Pub Date : 2023-06-21DOI: 10.1142/s021926592350010x
Xiao Xu, Zhuoma Gao, Lei Meng, Qin Tong
Let [Formula: see text] be an integer. The [Formula: see text]-factor of a graph [Formula: see text] is a spanning subgraph [Formula: see text] if [Formula: see text] for all [Formula: see text], and the [Formula: see text]-factor is a subgraph whose each component is either [Formula: see text] or [Formula: see text]. In this paper, we give the lower bounds with regard to tight toughness, isolated toughness and binding number to guarantee the existence of the [Formula: see text]-factors and [Formula: see text]-factors for a graph.
{"title":"Tight Toughness, Isolated Toughness and Binding Number Bounds for the [1,n]-Factors and the {K2,Ci≥4}-Factors","authors":"Xiao Xu, Zhuoma Gao, Lei Meng, Qin Tong","doi":"10.1142/s021926592350010x","DOIUrl":"https://doi.org/10.1142/s021926592350010x","url":null,"abstract":"Let [Formula: see text] be an integer. The [Formula: see text]-factor of a graph [Formula: see text] is a spanning subgraph [Formula: see text] if [Formula: see text] for all [Formula: see text], and the [Formula: see text]-factor is a subgraph whose each component is either [Formula: see text] or [Formula: see text]. In this paper, we give the lower bounds with regard to tight toughness, isolated toughness and binding number to guarantee the existence of the [Formula: see text]-factors and [Formula: see text]-factors for a graph.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80472320","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}
Pub Date : 2023-06-21DOI: 10.1142/s0219265923500081
Sheena Mohammed, Sridevi Rangu
Privacy and security are the most concerning topics while using cloud-based applications. Malware detection in cloud applications is important in identifying application malware activity. So, a novel Goat-based Recurrent Forensic Mechanism (GbRFM) is used to detect the attack and provide the attack type in cloud-based applications. At first, the dataset is pre-processed in the hidden phase, and the errorless features are extracted. The proposed model also trains the output of the hidden layer to identify and classify the malware. The wild goat algorithm enhances the identification rate by accurately detecting the attack. Using the NSL-KDD data, the preset research was verified, and the outcomes were evaluated. The performance assessment indicates that the developed model gained a 99.26% accuracy rate for the NSL-KDD dataset. Moreover, to validate the efficiency of the proposed model, the outcomes are compared with other techniques. The comparison analysis proved that the proposed model attained better results.
{"title":"To Secure the Cloud Application Using a Novel Efficient Deep Learning-Based Forensic Framework","authors":"Sheena Mohammed, Sridevi Rangu","doi":"10.1142/s0219265923500081","DOIUrl":"https://doi.org/10.1142/s0219265923500081","url":null,"abstract":"Privacy and security are the most concerning topics while using cloud-based applications. Malware detection in cloud applications is important in identifying application malware activity. So, a novel Goat-based Recurrent Forensic Mechanism (GbRFM) is used to detect the attack and provide the attack type in cloud-based applications. At first, the dataset is pre-processed in the hidden phase, and the errorless features are extracted. The proposed model also trains the output of the hidden layer to identify and classify the malware. The wild goat algorithm enhances the identification rate by accurately detecting the attack. Using the NSL-KDD data, the preset research was verified, and the outcomes were evaluated. The performance assessment indicates that the developed model gained a 99.26% accuracy rate for the NSL-KDD dataset. Moreover, to validate the efficiency of the proposed model, the outcomes are compared with other techniques. The comparison analysis proved that the proposed model attained better results.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89833021","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}
Pub Date : 2023-06-10DOI: 10.1142/s021926592350007x
Zhao Wang, Hongfang Liu, Yuhu Liu
The concept of [Formula: see text]-independent set, introduced by Fink and Jacobson in 1986, is a natural generalization of classical independence set. A k-independent set is a set of vertices whose induced subgraph has maximum degree at most [Formula: see text]. The k-independence number of [Formula: see text], denoted by [Formula: see text], is defined as the maximum cardinality of a [Formula: see text]-independent set of [Formula: see text]. As a natural counterpart of the [Formula: see text]-independence number, we introduced the concept of [Formula: see text]-edge-independence number. An edge set [Formula: see text] in [Formula: see text] is called k-edge-independent if the maximum degree of the subgraph induced by the edges in [Formula: see text] is less or equal to [Formula: see text]. The k-edge-independence number, denoted [Formula: see text], is defined as the maximum cardinality of a [Formula: see text]-edge-independent set. In this paper, we study the Nordhaus–Gaddum-type results for the parameter [Formula: see text] and [Formula: see text]. We obtain sharp upper and lower bounds of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for a graph [Formula: see text] of order [Formula: see text]. Some graph classes attaining these bounds are also given.
{"title":"Nordhaus–Gaddum-Type Results for the k-Independent Number of Graphs","authors":"Zhao Wang, Hongfang Liu, Yuhu Liu","doi":"10.1142/s021926592350007x","DOIUrl":"https://doi.org/10.1142/s021926592350007x","url":null,"abstract":"The concept of [Formula: see text]-independent set, introduced by Fink and Jacobson in 1986, is a natural generalization of classical independence set. A k-independent set is a set of vertices whose induced subgraph has maximum degree at most [Formula: see text]. The k-independence number of [Formula: see text], denoted by [Formula: see text], is defined as the maximum cardinality of a [Formula: see text]-independent set of [Formula: see text]. As a natural counterpart of the [Formula: see text]-independence number, we introduced the concept of [Formula: see text]-edge-independence number. An edge set [Formula: see text] in [Formula: see text] is called k-edge-independent if the maximum degree of the subgraph induced by the edges in [Formula: see text] is less or equal to [Formula: see text]. The k-edge-independence number, denoted [Formula: see text], is defined as the maximum cardinality of a [Formula: see text]-edge-independent set. In this paper, we study the Nordhaus–Gaddum-type results for the parameter [Formula: see text] and [Formula: see text]. We obtain sharp upper and lower bounds of [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for a graph [Formula: see text] of order [Formula: see text]. Some graph classes attaining these bounds are also given.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90752123","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}
Pub Date : 2023-05-31DOI: 10.1142/s0219265923500068
Zhiwei Guo
For a graph [Formula: see text] and positive integers [Formula: see text], [Formula: see text], a [Formula: see text]-tree-vertex coloring of [Formula: see text] refers to a [Formula: see text]-vertex coloring of [Formula: see text] satisfying every component of each induced subgraph generated by every set of vertices with the same color forms a tree with maximum degree not larger than [Formula: see text], and it is called equitable if the difference between the cardinalities of every pair of sets of vertices with the same color is at most [Formula: see text]. The strong equitable vertex [Formula: see text]-arboricity of [Formula: see text], denoted by [Formula: see text], is defined as the least positive integer [Formula: see text] satisfying [Formula: see text], which admits an equitable [Formula: see text]-tree-vertex coloring for each integer [Formula: see text] with [Formula: see text]. The strong equitable vertex [Formula: see text]-arboricity of a graph is very useful in graph theory applications such as load balance in parallel memory systems, constructing timetables and scheduling. In this paper, we present the tight upper and lower bounds on [Formula: see text] for an arbitrary graph [Formula: see text] with [Formula: see text] vertices and a given integer [Formula: see text] with [Formula: see text], and we characterize the extremal graphs [Formula: see text] with [Formula: see text], [Formula: see text], [Formula: see text], respectively. Based on the above extremal results, we further obtain the Nordhaus–Gaddum-type results for [Formula: see text] of graphs [Formula: see text] with [Formula: see text] vertices for a given integer [Formula: see text] with [Formula: see text].
{"title":"Nordhaus–Gaddum-Type Results for the Strong Equitable Vertex k-Arboricity of Graphs","authors":"Zhiwei Guo","doi":"10.1142/s0219265923500068","DOIUrl":"https://doi.org/10.1142/s0219265923500068","url":null,"abstract":"For a graph [Formula: see text] and positive integers [Formula: see text], [Formula: see text], a [Formula: see text]-tree-vertex coloring of [Formula: see text] refers to a [Formula: see text]-vertex coloring of [Formula: see text] satisfying every component of each induced subgraph generated by every set of vertices with the same color forms a tree with maximum degree not larger than [Formula: see text], and it is called equitable if the difference between the cardinalities of every pair of sets of vertices with the same color is at most [Formula: see text]. The strong equitable vertex [Formula: see text]-arboricity of [Formula: see text], denoted by [Formula: see text], is defined as the least positive integer [Formula: see text] satisfying [Formula: see text], which admits an equitable [Formula: see text]-tree-vertex coloring for each integer [Formula: see text] with [Formula: see text]. The strong equitable vertex [Formula: see text]-arboricity of a graph is very useful in graph theory applications such as load balance in parallel memory systems, constructing timetables and scheduling. In this paper, we present the tight upper and lower bounds on [Formula: see text] for an arbitrary graph [Formula: see text] with [Formula: see text] vertices and a given integer [Formula: see text] with [Formula: see text], and we characterize the extremal graphs [Formula: see text] with [Formula: see text], [Formula: see text], [Formula: see text], respectively. Based on the above extremal results, we further obtain the Nordhaus–Gaddum-type results for [Formula: see text] of graphs [Formula: see text] with [Formula: see text] vertices for a given integer [Formula: see text] with [Formula: see text].","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74424181","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}
Pub Date : 2023-05-29DOI: 10.1142/s0219265923990013
{"title":"Author Index Volume 23 (2023)","authors":"","doi":"10.1142/s0219265923990013","DOIUrl":"https://doi.org/10.1142/s0219265923990013","url":null,"abstract":"","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78759893","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}
Pub Date : 2023-01-05DOI: 10.1142/s0219265922420038
Zhexiong Cui, J. Ren
Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.
{"title":"Mobile Big Data Analytics for Human Behavior Recognition in Wireless Sensor Network Based on Transfer Learning","authors":"Zhexiong Cui, J. Ren","doi":"10.1142/s0219265922420038","DOIUrl":"https://doi.org/10.1142/s0219265922420038","url":null,"abstract":"Big data analysis of human behavior can provide the basis and support for the application of various scenarios. Using sensors for human behavior analysis is an effective means of identification method, which is very valuable for research. To address the problems of low recognition accuracy, low recognition efficiency of traditional human behavior recognition (HBR) algorithms in complex scenes, in this paper, we propose an HBR algorithm for Mobile Big data analytics in wireless sensor network using improved transfer learning. First, different wireless sensors are fused to obtain human behavior mobile big data, and then by analyzing the importance of human behavior features (HBF), the dynamic change parameters of HBF extraction threshold are calculated. Second, combined with the dynamic change parameters of threshold, the HBF of complex scenes are extracted. Finally, the best classification function of human behavior in complex scenes is obtained by using the classification function of HBF in complex scenes. Human behavior in complex scenes is classified according to the HBF in the feature set. The HBR algorithm is designed by using the improved transfer learning network to realize the recognition of human behavior in complex scenes. The results show that the proposed algorithm can accurately recognize up to 22 HBF points, and can control the HBR time within 2 s. The human behavior false recognition rate of miscellaneous scenes is less than 10%. The recognition speed is above 10/s, and the recall rate can reach more than 98%, which improves the HBR ability of complex scenes.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73336443","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}
Pub Date : 2022-12-31DOI: 10.1142/s021926592242004x
Huiping Li
Remote sensing image (RSI) segmentation is an effective method to interpret remote sensing information and an important means of remote sensing data information processing. Traditional RSI segmentation methods have some problems such as poor segmentation accuracy and low similarity difference measurement. Therefore, we propose a multi-scale segmentation (MSS) method for remote sensing big data image. First, the segmentation scale of RSI is divided, and the quantitative value of histogram band is used to calculate the similarity index between different objects; Second, the parameters in the same spot are improved based on the maximum area method to determine the shape factor of RSI; Finally, the object closure model is established to clarify the region conversion cost, and the RSI is dynamically segmented based on Multi-scale convolutional neural networks; The MSS algorithm of RSI is designed, and the MSS method of RSI is obtained. The results show that the maximum similarity difference measure of the proposed method is 0.648, and the similarity difference measure always remains the largest. The maximum recall of RSI is 0.954, and the highest recall is 0.988, indicating that the RSI segmentation accuracy of the proposed method is good.
{"title":"Multi-Scale Segmentation Method of Remote Sensing Big Data Image Using Deep Learning","authors":"Huiping Li","doi":"10.1142/s021926592242004x","DOIUrl":"https://doi.org/10.1142/s021926592242004x","url":null,"abstract":"Remote sensing image (RSI) segmentation is an effective method to interpret remote sensing information and an important means of remote sensing data information processing. Traditional RSI segmentation methods have some problems such as poor segmentation accuracy and low similarity difference measurement. Therefore, we propose a multi-scale segmentation (MSS) method for remote sensing big data image. First, the segmentation scale of RSI is divided, and the quantitative value of histogram band is used to calculate the similarity index between different objects; Second, the parameters in the same spot are improved based on the maximum area method to determine the shape factor of RSI; Finally, the object closure model is established to clarify the region conversion cost, and the RSI is dynamically segmented based on Multi-scale convolutional neural networks; The MSS algorithm of RSI is designed, and the MSS method of RSI is obtained. The results show that the maximum similarity difference measure of the proposed method is 0.648, and the similarity difference measure always remains the largest. The maximum recall of RSI is 0.954, and the highest recall is 0.988, indicating that the RSI segmentation accuracy of the proposed method is good.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79319980","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}
Pub Date : 2022-12-19DOI: 10.1142/s0219265921490025
Ashok Kumar Rai, A. K. Daniel
A wireless sensor network (WSN) can be used for various purposes, including area monitoring, health care, smart cities, and defence. Numerous complex issues arise in these applications, including energy efficiency, coverage, and intruder detection. Intruder detection is a significant obstacle in various wireless sensor network applications. It causes data fusion that jeopardizes the network’s confidentiality, lifespan, and coverage. Various algorithm has been proposed for intruder detection where each node act as an agent, or some monitoring nodes are deployed for intruder detection. The proposed protocol detects intruders by transmitting a known bit from the Cluster Head (CH) to all nodes. The legal nodes must acknowledge their identification to the CH in order to be valid; otherwise, if the CH receives an incorrect acknowledgement from a node or receives no acknowledgement at all, it is an intruder. The proposed protocol assists in protecting sensor data from unauthorized access and detecting the intruder with its location through the identity of other legal nodes. The simulation results show that the proposed protocol delivers better results for identifying intruders for various parameters.
{"title":"Energy-Efficient Model for Intruder Detection Using Wireless Sensor Network","authors":"Ashok Kumar Rai, A. K. Daniel","doi":"10.1142/s0219265921490025","DOIUrl":"https://doi.org/10.1142/s0219265921490025","url":null,"abstract":"A wireless sensor network (WSN) can be used for various purposes, including area monitoring, health care, smart cities, and defence. Numerous complex issues arise in these applications, including energy efficiency, coverage, and intruder detection. Intruder detection is a significant obstacle in various wireless sensor network applications. It causes data fusion that jeopardizes the network’s confidentiality, lifespan, and coverage. Various algorithm has been proposed for intruder detection where each node act as an agent, or some monitoring nodes are deployed for intruder detection. The proposed protocol detects intruders by transmitting a known bit from the Cluster Head (CH) to all nodes. The legal nodes must acknowledge their identification to the CH in order to be valid; otherwise, if the CH receives an incorrect acknowledgement from a node or receives no acknowledgement at all, it is an intruder. The proposed protocol assists in protecting sensor data from unauthorized access and detecting the intruder with its location through the identity of other legal nodes. The simulation results show that the proposed protocol delivers better results for identifying intruders for various parameters.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80413299","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}
Pub Date : 2022-11-21DOI: 10.1142/s0219265922420026
Ma Jingze, Zhan Guoye, Yang Fan, Chen Xingpei
Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good.
{"title":"Data-Driven Information Management Method of Power Supply Chains Using Mobile Cloud Computing","authors":"Ma Jingze, Zhan Guoye, Yang Fan, Chen Xingpei","doi":"10.1142/s0219265922420026","DOIUrl":"https://doi.org/10.1142/s0219265922420026","url":null,"abstract":"Based on the spring, spring MVC and MyBatis structures of the cloud platform SSM framework, an information management platform for power grid material supply chain is built. The data layer uses a variety of sensors to collect power grid material supply chain information, and the information is fed back to the data storage layer after being integrated by the logical reorganization function of the persistence layer. The data storage layer uses the multi-sensor supply chain information fusion method based on paste progress to fuse the information and store it in the database. The business logic layer calls the information in the database and uses the improved k-means clustering algorithm to detect the abnormal supply chain data information. After calculation and data control by the control layer, the data management results are displayed through the presentation layer. The experimental results show that the absolute error of data fusion is very low. It can effectively cluster data information and distinguish outlier anomaly information at the same time, and the effect of information management is good.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72393174","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}
Pub Date : 2022-10-13DOI: 10.1142/s0219265922430010
E. Babu, Ilaiah Kavati, Ramalingaswamy Cheruku, Soumyabrata Nayak, Uttam Ghosh
The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the “security” of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device’s keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required.
{"title":"Trust-Based Permissioned Blockchain Network for Identification and Authentication of Internet of Smart Devices: An E-Commerce Prospective","authors":"E. Babu, Ilaiah Kavati, Ramalingaswamy Cheruku, Soumyabrata Nayak, Uttam Ghosh","doi":"10.1142/s0219265922430010","DOIUrl":"https://doi.org/10.1142/s0219265922430010","url":null,"abstract":"The Internet of Things refers to billions of devices around us connected to the wireless internet. These IoT devices are memory-constrained devices that can collect and transfer data over the network without human assistance. Recently, IoT is materialized in retail commerce, transforming from recognition service to post-purchase engagement service. IoT examples in retail commerce are smart refrigerators, smart speakers, smart washing machines, smart automobiles, and automatic re-purchase of groceries using RFID tags. Despite the rise, one of the significant inconveniences slowing rapid adaption is the “security” of these devices, which are vulnerable to various attacks. One such attack is Distributed Denial-of-Service (DDoS) attacks targeting offline or online sensitive data. Hence, a lightweight cryptographic mechanism needs to establish secure communication among IoT devices. This paper presents the solution to secure communication among IoT devices using a permissioned blockchain network. Specifically, in this work, we proposed a mechanism for identifying and authenticating the smart devices using the Elliptic-curve cryptography (ECC) protocol. This proposed work uses permissioned blockchain infrastructure, which acts as a source of trust that aids the authentication process using ECC cryptosystem. In addition, lightweight Physical Unclonable Function (PUF) technology is also used to securely store the device’s keys. Using this technology, the private keys need not be stored anywhere, but it is generated on the fly from the trusted zone whenever the private key is required.","PeriodicalId":53990,"journal":{"name":"JOURNAL OF INTERCONNECTION NETWORKS","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73697485","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}
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