Pub Date : 2022-05-19DOI: 10.1108/ijpcc-02-2022-0042
Priyanka kumari Bhansali, Dilendra Hiran, K. Gulati
Purpose The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server. Design/methodology/approach SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT. Findings It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively. Originality/value In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive,
{"title":"Secure data collection and transmission for IoMT architecture integrated with federated learning","authors":"Priyanka kumari Bhansali, Dilendra Hiran, K. Gulati","doi":"10.1108/ijpcc-02-2022-0042","DOIUrl":"https://doi.org/10.1108/ijpcc-02-2022-0042","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.\u0000\u0000\u0000Design/methodology/approach\u0000SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.\u0000\u0000\u0000Findings\u0000It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.\u0000\u0000\u0000Originality/value\u0000In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, ","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41430736","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 : 2022-05-19DOI: 10.1108/ijpcc-02-2022-0041
Priyanka kumari Bhansali, Dilendra Hiran, Hemant Kothari, K. Gulati
Purpose The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud. Design/methodology/approach This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design. Findings For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning. Originality/value The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has
{"title":"Cloud-based secure data storage and access control for internet of medical things using federated learning","authors":"Priyanka kumari Bhansali, Dilendra Hiran, Hemant Kothari, K. Gulati","doi":"10.1108/ijpcc-02-2022-0041","DOIUrl":"https://doi.org/10.1108/ijpcc-02-2022-0041","url":null,"abstract":"\u0000Purpose\u0000The purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.\u0000\u0000\u0000Design/methodology/approach\u0000This section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.\u0000\u0000\u0000Findings\u0000For health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.\u0000\u0000\u0000Originality/value\u0000The Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41500359","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 : 2022-05-17DOI: 10.1108/ijpcc-02-2022-0082
Da’ad Ahmad Albalawneh, M. A. Mohamed
Purpose Using a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations. Design/methodology/approach In transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools. Findings This study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. Originality/value Furthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.
目的利用Al Salt city的实时道路网络和历史交通数据，提出一种新的基于联合遗传算法的优化技术来解决动态车辆路径问题。使用遗传算法求解器，300条染色体（路线）的估计路由时间是30代中最短、最有效的。设计/方法/方法在交通系统中，路线规划技术的目标已经从关注道路指挥者到道路使用者进行了修订。因此，新的交通系统使用先进的技术来支持驾驶员，并为他们提供所需的道路信息和服务，以减少交通拥堵并改善路线问题。近几十年来，人们对如何为车辆找到有效和合适的路线进行了大量研究，称为车辆路线问题（VRP）。为了确定最佳路线，VRP使用实时信息获取地理信息系统（GIS）工具。发现本研究旨在使用ArcGIS网络分析师开发一种路线规划工具，以提高成本和服务质量，并考虑几个因素，根据用户的偏好确定最佳路线。独创性/价值此外，使用ArcGIS网络分析师开发路线规划工具，以提高成本和服务质量，并考虑几个因素，根据用户的偏好确定最佳路线。考虑到影响车辆到达时间和造成延误的因素，使用自适应遗传算法（GA）来确定最佳时间路线。此外，ArcGIS的网络分析工具用于使用实时地图根据用户的偏好确定最佳路线。
{"title":"A new federated genetic algorithm-based optimization technique for multi-criteria vehicle route planning using ArcGIS network analyst","authors":"Da’ad Ahmad Albalawneh, M. A. Mohamed","doi":"10.1108/ijpcc-02-2022-0082","DOIUrl":"https://doi.org/10.1108/ijpcc-02-2022-0082","url":null,"abstract":"\u0000Purpose\u0000Using a real-time road network combined with historical traffic data for Al-Salt city, the paper aims to propose a new federated genetic algorithm (GA)-based optimization technique to solve the dynamic vehicle routing problem. Using a GA solver, the estimated routing time for 300 chromosomes (routes) was the shortest and most efficient over 30 generations.\u0000\u0000\u0000Design/methodology/approach\u0000In transportation systems, the objective of route planning techniques has been revised from focusing on road directors to road users. As a result, the new transportation systems use advanced technologies to support drivers and provide them with the road information they need and the services they require to reduce traffic congestion and improve routing problems. In recent decades, numerous studies have been conducted on how to find an efficient and suitable route for vehicles, known as the vehicle routing problem (VRP). To identify the best route, VRP uses real-time information-acquired geographical information systems (GIS) tools.\u0000\u0000\u0000Findings\u0000This study aims to develop a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences.\u0000\u0000\u0000Originality/value\u0000Furthermore, developing a route planning tool using ArcGIS network analyst to enhance both cost and service quality measures, taking into account several factors to determine the best route based on the users’ preferences. An adaptive genetic algorithm (GA) is used to determine the optimal time route, taking into account factors that affect vehicle arrival times and cause delays. In addition, ArcGIS' Network Analyst tool is used to determine the best route based on the user's preferences using a real-time map.\u0000","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45100031","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 : 2022-04-26DOI: 10.1108/ijpcc-01-2022-0011
Elham Kariri, Kusum Yadav
Purpose In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively. Design/methodology/approach A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model. Findings When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly. Originality/value Additionally, it reduces the amount of routing overhead and the average end-to-end delay.
{"title":"Trusted routing protocol for federated UAV ad hoc network","authors":"Elham Kariri, Kusum Yadav","doi":"10.1108/ijpcc-01-2022-0011","DOIUrl":"https://doi.org/10.1108/ijpcc-01-2022-0011","url":null,"abstract":"\u0000Purpose\u0000In the final step, the trust model is applied to the on-demand federated multipath distance vector routing protocol (AOMDV) to introduce path trust as a foundation for routing selection in the route discovery phase, construct a trusted path, and implement a path warning mechanism to detect malicious nodes in the route maintenance phase, respectively.\u0000\u0000\u0000Design/methodology/approach\u0000A trust-based on-demand multipath distance vector routing protocol is being developed to address the problem of flying ad-hoc network being subjected to internal attacks and experiencing frequent connection interruptions. Following the construction of the node trust assessment model and the presentation of trust evaluation criteria, the data packet forwarding rate, trusted interaction degree and detection packet receipt rate are discussed. In the next step, the direct trust degree of the adaptive fuzzy trust aggregation network compute node is constructed. After then, rely on the indirect trust degree of neighbouring nodes to calculate the trust degree of the node in the network. Design a trust fluctuation penalty mechanism, as a second step, to defend against the switch attack in the trust model.\u0000\u0000\u0000Findings\u0000When compared to the lightweight trust-enhanced routing protocol (TEAOMDV), it significantly improves the data packet delivery rate and throughput of the network significantly.\u0000\u0000\u0000Originality/value\u0000Additionally, it reduces the amount of routing overhead and the average end-to-end delay.\u0000","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45199749","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 : 2022-01-25DOI: 10.1108/ijpcc-07-2021-0158
A. Maddali, Habibullah Khan
Purpose Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist. Design/methodology The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation. Findings Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques. Original value A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.
{"title":"Classification of disordered patient’s voice by using pervasive computational algorithms","authors":"A. Maddali, Habibullah Khan","doi":"10.1108/ijpcc-07-2021-0158","DOIUrl":"https://doi.org/10.1108/ijpcc-07-2021-0158","url":null,"abstract":"\u0000Purpose\u0000Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.\u0000\u0000\u0000Design/methodology\u0000The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.\u0000\u0000\u0000Findings\u0000Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.\u0000\u0000\u0000Original value\u0000A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.\u0000","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46350234","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 : 2022-01-21DOI: 10.1108/ijpcc-10-2021-0259
G. K, Brahmananda S.H.
Purpose IoT has a wide range of applications in the health-care sector and has captured the interest of many academic and industrial communities. The health IoT devices suffer from botnet attacks as all the devices are connected to the internet. An army of compromised bots may form to launch a DDoS attack, steal confidential data of patients and disrupt the service, and hence detecting this army of bots is paramount. This study aims to detect botnet attacks in health IoT devices using the deep learning technique. Design/methodology/approach This paper focuses on designing a method to protect health IoT devices from botnet attacks by constantly observing communication network traffic and classifying them as benign and malicious flow. The proposed algorithm analyzes the health IoT network traffic through implementing Bidirectional long-short term memory, a deep learning technique. The IoT-23 data set is considered for this research as it includes diverse botnet attack scenarios. Findings The performance of the proposed method is evaluated using attack prediction accuracy. It results in the highest accuracy of 84.8%, classifying benign and malicious traffic. Originality/value The proposed method constantly monitors the health IoT network to detect botnet attacks and classifies the traffic as benign or attack. The system is implemented using the BiLSTM algorithm and trained using the IoT-23 data set. The diversity of attack scenarios of the IoT-23 data set demonstrates the proposed algorithm's competence in detecting botnet types in a heterogeneous environment.
{"title":"Network traffic analysis through deep learning for detection of an army of bots in health IoT network","authors":"G. K, Brahmananda S.H.","doi":"10.1108/ijpcc-10-2021-0259","DOIUrl":"https://doi.org/10.1108/ijpcc-10-2021-0259","url":null,"abstract":"\u0000Purpose\u0000IoT has a wide range of applications in the health-care sector and has captured the interest of many academic and industrial communities. The health IoT devices suffer from botnet attacks as all the devices are connected to the internet. An army of compromised bots may form to launch a DDoS attack, steal confidential data of patients and disrupt the service, and hence detecting this army of bots is paramount. This study aims to detect botnet attacks in health IoT devices using the deep learning technique.\u0000\u0000\u0000Design/methodology/approach\u0000This paper focuses on designing a method to protect health IoT devices from botnet attacks by constantly observing communication network traffic and classifying them as benign and malicious flow. The proposed algorithm analyzes the health IoT network traffic through implementing Bidirectional long-short term memory, a deep learning technique. The IoT-23 data set is considered for this research as it includes diverse botnet attack scenarios.\u0000\u0000\u0000Findings\u0000The performance of the proposed method is evaluated using attack prediction accuracy. It results in the highest accuracy of 84.8%, classifying benign and malicious traffic.\u0000\u0000\u0000Originality/value\u0000The proposed method constantly monitors the health IoT network to detect botnet attacks and classifies the traffic as benign or attack. The system is implemented using the BiLSTM algorithm and trained using the IoT-23 data set. The diversity of attack scenarios of the IoT-23 data set demonstrates the proposed algorithm's competence in detecting botnet types in a heterogeneous environment.\u0000","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45705869","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 : 2022-01-14DOI: 10.1108/ijpcc-06-2021-0144
Sandeep Kumar Reddy Thota, C. Mala, G. Krishnan
Purpose A wireless body area network (WBAN) is a collection of sensing devices attached to a person’s body that is typically used during health care to track their physical state. This paper aims to study the security challenges and various attacks that occurred while transferring a person’s sensitive medical diagnosis information in WBAN. Design/methodology/approach This technology has significantly gained prominence in the medical field. These wearable sensors are transferring information to doctors, and there are numerous possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. As a result, mutual authentication and session key negotiations are critical security challenges for wearable sensing devices in WBAN. This work proposes an improved mutual authentication and key agreement protocol for wearable sensing devices in WBAN. The existing related schemes require more computational and storage requirements, but the proposed method provides a flexible solution with less complexity. Findings As sensor devices are resource-constrained, proposed approach only makes use of cryptographic hash-functions and bit-wise XOR operations, hence it is lightweight and flexible. The protocol’s security is validated using the AVISPA tool, and it will withstand various security attacks. The proposed protocol’s simulation and performance analysis are compared to current relevant schemes and show that it produces efficient outcomes. Originality/value This technology has significantly gained prominence in the medical sector. These sensing devises transmit information to doctors, and there are possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. Hence, this paper proposes a lightweight and flexible protocol for mutual authentication and key agreement for wearable sensing devices in WBAN only makes use of cryptographic hash-functions and bit-wise XOR operations. The proposed protocol is simulated using AVISPA tool and its performance is better compared to the existing methods. This paper proposes a novel improved mutual authentication and key-agreement protocol for wearable sensing devices in WBAN.
{"title":"A lightweight and flexible mutual authentication and key agreement protocol for wearable sensing devices in WBAN","authors":"Sandeep Kumar Reddy Thota, C. Mala, G. Krishnan","doi":"10.1108/ijpcc-06-2021-0144","DOIUrl":"https://doi.org/10.1108/ijpcc-06-2021-0144","url":null,"abstract":"\u0000Purpose\u0000A wireless body area network (WBAN) is a collection of sensing devices attached to a person’s body that is typically used during health care to track their physical state. This paper aims to study the security challenges and various attacks that occurred while transferring a person’s sensitive medical diagnosis information in WBAN.\u0000\u0000\u0000Design/methodology/approach\u0000This technology has significantly gained prominence in the medical field. These wearable sensors are transferring information to doctors, and there are numerous possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. As a result, mutual authentication and session key negotiations are critical security challenges for wearable sensing devices in WBAN. This work proposes an improved mutual authentication and key agreement protocol for wearable sensing devices in WBAN. The existing related schemes require more computational and storage requirements, but the proposed method provides a flexible solution with less complexity.\u0000\u0000\u0000Findings\u0000As sensor devices are resource-constrained, proposed approach only makes use of cryptographic hash-functions and bit-wise XOR operations, hence it is lightweight and flexible. The protocol’s security is validated using the AVISPA tool, and it will withstand various security attacks. The proposed protocol’s simulation and performance analysis are compared to current relevant schemes and show that it produces efficient outcomes.\u0000\u0000\u0000Originality/value\u0000This technology has significantly gained prominence in the medical sector. These sensing devises transmit information to doctors, and there are possibilities for an intruder to pose as a doctor and obtain information about the patient’s vital information. Hence, this paper proposes a lightweight and flexible protocol for mutual authentication and key agreement for wearable sensing devices in WBAN only makes use of cryptographic hash-functions and bit-wise XOR operations. The proposed protocol is simulated using AVISPA tool and its performance is better compared to the existing methods. This paper proposes a novel improved mutual authentication and key-agreement protocol for wearable sensing devices in WBAN.\u0000","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48092924","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 : 2022-01-01DOI: 10.1108/IJPCC-07-2021-0154
B. Jagan, S. Rao, M. Lakshmi
{"title":"Underwater target tracking in three-dimensional environment using intelligent sensor technique","authors":"B. Jagan, S. Rao, M. Lakshmi","doi":"10.1108/IJPCC-07-2021-0154","DOIUrl":"https://doi.org/10.1108/IJPCC-07-2021-0154","url":null,"abstract":"","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62801883","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 : 2022-01-01DOI: 10.1108/IJPCC-11-2021-0280
B. SantoshKumar, E. KrishnaKumar
{"title":"A novel utilization-aware and power-delay-aware intelligent DMA controller for video streaming used in AI applications","authors":"B. SantoshKumar, E. KrishnaKumar","doi":"10.1108/IJPCC-11-2021-0280","DOIUrl":"https://doi.org/10.1108/IJPCC-11-2021-0280","url":null,"abstract":"","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61997843","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 : 2022-01-01DOI: 10.1108/IJPCC-09-2021-0217
K. ShobhaY., H. Rangaraju
{"title":"Intrinsic interference suppressed FBMC QAM for MU-MIMO systems in computing and communications","authors":"K. ShobhaY., H. Rangaraju","doi":"10.1108/IJPCC-09-2021-0217","DOIUrl":"https://doi.org/10.1108/IJPCC-09-2021-0217","url":null,"abstract":"","PeriodicalId":43952,"journal":{"name":"International Journal of Pervasive Computing and Communications","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62801737","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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