# Intelligence-based Network Security System to Predict the Possible Threats in Healthcare Data

Authors: K. Vijayakumar; Sangheethaa Sukumaran; D. Murali; R.Venkateswara Reddy; Patteti Krishna; C. Bazil Wilfred; Karthikeyan Kaliyaperumal
Journal: Security and Communication Networks
 (2022)

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## Abstract

The world is filled with exciting technologies and ideas; scientists build machines to avoid human intervention in completing work. It is highly challenging to complete the task without the artificial intelligence (AI) technology intervention. With technological development, specific processes or consultations are performed with doctors available worldwide. In this scenario, it could be noticed that health care is one of the world’s expected domains that require the most incredible attention in data security while performing data transfer. Nodes in the network are considered based on the weakest link to overcome the cyber attacker’s issues. Besides building the software for data storage, a better mechanism has to be incorporated to provide security to the stored data. This process is a delicate task for every network engineer. This paper will explain such concepts related to health prediction and health care by building the most robust network security systems. The proposed optimized neural network representation is differentiated with the available data conclusive process. From the outcomes, it is observed that the suggested representation achieves an accuracy of 98.89%, which is 4.76% higher than the existing model.


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## Body

## 1. Introduction

With technological development, the threats and vulnerabilities are growing in a proportional direction each day. Hence, if the threats are identified and trained with the neural network by making variations about the threats and vulnerabilities, the intelligent system can reduce the detection duration later. In that case, artificial intelligence (AI) and wireless sensor networking (WSN) technologies help detect threats without human intervention. More than half the companies create secure software to run over their marketing circumstances by utilizing such availability. Suppose the error occurs in the connections between cyber-physical systems (CPS) and the Internet of Things (IoT); in that case, it can access the requirement for increased healthcare data with the aid of AI as one of the background applications or software for managing the needs. There are thousands of algorithms currently available behind both the AI and WSN; each would differ by the health care systems the data scientist used to find and merge the major one. At the same time, the continuous development of sensory objects and nanosensors is noted by every scientist and country’s government [1]. This would increase confidence and availability to invent enough new things. In previous years, the Internet of Things was developed by creating an imaginable smart world and explaining the actual presence of the future world. At the same time, they are getting into the AI-driven models belonging to the Internet of Things (IoT) platform connected to machine learning by providing several data sets. Here storing and making use of those data is one of the essential processes in our concept.Every concept based on artificial intelligence might have some relations with neither machine learning (ML) nor deep learning (DL). However, such concepts are utilized once the proper algorithm is fetched into the machines [2]. Artificial intelligence is being functioned in all such cases. Typically, if a drug is getting into the market without getting the actual certifications, it is considered illegal while selling it out. While in the previous years, a critical program conducted from the networking technology research development has been discussed the difference between the statistical reports of health data, management, and further activities. Typically, every node behind the IoT systems works as one of the most innovative techniques, even it could be performed without the help of human interaction.Before ancient days, people who lived from 1960 to 1970 might have heard words similar to artificial intelligence. Under the same prospect, some old technologies have been introduced, such as expertized systems, autonomous managing systems, augmented reality (AR), and neural network connections. The first term of expert systems is mostly being worked to solve those issues through human thinking and without the man’s help. While before getting the right solution from the plans, the machine would come across actions and algorithms like matching the facts and analyzing the reports according to the previous occurrence. While designing these algorithms’ purpose of each technique is compared with the particular domain and started to separate sections using the domain. Autonomous and other such areas would face the machines and robotics side, but the expert systems are adapted to medical science and different health care addresses.Artificial intelligence-based countermeasure results and applications are always on the path to reaching an essential thing for developing security in the resources. To navigate the gap and mitigate the issues faced, a deep study of the layers available in the network system is mandatory. Only then could it be possible to reach a robust security system. The most common thing to be managed in this system is to analyze the health care threats in reality and to find the right solution for them. All these processes should be encountered with the help of a system under the technical aspect known as artificial intelligence. If an IoT system is being proposed to manage the system, in any case, even by having a try, the crime agents cannot be able to access the confidential data that are gathered from the IoT network. Expecting automation in all areas can stimulate a better experience in the digital world, and this application might boost the requirements expected by patients or the guiding person. The proposed model always promotes confidentiality, integrity, and data protection under IoT. It can manage some of the cybercrime attacks like spoofing, hardware default threats, cloning techniques, and distributed denial service.


## 2. Literature Review

In [3], the authors have introduced a new blockchain and distributed the ledger-based security system to improve EHR security. The model is designed for biomedical security systems and compared with the existing system with an increased performance of 8.077%. The authors of the [2] have tried to solve the problem of re-counseling security and privacy issues by implementing decision support mechanisms and specific planning strategies. They have implemented them for prominent health care data. AI is still one of the fastest-growing technologies that contain n number of applications. It is also helpful for either cyber security or health management. Sometimes, robust systems can find solutions that are similar to cyber threats. Data collection [4] is one of the significant expectations for AI and ML, which is ensured under the countermeasure of IoT threats. This kind also manages the system’s performance without any central control managing. Technological growth under [5] does not mean only entertainment development, improving modern life, and maintaining the commerce section. It also includes health care. Here the requirement is about private profiling. We will see the conversion of image sensing to a disease predicting system and in the developing drug systems. The collection of data [6] for future prediction through AI also means its security. Implementing private data to identify diseases in a human body also relates to the data security process. The actual purpose of [7] AL health care systems is to create a shortcut for the doctors to identify the name of the disease. In such cases, the availability should be worldwide, which helps to prevent privacy violations and breaches under Electronic Health Records (EHR). Some protocols for transport layer security are being designed for the data transaction, and in [8], its main prospect is to avoid data loss.Once the data transactions are made through low-power wireless networking systems using datagram transport layer security (DTLS), it acts under different protocols and adds through smart gateway authentication. Recent defects have been found [9] under the robustness proposed method. Usually, the images filled with watermarks have simulated impermeable attacks and cause repeated attacks. The authors of [10] have created a deep connection between the cloud and AI. There could be some centralization of data that are asked to face privacy challenges after researching for such malfunctions only the subsets concerned about the state-of-art. E-health management is one of the most powerful cloud computing technologies under consideration in creating abundant resources at such a low cost. IoT creates a separate connection with all the other devices and also the cloud access one common difference is cost management.Moreover, every system managing a person expects the wireless network connection [11] only when the system is updated according to traditional methods such as blockchain, data securing, and ML. When creating a separate database for identifications expected through an image, the computations should be maintained in the right way by making the demonstration outsourcing proper the resource connected with the IoT devices are constrained [12]. The growth of blockchain advancements should be considered majorly because the improvements are being truthful and fast. If there could be some reason for its development, then security maintenance of those hashing functions under cryptographic control would be essential [13, 14]. Finally, EHR electronic health records can be valuable human creations. It can be accessed using a particular domain or malicious codes [15]. Threats can be noted and significantly impacted by their presence; threat management is most necessary for every business website and application. For example, all the company data are shared through the website to attract customers. In such a case, the data transmitted over the websites should not be leaked or misused; these are considered threats. To manage such threats, enhancing security is essential in every resource [16]. Threats cannot be noted only from the software side; apart from the software issues, some hardware problems create a significant cause in the system; healthcare is an essential resource where the patients should not get affected by power issues. In such cases, through this paper, the author has deployed a few exciting facts about threat management and its security [17]. In 2016, Ransomware was considered one of the significant threats by a different set of organizations. The companies manage online applications, so if this kind of cyber threat affects a business deal, the impact of loss would be only about the currency. Still, if the exact cause happens in health care management, it involves a person’s health. In such a case, it is more important to take care of the threats while bringing the concepts like artificial intelligence and deep learning [18, 19]. Every expert notes health care, and they do always work to have some changes in it. In this paper, the author has examined the entire field of health and managing the technology to prove the proposed system with accurate results used to work with fog computing, cloud computing, and a few technical aspects [20]. As default Internet of Things is being referred to be a designing and modeling process once the systems are connected to the Internet network, so the possibilities of converting the operation in a negative side are enough, hospitalization always being as a regular health updating center for every person living in this Earth, in such case, the data that are collected for future purpose should be maintained properly, in [21, 22] the author used to manage a different set of wearable and nonwearable sensors using IoT.


## 3. Proposed System

Healthcare in a hospital region or inside a care home its main target is to safeguard the patients and protect them through personal health. So while checking out the working progress, there are some involvements like telecommunication such as wearable systems, users’ perception while listening to the issues, maintaining the database using the concept of big data, and finally, smartphone access. Figure1 represents the overall model of the proposed system for the intelligent healthcare environment. Data collected bout the patient’s health conditions can be stored in the cloud environment, and hence it can be made available to the users under any required circumstances. The users in this model can be patients, doctors (both native and remote), and technical persons involved in the patient’s treatment. Whenever a new medicine or treatment is introduced to the patient, all the data can be recorded frequently in the cloud environment for easy access. In addition to these specifications, the patient can be monitored with the Internet of Things (IoT) devices. The data will be uploaded automatically to the cloud systems by applying intelligence. The generated data will be more significant. Hence, it can be accessed with the aid of a smartphone by using mobile applications (if needed) or directly from the hospital website through an authentication mechanism (to support security breaches).Figure 1
AI management by means of cloud.In certain critical conditions like travel of either the patient or the doctor, if there is an emergency need for the patient, there might be a mandatory requirement for all the medical history to perform an effective treatment. It will be challenging for the patient to carry a third copy of the medical files on the go. Besides these difficulties, they should preserve the hard copy of their data. The patient’s medical history data can be maintained in the hospital server and provide access privilege on-demand to overcome these difficulties. However, the data have to be stored in a highly restricted zone and with high-security authentication procedures, as the data have a high possibility of being attacked by threats. This attack may result in data loss and improper treatment of the patients. Types of resources that can access the resources and the processes involved are depicted in Figure2. In this research, neural network model is designed to train the system with the existing threats in the healthcare domain and is tested for the occurrences. AI mechanism is about diagnosing and getting accuracy, predicting the actual data collected from the resource. In such a case, we compare data collected from different health sectors and expect thousands of medical records using the collected data. This data collection also results in experiencing an automation technique with medical notifications and creates a strong belief in the health management sector. These requirements are managed with the stored patient history while their treatment progresses.Figure 2
Possible threat identification in healthcare data.The methodologies are initially examined by developing an artificial intelligence-based interactive telemedicine transportation system for healthcare and a data introduction method for diverse situations. Later, the system reimagines the obtained data and stores the reimagined reliable data from the database, allowing for quick data display of the findings in a large concurrency environment. The data transformation technique of a wireless controller is needed to solve the problem of data in various file formats that should be delivered and examined. The technique manages varying information from numerous sources using a centralized data template and data transformation restrictions, resulting in a consistent data support center for such setup and external frameworks. To address issues that may need correlation analysis data, its information system delivers data for each component of such a communications framework based on business requirements, using a defined data structure and strong but also worldwide data storage. It avails for the free flow of highly linked data from many sources. The doctor might communicate with the patient utilizing multimedia technology while obtaining the patient’s historical time health information and the arranged data disseminated during a healthcare discourse.The technique contains a central data template with data transformation limits to manage diverse data from several sources, resulting in a unified data support unit for such systems with external frameworks. To handle issues that may need association analysis data, the company’s information system provides data for each element of such a communications framework that is relevant to business objectives, using a defined data structure with strong but also worldwide data storage. It enables the free flow of highly related data from a variety of sources. The doctor might employ multimedia technology to connect with the patient while obtaining the patient’s past temporal health information and the arranged data aired during a healthcare conversation, this scenario is depicted in equation (1).(1)Sm=∑xnSF×Dn+∑mnS×DnKm×g+∑tnS×DnDt.In the equation (1), S represents the requisite for a residence health service; Dn signifies the cost of consulting; Dt denotes the operational sociocultural security services; and Km is the percentage of total rate in that the free movement of g is closely linked information from diverse sources.The healthcare involvement to preserving resources is related to the fact that costs of preserving cash reserves has now increased in connection to the organization’s measures. Its very own proportion appears to be a linked information that is obtained from the very next equation (2)'s multiplication process.(2)SE=∑xnSF×Dn+∑m=1gF2+Qm×g×Km×∑snS×DnDt.In the equation (3), SE stands for maximum number of simultaneous users in health coverage; Fstands for the amplitude of a delivery component, Qmstands for the successful execution of the safety factor; and the letter h stands for a different type of consulting firm.(3)SE=∑m=1gF2+Qm×∑xn1−F×Dn×Sg×h+K×1−F,∑xn1−F×Dn×St is the optimal magnitude of a small transaction for maximizing market capitalization. The average tax rate of statistical item production is denoted by the letter K.(4)SE=∑nmSF×Dn+∑mvF2+Qm×v×S×∑gmg×Km,(5)SE=∑nmSF×Dn+g×Km∑sn1−F×Dn+Dn∗×Sv×h+K∗+K$×1−F.The item level but also revenue expenses of working to develop product availability are represented in equations (4) and (5). During which Dn$ denotes the profitability spacing of establishing speed levels, Dn∗ represents its semitrailer of developing inventory exact distance simultaneous number levels, and K$ signifies the stock levels’ negligible shortest variance to represent the effective maximum range.Through to the scheme, its supervisor can effectively gather information from specialists and service users while also meeting the requirement for straightforward dialogue are given in the equations (6) and (7) are as follows:(6)SE=∑nSSF×Dn$+SF×Dn∗×g×Km∑ymF2+Qm×v×K$,(7)SE=∑nSSF×Dn$+SF×Dn∗×g×Km∑mFF2+Qm×v×K∗.Differences in theT2are significantly affected by Qm distribution residence health system predicated on reliability is given in equation (8). A variety of safety mechanism consulting services that distributors are considered necessary to do is also provided in the equation.(8)Qm=∑nm∑i=1KKi−K2T2×ln∑DFD×F×T×v×2πS×Dnm.The term isT is used to find the transfer utilization standard deviation and Dnm is the cost of not requiring stock levels resources is calculated according to the equation (9).(9)T=∑i=1mSi×∑i=1KKi−K2+∑DFD×F×T×v×2π+S×Dnm,Si appears to define the statistically estimated probability of occurring of a specific equation (10) predicament.(10)T=∑i=1mD=∑i=1mSi×Ki−K2+∑yFF2+Qy×v×K$+S×Dnm.To accomplish centralized regulation of differing data from many origins, likely to result in such consolidated evidence supports center for these kinds of systems but also external implementation methods that adhere here to equation (11).(11)L2=∑i=1mSiK1i−K1×K2i−K2∑i=1TT1×T2+∑i=1mSi×Ki−K2,L2 is the value of correlation between both the minimum but also maximum and T1 indicates that multiple scenarios are supported in the equation (11). T2 signifies the wireless controller’s information modification process, which is essential to identify the problem of provided in multiple file types, but also K1 signifies the distributor’s random errors? K2 represents the standard error for such second supplier. T1 denotes the possibility of generating prospective rates.Tn denotes the possibility of employing a centralized data template but also process design constraints to attain centralized regulation of divergent data from many origins using the equation (12).(12)TS=∑nmTn2+Tm2×Tn×Qm×Lnm+∑i=1mSi×Ki−K2,TS is the overall standard deviation, Tm2,Tn2 appears to have been the standard deviation of its first solution, Qm is the standard deviation of the standard solution, and nm are also the regression coefficients between multidimensional data. The evaluations that could have been carried out in any case for the development of WSN of AI techniques and also the innovation of the management system would have been described.(13)TS=∑nmL1<L2<,…,<Ln+∑i=1mSi×Ki−K2.In equation (13), where ki probably comes after Si it appears to have come within a week of Li, and so on. A machine learning set of frequency instructing of attribute calculated L1, which will also be indicated as an equation (14), and is prespecified as S.(14)S=∑nmSi+∑i=1mSiLi×Ki−K2i=1,2,3,…,n.The accumulated scheme performance metric could be represented like a complex performance metric with in form of such a set of equations (15) and is represented below.(15)Q=∑i=1nS1L1+S2L2+⋯+SnLn=∑i=1nSiLi×∑i=1mSi×Ki−K2.A policy’s efficiency is easily a componentQ with n performance analysis that appears to be iterative to its evaluation measures Li. The tele-consultation design is very easy rather than friendly, making it much more efficient for consumers but also providing them with such a good experience, but it must also meet their requirements before it is used. The confidentiality and anonymity of the tele-consultation service have been enhanced, as has the identification of access restrictions. The focus of a process of customers from various protections is also distinct, which contributes to the system’s confidentiality.


## 4. Experimental Result

The healthcare system aims to satisfy the actual needs of patients without requiring them to leave their initial living environment and relying on web service and supplying a complete range of services to healthcare in different perspectives through the incorporation of social resources. As seen in Figure3, the facilities are given in day healthcare institutions, network security and service station, or clear the maximum user supportive services. It allows the interface of data in the network security system to predict the possible healthcare benefit from facilities close to their homes. Its performance analysis for the statistical test analysis for the healthcare construction response of the social network security system is provided in Figure 3 retrieved by the equation (3), SE is the highest amount of concurrent users in health coverage; F denotes the amplitude of such an application tier; Qm denotes the successful implementation of the safety system; and the letter h denotes a specific variety of consulting data.Figure 3
Test statistics healthcare construction of the social security system performance analysis.Table1 provides an analysis of the services (D1–D10) provided to construct a healthcare system with statistical tests. The statistical analysis is performed based on the constructed healthcare and the predictive social network security system with accuracy. The performance is also fluctuating with the varying services, depending on the services.Table 1
Test statistics healthcare construction of the social security system result analysis.
DataHealthcare construction of statistics (%)Predictive social network security system (%)Overall accuracy (%)D167.6276.8792.43D273.5469.4694.24D365.2483.5389.52D489.2176.4890.15D591.2486.4995.64D688.2290.4597.43D779.1391.3193.52D876.6489.4796.14D984.6381.3589.35D1090.2189.3295.32Users can also provide increased community healthcare and enhance the service experience. Because all models in service design consist of the all-around needs of the users, the requirements and perspectives of healthcare users and relevant entities should be considered in innovative overage services. Telecommunications companies must recognize the diversity but also hierarchy of needs, as well as deeply understand the real healthcare needs and pay attention to real-world situations of service usage. From the standpoint of customer experience, it is essential to meet usability, ease of use, and efficiency at a cognitive level and elicit the psychology which users want to use and are ready to involve in the provider while having received it. The personal service needs in healthcare construction of a social network security system (Figure4) using the AI techniques. It is used to analyze for the tele-consultation of the data security in the number of healthcare construction users to testing and training accuracy in the social network services. In Figure 4, based on representation in which Dn$ represents the profit distance of creating speed extents, Dn∗ represents its semitrailer of growing inventory precise distance simultaneous quantity levels, and K$ represents the product levels’ negligible shortest variance.Figure 4
Personal service needs in healthcare construction of a social network security system using AI techniques.It is not only critical to meet accessibility, simplicity of the use, but instead efficiency just at cognitive level, but also to generate the psychology that consumers are using and are willing to involve there in provider after receiving it. Using AI techniques, the personalized service needs in healthcare development of a social network system (Table2). It was used to analyze data security for teleconsultation in a large number of healthcare building projects users and to test but also train accuracy in Internet platforms. The teleconsultation analysis for the testing and training prediction for the mean and standard deviation gives overall accuracy in 1–10 data (95.10%). Based on that, the data security in the system for the training and testing prediction for the mean and standard deviation gives overall accuracy (94.53%). The concurrent user analyze it for the performance in training and testing mean, and standard deviation gives the overall accuracy (96.54%).Table 2
AI-assisted personal service use of the healthcare building of the social security system in WSN.
Individual healthcare service requirementsMeanStandardTest data (1 to 10)Training/TestingAccuracyTele-consultation66.7878.4392.7894.7695.10Data security in the system88.3489.9887.3493.5694.53Concurrent user count77.9888.3479.8995.4396.54Patient concentration increases in healthcare sectors to continue utilizing and enhancing the service quality and specialization in benign competitive pressure received from the hospitals. At the moment, the structure of sophisticated products within the provider of intelligent healthcare is too single and also the information tends to be homogenized, lacking imaginative points. However, as the quality improves, a single product structure cannot meet the diverse needs of users. In this instance, services with considerable experience are sometimes a game-changer in the line of work of aging in place. Never use service strategy processes and technologies logically to investigate the needs of the residents in the smart overage service (refer Figure5). Variations in the equation (8) T2 are significantly influenced by the Qm distribution residential healthcare system based on dependability. Its variety of security mechanism consultancy services that producers are thought to be required to provide. A healthcare system evaluation was based on strongly correlated information from various sources is realized to interface the healthcare data transaction from the social network services to analyze for the transaction speed.Figure 5
A healthcare system evaluation was based on strongly correlated information from various sources is realized.The healthcare develops progressive scan data behaviors are influenced based on the unique qualities of data, defining a unified data format and adaptation process to standardize data formats accessing the telemedicine framework. The healthcare analysis for the overall accuracy interface (86.89%) system to predicate the data network security (89.12%) on the facet of a test tool (84.56%) to analyze for the speed (91.75%) of transaction for the massive correlated processing from the various sources (refer Table3).Table 3
Result analysis for healthcare system predicated on the fact of a highly correlated information from various sources.
Data that is highly correlatedTraining (%)Testing (%)Overall accuracy (%)Interface92.4384.6286.88Data network security85.3292.4389.1Test tools87.6683.8784.53Speed of transaction processing91.6892.8491.75The tele-consultation data set includes a vast quantity of information but data, which is the primary source of information in hospitals of various sizes and different areas. The amount of information is reasonably large and includes a wide range of data. In developing this system, the data are also stored on the server and the customer.TS denotes the overarching standard error, Tm2,Tn2 appears to be the standard deviation of its first solution, Qm is the standard error of the standard stock solution, but also nm are always the significant model terms between multidimensional data. The evaluations that could have been conducted in any case for the development of WSN of AI techniques as well as the technology of the management software would have been discussed in Figure 6. The performance analysis (refer Figure 6) for testing and training in tele-consultation of a healthcare system during the development of the social protection for minimum and maximum possible prediction to analysis for the accuracy.Figure 6
Performance analysis for testing and training in tele-consultation of a healthcare system during the development of the social protection.Considering the high correlation of required data by telemedicine services, it proposes an implementation method for providing strongly correlated data with each component of a telemedicine platform based on business prerequisites. Thus, the pathway transfer of highly correlated information from various sources is recognized. The result (shown in Table4) analysis for the average transaction in minimum (350) and maximum (478) response and actual load concurrent number of minimum (680) and maximum (540) the overall accuracy for the existing method (92.56%) in the minimum distance and (89.43%) the maximum distance for the data security.Table 4
A result comparison analysis for the existing system healthcare system.
ParameterAverage transaction responseActual load concurrent numberMaximum number of concurrent usersOverall accuracyMinimum distance35067368092.56Maximum distance47853054089.43Mean53035038091.34Data security42042345093.45To identify five most critical categories of threats in healthcare industry to analysis that are presented in significant threat categories with such an accurate summary with each threat. Essentially, the threats were classified so according standard requirements and presented the five greatest critical threats based on such a comparative focus of past works and publications. According to the study, the category of power outages is the most serious threat to the healthcare. The most common threat in this category is a server outage due to a power outage, which was mentioned by 89.8% of respondents for the training (Table5). Furthermore, 28% of respondents reported service provider interruption Internet service provider, accompanied not only by electrical problem (19.5%) but also server plane failure (15.7%).Table 5
Identify five most critical categories threats in healthcare industry to analysis and find the security using optimization neural network algorithm.
Possible threat identify in healthcare industryFind the security in healthcare using optimization neural network algorithm (%)Training (%)Overall accuracy (%)Cyber risk in power failure/loss928994Healthcare infections acts of human error or failure958595Telemedicine-based hardware and software failures or errors979197Violent incidents in hospitals communications infiltration969396Alarm fatigue989595Acts of life form errors or mistakes are also a frequent occurrence in healthcare. The entry of incorrect data through staff, which had been recognized by 98%, is the highest average threat in the healthcare industry. This poses a serious risk to data confidentiality, integrity, and availability. This type of incident occurs as a result of a low level of awareness but also good practice among the staff. Moreover, 96% of respondents suggested that inadvertent deletion or data modification by staff was also a factor in this category. Furthermore, technical obsolescence is seen as a major element for it though. Technological obsolescence relates to obsolete infrastructure also including hardware and software applications, including network equipment that can lead to unreliable but also untrustworthy systems. Within the technical obsolescence danger category (refer Table6), about 97% of respondents identified obsolete hardware as a primary threat. Following that, 98% cited outdated application software as a major factor inside this category, followed with outdated software system (97%) and old networking devices alarm fatigue (98%).Table 6
Identify the five most critical categories threats in healthcare industry to find the security in testing using optimization neural network algorithm.
Possible threat identify in healthcare industryFind the security in healthcare using optimization neural network algorithm (%)Testing (%)Overall accuracy (%)Cyber risk in power failure/loss939096Healthcare infections acts of human error or failure968897Telemedicine-based hardware and software failures or errors989498Violent incidents in hospitals communications infiltration979697Alarm fatigue999798The comparison result analysis for the healthcare system using the optimization neural network algorithm construction of the social network security (92.56%) and the average transaction speed to study for (80.53%) and then the training/testing (95.68%) with the overall accuracy (98.89%). The existing methods for the same process to getting the building of the social safety (89.34%), the average arrangement speed (72.45%), the training/testing (91.34%), and the analysis for the overall accuracy (94.12%). This performance analysis is given in Table7.Table 7
Comparison result with existing method.
AlgorithmHealthcare system construction of the social securityAverage transaction speedTesting/training (%)Accuracy (%)Optimization neural network algorithm92.5680.5395.6898.89Existing method(1) Data summarization method89.3472.4591.3494.12(2) Blockchain-based intelligent monitored security method88.1270.1290.2390.57(3) Cloud-based WAFs method87.3472.3489.3291.34(4) Disease prediction and diagnosis method89.1271.5490.9892.78


## 5. Conclusion

The growth of intelligent systems is gaining attention in the healthcare domain to a greater extent. The attention is extended to monitor the patient’s health conditions and provide security for the collected data. In this research, continuous monitoring of patients’ treatment is assumed to be performed with IoT devices and updated in the cloud environment for easy access. This research focuses on the intelligent neural system to analyze the existing threats and predict the results during the data transfer. The system is then differentiated with the data summarization representation. From the outcomes, it can be observed that the suggested approach has achieved an accuracy of 98.89% in threat identification. This value is 4.77% higher than the existing model. As a future enhancement, the system can be designed to perform complete atomization of the processes that include monitoring, decision making, transfer of data to the users, detection of threats, and also necessary measures after threat identification.



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*Source: 6716370-2022-05-26.xml*