Int. J. Commun. Networks Inf. Secur.最新文献

{"title":"Underwater Acoustic Sensor Network Data Optimization with Enhanced Void Avoidance and Routing Protocol","authors":"M. Garg, Sachin Sharma, Vincent Balu, D. Sinha, P. Bhatt, Akash Kumar Bhagat","doi":"10.17762/ijcnis.v14i3.5602","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5602","url":null,"abstract":"Deployment of a multi-hop underwater acoustic sensor network (UASN) in a larger region presents innovative challenges in reliable data communications and survivability of network because of the limited underwater interaction range or bandwidth and the limited energy of underwater sensor nodes. UASNs are becoming very significant in ocean exploration applications, like underwater device maintenance, ocean monitoring, ocean resource management, pollution detection, and so on. To overcome those difficulties and attains the purpose of maximizing data delivery ratio and minimizing energy consumption of underwater SNs, routing becomes necessary. In UASN, as the routing protocol will guarantee effective and reliable data communication from the source node to the destination, routing protocol model was an alluring topic for researchers. There were several routing techniques devised recently. This manuscript presents an underwater acoustic sensor network data optimization with enhanced void avoidance and routing (UASN-DAEVAR) protocol. The presented UASN-DAEVAR technique aims to present an effective data transmission process using proficient routing protocols. In the presented UASN-DAEVAR technique, a red deer algorithm (RDA) is employed in this study. In addition, the UASN-DAEVAR technique computes optimal routes in the UASN. To exhibit the effectual results of the UASN-DAEVAR technique, a wide spread experimental analysis is made. The experimental outcomes represented the enhancements of the UASN-DAEVAR model.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131147686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy Efficiency Based Load Balancing Optimization Routing Protocol In 5G Wireless Communication Networks","authors":"Divya Paikaray, Divyanshi Chhabra, Sachin Sharma, S. Goswami, S. H K, Prof. Gordhan Jethava","doi":"10.17762/ijcnis.v14i3.5605","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5605","url":null,"abstract":"A significant study area in cloud computing that still requires attention is how to distribute the workload among virtual machines and resources. Main goal of this research is to develop an efficient cloud load balancing approach, improve response time, decrease readiness time, maximise source utilisation, and decrease activity rejection time. This research propose novel technique in load balancing based network optimization using routing protocol for 5G wireless communication networks. the network load balancing has been carried out using cloud based software defined multi-objective optimization routing protocol. then the network security has been enhanced by data classification utilizing deep belief Boltzmann NN. Experimental analysis has been carried out based on load balancing and security data classification in terms of throughput, packet delivery ratio, energy efficiency, latency, accuracy, precision, recall.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129224887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Honeynet Implementation in Cyber Security Attack Prevention with Data Monitoring System Using AI Technique and IoT 4G Networks","authors":"Kushnian Kour, Subhashish Goswami, Meenakshi Sharma, P. Sivasankar, V. Vekariya, A. Kumari","doi":"10.17762/ijcnis.v14i3.5603","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5603","url":null,"abstract":"Cyber Physical Systems (CPS) comprises of the ubiquitous object concept those are connected with Internet to provide ability of data transmission and sensing over network. The smart appliances transmits the data through CPS devices with the implementation of Internet of Things (IoT) exhibits improved performance characteristics with significant advantages such as time savings, reduced cost, higher human comfort and efficient electricity utilization. In the minimal complexity sensor nodes cyber physical system is adopted for the heterogeneous environment for the wireless network connection between clients or hosts. However, the conventional security scheme uses the mechanisms for desktop devices with efficient utilization of resources in the minimal storage space environment, minimal power processing and limited energy backup. This paper proposed a Secure Honeynet key authentication (SHKA) model for security attack prevention through effective data monitoring with IoT 4G communication. The proposed SHKA model uses the lightweight key agreement scheme for authentication to provide security threats and confidentiality issues in CPS applications. With the implementation of SHKA HoneyNet model the data in IoT are monitored for security mechanism in IoT environment. The middleware module in SHKA scheme uses the Raspberry platform to establish internetworking between CPS device to achieve dynamic and scalability. The secure IoT infrastructure comprises of flexible evaluation of user-centric environment evaluation for the effectiveness. The developed SHKA model perform mutual authentication between CPS devices for minimal computation overhead and efficiency. The wireless channel uses the dynamic session key for the secure communication for cyber-attacks security with lightweight security in CPS system. The SHKA model demonstrate the effectiveness based on consideration of three constraints such as low power processing, reduced storage and minimal backup energy. Experimental analysis stated that proposed SHKA scheme provides lightweight end-to-end key establishment in every session. The CPS devices generates the session key of 128 bit long. The minimum key size is implemented to provide effective security in IoT 4G communication with minimal execution time. The simulation results demonstrated that SHKA model exhibits effective cyber-attacks for the constraint devices to improve performance of IoT network.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128927935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blockchain Technologies in Healthcare System for Real Time Applications Using IoT and Deep Learning Techniques","authors":"Alcides Bernardo Tello, Jiuhong Xing, A. L. Patil, Lalitkumar Premchandra Patil, Shabnam Sayyad","doi":"10.17762/ijcnis.v14i3.5621","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5621","url":null,"abstract":"Data transparency, flexible access, immutability, privacy, audit, traceability, data provenance, trust, and security are fundamental issues for modern healthcare data management systems. As a promising new technology, blockchain has the potential to enhance healthcare data management functions by boosting data efficiency and guaranteeing trust. The present research looked into the benefits of blockchain technology in healthcare and the challenges that have prevented its widespread implementation so far. Healthcare organisations around the world are using a variety of methods to modernise into more effective, coordinated and user-cantered structures. There is an increase in both human effort and security risks when dealing with massive amounts of data, such as reports and images for each individual. Internet of Things (IoT) solutions in healthcare aim to address these problems by enhancing patient care while reducing costs through more effective use of healthcare resources. However, many different types of intrusion can pose serious risks to IoT devices. In some cases, doctors will insist that their patients use only certain labs or pharmacies, regardless of the quality of the services they provide, simply to increase the doctor's bottom line. Because of this, protecting data is essential when discussing the Internet of Things. To solve these problems, Blockchain technology has emerged as the most reliable method for protecting the privacy of control systems in real time. In this paper, we will introduce a CNN-based healthcare data security framework using the blockchain technique by generating the hash of each data point, which will alert all users of the blockchain network to any unauthorised changes to data or breaches in the supply of medicines.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115302363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Analysis of MEMS Based Oscillator for High Frequency Wireless Communication Systems","authors":"Dhifaf Talal Shakir, Hassan Jassim Al-Qureshy, Saad S. Hreshee","doi":"10.17762/ijcnis.v14i3.5574","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5574","url":null,"abstract":"The frequency oscillator is a basic component found in many electrical, electronic, and communications circuits and systems. Oscillators come in a variety of shapes and sizes, depending on the frequency range employed in a given application. Some applications need oscillators that generate low frequencies and other applications need oscillators that generate extremely high and high frequencies. As a result of the expansion and speed of modern technologies, new oscillators appeared that operating at extremely high frequencies. Most wireless communication systems are constrained in their performance by the accuracy and stability of the reference frequency. Because of its compatibility with silicon, micro-electro-mechanical system (MEMS) is the preferred technology for circuit integration and power reduction. MEMS are a rapidly evolving area of advanced microelectronics. The integration of electrical and mechanical components at the micro size is referred to as a MEMS. MEMS based oscillators have demonstrated tremendous high frequency application potential in recent years. This is owing to their great characteristics such as small size, integration of CMOS IC technology, high frequency-quality factor product, low power consumption, and cheap batch manufacturing cost. This paper's primary objective is to describe the performance of MEMS oscillator technology in high-frequency applications, as well as to discuss the challenges of developing a new MEMS oscillator capable of operating at gigahertz frequencies.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cloud Computing Based Computing System for Women's Higher Education in Isolated Areas","authors":"Ranjani M, Unggul Priyadi, Anas A. Salameh, Mochamad Ali Imron, K. Kishore","doi":"10.17762/ijcnis.v14i3.5568","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5568","url":null,"abstract":"A new chapter in information technology is opened by cloud computing in computer science and engineering education. Understanding the importance of using cloud computing (CC) in institutions of higher learning is the aim of this research. This analysis shows some of the benefits that cloud computing can provide to higher education, assesses some of the most significant challenges that academics may encounter as a result of its implementation, and suggests some initial steps toward its adoption while mitigating the risks associated. Enterprise apps have migrated in large numbers to the cloud in recent years. One of the challenges posed by cloud applications is the challenge of allocating resources to the application to ensure a service level along dimensions like performance, availability, and dependability. To do this, a system based on the infrastructure of governmental bodies, non-governmental organisations (NGOs), academic institutions, and other providers of social services has been established. The results of this analysis demonstrate that it is possible to use a few variables, including administrative bodies and governments, internal stakeholders, cloud suppliers, firm attributes, socio-political changes, IT framework, and others, to understand how CC adoption methodologies are used in higher education institutions. In addition to providing insight into how cloud providers, advisers, governments, and academics see various market demands and how they respond to these expectations while expanding services provided by CC in higher education institutions, this analysis opens opportunities for future research. The implications for practice can aid decision-makers in utilizing CC services legally.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133166456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Supervised Machine Learning Based Intrusion Detection Model for Detecting Cyber-Attacks Against Computer System","authors":"Santhosh Kumar Chenniappanadar, Gnanamurthy Sundharamurthy, Vinoth Kumar Sakthivelu, V. K. Kaliappan","doi":"10.17762/ijcnis.v14i3.5567","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5567","url":null,"abstract":"Internet usage has become essential for correspondence in almost every calling in our digital age. To protect a network, an effective intrusion detection system (IDS) is vital. Intrusion Detection System is a software application to detect network intrusion using various machine learning algorithms. The function of the expert has been lessened by machine learning approaches since knowledge is taken directly from the data. The fact that it makes use of all the features of an information packet spinning in the network for intrusion detection is weakened by the employment of various methods for detecting intrusions, such as statistical models, safe system approaches, etc. Machine learning has become a fundamental innovation for cyber security. Two of the key types of attacks that plague businesses, as proposed in this paper, are Denial of Service (DOS) and Distributed Denial of Service (DDOS) attacks. One of the most disastrous attacks on the Internet of Things (IOT) is a denial of service.  Two diverse Machine Learning techniques are proposed in this research work, mainly Supervised learning. To achieve this goal, the paper represents a regression algorithm, which is usually used in data science and machine learning to forecast the future. An innovative approach to detecting is by using the Machine Learning algorithm by mining application-specific logs. Cyber security is a way of providing their customers the peace of mind they need knowing that they have secured their information and money.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122766480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data Security Enhancement in 4G Vehicular Networks Based on Reinforcement Learning for Satellite Edge Computing","authors":"Luis Alberto Núñez Lira, K. A. Kumari, R. Raman, Ardhariksa Zukhruf Kurniullah, Santiago Aquiles Gallarday Morales, Tula Del Carmen Espinoza Cordero","doi":"10.17762/ijcnis.v14i3.5571","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5571","url":null,"abstract":"The vehicular network provides the dedicated short-range communication (DSRC) with IEEE 802.11p standard. The VANET model comprises of cellular vehicle-to-everything communication with wireless communication technology. Vehicular Edge Computing exhibits the promising technology to provide promising Intelligent Transport System Services. Smart application and urban computing. Satellite edge computing model is adopted in vehicular networks to provide services to the VANET communication for the management of computational resources for the end-users to provide access to low latency services for maximal execution of service. The satellite edge computing model implemented with the 4G vehicular communication network model subjected to data security issues. This paper presented a Route Computation Deep Learning Model (RCDL) to improve security in VANET communication with 4G technology. The RCDL model uses the route establishment model with the optimal route selection. The compute route is transmitted with the cryptographic scheme model for the selection of optimal route identified from the satellite edge computing model. The proposed RCDL scheme uses the deep learning-based reinforcement learning scheme for the attack prevention in the VANET environment employed with the 4G technology communication model. The simulation results expressed that proposed RCDL model achieves the higher PDR value of 98% which is ~6% higher than the existing model. The estimation of end-to-end delay is minimal for the RCDL scheme and improves the VANET communication.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"245 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122300375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous Underwater Vehicle: 5G Network Design and Simulation Based on Mimetic Technique Control System","authors":"Fidel Castro-Cayllahua, Juan Luis Meza Carhuancho, Carlos Mario Fernández Díaz, Zoila Mercedes Collantes Inga, Tariq Rasheed, J. Cotrina-Aliaga","doi":"10.17762/ijcnis.v14i3.5566","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5566","url":null,"abstract":"The Internet of Underwater Things (IoUT) exhibits promising advancement with underwater acoustic wireless network communication (UWSN). Conventionally, IoUT has been utilized for the offshore monitoring and exploration of the environment within the underwater region. The data exchange between the IoUT has been performed with the 5G enabled-communication to establish the connection with the futuristic underwater monitoring. However, the acoustic waves in underwater communication are subjected to longer propagation delay and higher transmission energy. To overcome those issues autonomous underwater vehicle (AUV) is implemented for the data collection and routing based on cluster formation. This paper developed a memetic algorithm-based AUV monitoring system for the underwater environment. The proposed Autonomous 5G Memetic (A5GMEMETIC) model performs the data collection and transmission to increase the USAN performance. The A5GMEMETIC model data collection through the dynamic unaware clustering model minimizes energy consumption. The A5GMemetic optimizes the location of the nodes in the underwater environment for the optimal data path estimation for the data transmission in the network. Simulation analysis is performed comparatively with the proposed A5Gmemetic with the conventional AEDG, DGS, and HAMA models. The comparative analysis expressed that the proposed A5GMeMEMETIC model exhibits the ~12% increased packet delivery ratio (PDR), ~9% reduced delay and ~8% improved network lifetime.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125255648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ECG Analysis-Based Cardiac Disease Prediction Using Signal Feature Selection with Extraction Based on AI Techniques","authors":"A. Sharma, Dr. H.S. Hota","doi":"10.17762/ijcnis.v14i3.5573","DOIUrl":"https://doi.org/10.17762/ijcnis.v14i3.5573","url":null,"abstract":"ECG (Electrocardiogram) performs classification using a machine learning model for processing different features in the ECG signal. The electrical activity of the heart is computed with the ECG signal with machine learning library. The key issue in the handling of ECG signals is an estimation of irregularities to evaluate the health status of patients. The ECG signal evaluate the impulse waveform for the specialized tissues in the cardiac heart diseases. However, the ECG signal comprises of the different difficulties associated with waveform estimation to derive certain features. Through machine learning (ML) model the input features are computed with input ECG signals. In this paper, proposed a Noise QRS Feature to evaluate the features in the ECG signals for the effective classification. The Noise QRS Feature model computes the ECG signal features of the waveform sequences.  Initially, the signal is pre-processed with the Finite Impulse response (FIR) filter for the analysis of ECG signal. The features in the ECG signal are processed and computed with the QRS signal responses in the ECG signal. The Noise QRS Feature evaluate the ECG signal with the kNN for the estimation and classification of features in the ECG signals. The performance of the proposed Noise QRS Feature features are comparatively examined with the Discrete Wavelet Transform (DWT), Dual-Tree Complex Wavelet Transforms (DTCWT) and Discrete Orthonormal Stockwell Transform (DOST) and the machine learning model Cascade Feed Forward Neural Network (CFNN), Feed Forward Neural Network (FFNN). Simulation analysis expressed that the proposed Noise QRS Feature exhibits a higher classification accuracy of 99% which is ~6 – 7% higher than the conventional classifier model.","PeriodicalId":232613,"journal":{"name":"Int. J. Commun. Networks Inf. Secur.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129804036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}