opening the way for the emergence of a new war, namely cyber warfare. Cyber attacks are a new threat to Adisutjipto Air Base, which targets vital parts that can impact the organization and make the command and control system ineffective and inefficient. One of the important elements of Adisutjipto Lanud in facing cyber attacks is the readiness of data and communication network security personnel. In the direct or conventional personnel selection process, it is not possible to see the abilities possessed by prospective data security personnel, both in terms of skills, management aspects, analytical aspects, competency weight, and so on. A decision support system can be used to assist decision-making based on existing criteria. This research is limited to only considering the selection of personnel who will become members of komlek or senkom who are responsible for data security and communications networks at Adisutjipto Air Base. In this research, the method used is the profile matching method. The concept of the profile matching method is to compare the selection using the conventional method with the decision support system method in selecting komlek/senkom personnel as cyber security personnel so that differences in competency can be identified, also called GAP (Gross Across Product). The smaller the GAP produced, the greater the weight of the value. large, this means that personnel who meet the requirements have a greater chance of someone occupying that position. The final result of this research is to obtain ranking information for each cyber security candidate based on profile matching calculations to be able to carry out tasks optimally in securing data and networks at Adisutjipto Air Base.

Applications of Nonlinear Analysis Transforming Communication Paradigms for Seamless Connectivity

The dynamic nature of communication paradigms demands uninterrupted connectivity in order to achieve maximum functionality. It will take creative thinking to achieve this smoothness. It becomes clear that nonlinear analysis is a revolutionary methodology that provides original insights and solutions. In order to attain seamless connectedness, this study investigates how nonlinear analysis might be applied to revolutionise communication paradigms. A strong foundation for comprehending and improving communication systems is provided by nonlinear analysis, which has its roots in the study of complex systems. This paper delves into the fundamental concepts of nonlinear analysis and elucidates how they may be applied to the complicated dynamics of modern communication. Nonlinear analysis, which includes information theory, signal processing, and chaos theory, introduces a new paradigm in communication technology. Network congestion, signal distortion, and security holes are just a few of the issues plaguing today's communication infrastructure that cause disruptions in service. Nonlinear analysis is a useful technique for overcoming and reducing these challenges. The efficiency, dependability, and security of communication networks can all be improved by implementing nonlinear system dynamics. In this study, nonlinear analysis is applied to real-world problems in network security, wireless communication, and the Internet of Things (IoT) through several detailed case studies. Using these real-world examples, we can see how nonlinear methods can successfully alter established modes of communication and demonstrate their value. The implications of nonlinear analysis for future communication paradigms are far-reaching and extend beyond the capabilities of current technology. This essay delves into the future of communication technology, shedding light on emerging trends that may have profound impacts in the years ahead. It also examines the benefits and difficulties that come with employing nonlinear analysis to establish seamless communication. Nonlinear analysis is a potent instrument with potential applications in shifting communication paradigms, facilitating seamless connectivity, and paving the way for the creation of novel forms of communication technology. The study emphasises the significance of nonlinear techniques in addressing the challenges of modern communication systems and guiding them towards a more connected and successful future.

Resource Allocation Method of Edge IoT Agent Based on Deep Reinforcement Learning

Reasonable allocation of resources is an important guarantee for efficient support of power business in edge IoT agents. Facing the above problems of the current power Internet of Things, this paper proposes a resource optimization allocation method based on deep Q-learning. This method first comprehensively considers the communication performance and network security. Involving indicators such as latency and service satisfaction, a complete and reliable mathematical model of the edge Internet of Things proxy network is constructed to achieve efficient and reliable modeling of the power Internet of Things (pIoT), aiming to better fit the practical interaction needs for efficient and secure communication. The Q-learning network model is optimized, and the method combining Reinforcement learning and deep learning is used to solve the model. Used by this network, the optimization and improvement of the deep network model is realized, so that the status, action and other parameters of the network model can be solved in a timely manner, so as to better support the reliable and efficient information interaction of the communication network. The test results prove that the delay of the proposed method can be maintained within 12[Formula: see text]ms in more complex scenarios, and the interaction success rate reaches 0.975, confirming that the proposed method can provide good information interaction guarantee services.

DAR Model: A Novel Symmetric Key Enabled Security architecture for reliable data transfer in Wireless Sensor Networks

Security is an indispensable aspect in every transaction happening in the network transmissions. Wireless Sensor Networks are pretty vulnerable to the security attacks. Hence a highly efficient architectural model is very much essential in designing the sensor networks. Cryptographic algorithms play a vital role in providing encryption and decryption to the data being transmitted consequently with which security is offered in an elegant manner. In this paper, a reliable design comprising three pioneering algorithms enabled with symmetric key is architected for secure communication in wireless sensor networks from a node to the base station. The design involves two phases. In the former phase two algorithms which are effective in all perspectives are used for data transmission from node to cluster head and in the latter phase another proficient algorithm is used for communication between cluster head to base station. The three algorithms used are Data Encryption Standard (DES), Advanced Encryption Standard (AES) and RC4. Both block and stream cipher algorithms are used to fine tune the performance; and in addition, the data has been compressed with unprecedented techniques to reduce the burden on encryption. This led to an amazing performance in terms of security parameters.

Enhancing Data Security in Autonomous Vehicle Communication Networks

In today's driving landscape, in-vehicle communication has become a cornerstone, facilitated by the proliferation of sensor-centric communication and computing devices within vehicles. These systems serve various functions such as vehicle monitoring, reducing physical wiring, and enhancing driving efficiency. However, the existing literature on cybersecurity for in-vehicle communication systems lacks dedicated solutions to mitigate in-vehicle cyber risks effectively. Current approaches primarily rely on protocol-specific security techniques, lacking a comprehensive security framework for in-vehicle communication. This paper critically examines the literature on cybersecurity for in-vehicle communication, focusing on technical architecture, methodologies, challenges, and potential solutions.
 The paper presents an in-depth analysis of in-vehicle communication network architecture, outlining key components, interfaces, and associated technologies. Protocols utilized in in-vehicle communication are classified based on their characteristics and usage types. Furthermore, security solutions for in-vehicle communication are critically reviewed, encompassing machine learning, cryptography, and port-centric techniques. A multi-layer secure framework is proposed as a protocol and use case-independent solution for in-vehicle communication security.Lastly, the paper identifies open challenges and outlines future research directions for enhancing cybersecurity in in-vehicle communication.

A Blockchain-based Approach for Securing Network Communications in IoT Environments

A Blockchain-based Approach for Securing Network Communications in IoT Environments

Assurance of Network Communication Information Security Based on Cyber-Physical Fusion and Deep Learning

The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.

Realization of chaotic oscillator and use in secure communication

This scientific study reports on the practical as well as modelling of active hybrid inductor based chaotic oscillators using Current Feedback Amplifier (CFOA) and Op-Amp. Furthermore, a workable design strategy for the chaotic oscillator and its application to secure communication are provided. Here, the transient analysis of chaos waveforms is verified by using MATLAB, Multi-SIM, and LT-Spice tools. The numerical outcomes based on LT-Spice and MATLAB are closely matched. Additionally, results from the multi-SIM platform show how a secure communication network was implemented utilising the master-slave technique of synchronisation. Finally, a comparison analysis is conducted to demonstrate the originality of the suggested work.