ABSTRACT In this paper, the improved chameleon swarm algorithm (ICSA) enhances the exploration–exploitation balance while optimizing feature subset selection. The integration of Lévy flight‐based exploration refines ICSA's search strategy, complemented by rotation‐type refinement and adaptive parameter‐setting mechanisms. These modifications ensure that exploration aligns effectively with the feature selection process, leading to a more adaptive and efficient approach. To evaluate ICSA's effectiveness, it is tested on the NSL‐KDD benchmark, a well‐established dataset in intrusion detection systems. Performance is assessed based on key metrics, including accuracy, detection rate, false alarm rate, execution time, and the number of selected features. Comparative analysis against six advanced classifiers demonstrates that ICSA achieves superior results with minimal computational overhead. The algorithm attains the highest accuracy (97.91%) and detection rate (98.75%), the fastest execution time, and the lowest false alarm rate (0.0021), eliminating the need for excessive feature selection. These results confirm that modifying feature selection mechanisms within ICSA significantly enhances computational efficiency and detection performance, as validated through rigorous experimental testing at the classifier level.

Fog’s inherent decentralized nature and ability to process data in transit, i.e., the ability to draw conclusions in real-time, are quite suitable for scenarios where an enormous number of decentralized devices need to communicate and provide live analysis of data and storage tasks. Fog computing’s ability to work close to the end user and non-reliance on centralized architecture provides the dependability that time-critical smart healthcare systems need. Because of the critical nature of healthcare data, better security and privacy solutions for fog computing are required, with trust being of the utmost importance. Context-dependent trust solution for fogs is still an open research area, so the aim of this research is to propose a context-based adaptive trust solution for smart healthcare environments using a Bayesian approach and similarity measures. The proposed trust model has been simulated in Contiki, Cooja, and a Java-based application has been developed to analyze our results. Adaptive weights assigned to direct and indirect trust using entropy values ensure the minimization of trust bias as opposed to static weighting. Context-based similarity calculations filter out recommender nodes with malicious intent using server, social contact, and service similarity. This model is efficient and has a low-trust computation overhead because it has a linear complexity of$O(n)$.

Different applications in modern networks produce various types of traffic with diverse service requirements. In the network traffic classification, "unknown applications" are regarded as a difficult problem that remains unsolved, especially in the healthcare sector. Traffic classification helps in classifying and aggregate traffic flows into categories with the same traffic patterns. Identification and classification of traffic are critical for network management efficiency, which includes Quality of Service (QoS), detection of intrusions, and lawful interception. Only the network traffic classification technology based on payloads is fitting because most of the applications are IP based, whether is attached to a specific port number or is dynamic or is temporary. Payload-based classifiers consist of finding the features in the payload of data packets to differentiate between the application protocols. In this work, we propose a model using machine learning (ML) for an accurate and efficient traffic classification. ML allows for an automatic response to various applications by classifying traffic without a network operator interference. Experimental results demonstrate that ML-based traffic classification methods are effective and obtained high accuracy and a low data loss rate in front of other available models.

In this paper, a remote monitoring system enabled with the smart internet of things (IoT) station for ambient assisted living (AAL) and smart environments is introduced. IoT station that is based on Waspmote platform to work as an intelligent device for capturing the data and transfers through 3rd Generation (3G) cellular module to the cloud by avoiding redundancy. The complete architecture of the developed ubiquitous monitoring system for AAL is elaborated with the process flow. The video camera sensor board with a presence sensor is connected to the Waspmote to take a snapshot or a video clip when there is any movement in the surrounding. The IoT station with the sensor board have a capability to detect and take picture even in a dark environment. The Waspmote generate message as soon as the motion sensor is triggered and send the image to the storage server using file transfer protocol (FTP) or the file transfer protocol secure (FTPs) over cellular communication. The recent related work is reviewed and discussed to show the advantages of the proposed design. Furthermore, the real testbed experiment presents the efficiency of the cellular IoT based monitoring system.

Summary Internet of things environment is adopted widely in different industries and business organizations with varying capacity. It provides a favorable environment to outsource the crowdsourced data in the cloud to minimize the cost of computation, which is called crowdsourcing. Crowdsourcing is a technique where individuals or organizations obtain goods and services. A professional or industry outsource the crowdsourced data in the cloud, where confidentiality and authenticity of data become essential. Signcryption is the cryptographic technique that serves both the authenticity and the privacy of transmitted messages. This technique ensures secure authentic data transmission and storage. Therefore, this paper proposes an identity‐based signcryption scheme. In the proposed PSSCC framework, the user does pairing free computation during signcryption, which makes efficient calculation on user‐side. Moreover, PSSCC framework is proved secure under modified bilinear Diffie‐Hellman inversion and modified bilinear strong Diffie‐Hellman problems. The performance analysis of PSSCC with related schemes indicates that the proposed system supports efficient communication along with less computation cost.

The security and privacy of healthcare enterprises (HEs) are crucial because they maintain sensitive information. Because of the unique functional requirement of omni-inclusiveness, HEs are expected to monitor patients, allowing for connectivity with vehicular ad hoc networks (VANETs). In the absence of literature on security provisioning frameworks that connect VANETs and HEs, this paper presents a smart zero-knowledge proof and statistical fingerprinting-based trusted secure communication framework for a fog computing environment. A zero-knowledge proof is used for vehicle authentication, and statistical fingerprinting is employed to secure communication between VANETs and HEs. Authenticity verification of the operations is performed at the on-board unit (OBU) fitted in the vehicle based on the service executions at the resident hardware platform. The processor clock cycles are acquired from the service executions in a complete sandboxed environment. The calculated cycles assist in developing the blueprint signature for the particular OBU of the vehicle. Hence, the fingerprint signature helps build trust and plays a key role in authenticating the vehicle's horizontal movement to everything or to different sections of the HEs. In an environment enabled for fog computing, our novel model can provide efficient remote monitoring.

Nowadays, access to healthcare services in rural or remote regions remains a major issue in both developing and developed countries. The advent of mobile health (m-Health) services is becoming a major improvement for patients. The main objective of this paper is the development and Quality of Experience (QoE) evaluation of a mHealth solution for healthcare professionals in remote areas. The system architecture is based on a Service Oriented Architecture (SOA). Android OS was chosen for developing the application, mainly, due to its open source APIs and the vast diversity of covered mobile devices. The system was evaluated and demonstrated in a real pilot in cooperation with a healthcare institution involving 42 patients and 4 healthcare professionals. A total of 294 patients evaluated the solution. Hardware issues, such as network disconnection and energy issues were reported in 4% of all the cases. This system reduced significantly care costs lessen the need for physical contact between patient and physician.

The incidence of hypertension associated with pregnancy contributes significantly to increase maternal and fetal deaths during pregnancy and childbirth. Due to its high incidence rate and several complications, the study of this disorder has been subject of numerous investigations in an attempt to determine its prevention and improve the treatment conduction. In this context, this paper uses a data mining (DM) technique, named random forest (RF), applied to health care to early identification of these disorders. It also presents the modeling, performance assessment, and comparison with other DM methods to evaluate the performance of the proposed model. Results showed that the RF classifier had a regular performance, presenting the best values for true positive Rate (TP Rate) and recall in the prediction of preeclampsia superimposed on chronic hypertension compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other tree-based methods need to be evaluated, as well as other DM techniques. Discovering reliable information of pregnant women suffering from the hypertensive disease is an important path to reduce the high rate of deaths, mainly, in developing countries where 99% of these deaths occur.

The current century has brought an unimaginable growth in information and communications technology (ICT) and needs of enormous computing. The advancements in computer hardware and software particularly helped fuel the requirements of human beings, and revolutionized the smart products as an outcome. The advent of wearable devices from their development till successful materialisation has only taken less than a quarter of a century. The huge benefits of these smart wearable technologies cannot be fully enjoyed until and unless the reliability of a complete system is ensured. The reliability can be increased by the consistent advancements in hardware and software in parallel. User expectations actually are the challenges that keep the advancements alive while improving at an unmatchable pace. The future of wearable and other smart devices depends on whether they can provide a timely solution that is reliable, richer in resources, smaller in size, and cheaper in price. This paper addresses the threats and opportunities in the development and the acceptance of immersive and wearable technologies. The hardware and software challenges for the purpose of development are discussed to demonstrate the bottlenecks of the current technologies and the limitations that impose those bottlenecks. For the purpose of adoption, social and commercial challenges related to innovation and acceptability are discussed. The paper proposes guidelines that are expected to be applicable in several considerable applications of wearable technologies, for example, social networks, healthcare, and banking.

Search engines and social networks are two entirely different data sources that can provide valuable information about Influenza. While search engine hosts can deliver popular queries (or terms) used for searching the Influenza related information, the social networks contain useful links of information sources that people have found valuable. The authors hypothesize that such data sources can provide vital first-hand information. In this article, they have proposed a methodology for detecting the information sources from social networks, particularly Twitter. The data filtering and source finding tasks are posed as classification tasks. Search engine queries are used for extracting related dataset. Results have shown that propose approach can be beneficial for extracting useful information regarding side effects, medications and to track geographical location of epidemics affected area.

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes. Using these IPI-based methods to generate a 128-bit BS in real time normally takes around half a minute. In order to improve the time efficiency of such methods, this paper presents an ECG multiple fiducial-points based binary sequence generation (MFBSG) algorithm. The technique of discrete wavelet transforms is employed to detect arrival time of these fiducial points, such as P, Q, R, S, and T peaks. Time intervals between them, including RR, RQ, RS, RP, and RT intervals, are then calculated based on this arrival time, and are used as ECG features to generate random BSes with low latency. According to our analysis on real ECG data, these ECG feature values exhibit the property of randomness and, thus, can be utilized to generate random BSes. Compared with the schemes that solely rely on IPIs to generate BSes, this MFBSG algorithm uses five feature values from one heart beat cycle, and can be up to five times faster than the solely IPI-based methods. So, it achieves a design goal of low latency. According to our analysis, the complexity of the algorithm is comparable to that of fast Fourier transforms. These randomly generated ECG BSes can be used as security keys for encryption or authentication in a WBAN system.

Computer security is something that brings to mind the greatest developers and companies who wish to protect their data. Major steps forward are being taken via advances made in the security of technology. The main purpose of this paper is to provide a view of different mechanisms and algorithms used to ensure big data security and to theoretically put forward an improvement in the health-based environment using a proposed model as reference. A search was conducted for information from scientific databases as Google Scholar, IEEE Xplore, Science Direct, Web of Science, and Scopus to find information related to security in big data. The search criteria used were “big data”, “health”, “cloud”, and “security”, with dates being confined to the period from 2008 to the present time. After analyzing the different solutions, two security alternatives are proposed combining different techniques analyzed in the state of the art, with a view to providing existing information on the big data over cloud with maximum security in different hospitals located in the province of Valladolid, Spain. New mechanisms and algorithms help to create a more secure environment, although it is necessary to continue developing new and better ones to make things increasingly difficult for cybercriminals.

Hypertensive disorders are the leading cause of deaths during pregnancy. Risk pregnancy accompaniment is essential to reduce these complications. Decision support systems (DSS) are important tools to patients' accompaniment. These systems provide relevant information to health experts about clinical condition of the patient anywhere and anytime. In this paper, a model that uses the Naive Bayesian classifier is introduced and its performance is evaluated in comparison with the Data Mining (DM) classifier named J48 Decision Tree. This study includes the modeling, performance evaluation, and comparison between models that could be used to assess pregnancy complications. Evaluation analysis of the results is performed through the use of Confusion Matrix indicators. The founded results show that J48 decision tree classifier performs better for almost all the used indicators, confirming its promising accuracy for identifying hypertensive disorders on pregnancy.

Significant advances on smart decision support systems (DSSs) development have influenced important results on pregnancy care. Nevertheless, even considering the efforts to reduce the number of women deaths due to problems related to pregnancy, this decrease presented less impact than other areas of human development. Hypertensive disorders in pregnancy, particularly pre-eclampsia and eclampsia, account for significant proportion of perinatal morbidity and maternal mortality. In this context, this paper proposes an inference model that uses data mining (DM) techniques capable for operating in a data set to extract patterns and assist in knowledge discovery. Identifying hypertensive crises that complicate pregnancy, it can impact in a meaningful reduction the incidence of sequelae and death of pregnant women. Comparison between two Bayesian classifiers is performed in this work to better classify the hypertensive disorders severity. Results showed that Naïve Bayes classifier had an excellent performance, presenting better precision and F-measure, compared to the other experimented classifiers. Even finding a good performance to predict hypertensive disorders, other Bayesian methods need to be evaluated, as well as other DM techniques such as those based on artificial intelligence (AI) and tree-based methods.

Information and Communication Technologies (ICT) based applications for Ambient Assisted Living (AAL) help elderly or individual people living home alone. AAL system reliability is mostly based on the recent emerging class of network that is known as wireless mesh network (WiMesh). In WiMesh the information security is the most difficult problem to tackle because the medium is open to cyber-attacks. Moreover, when we talk about AAL where the complete personal information is digitized and stored, the need for implementation and maintenance of strict security measures is essential. In this article, we present a critical literature survey on communication security issues in e-health care environments. We highlight and explore the representative state of the art security prototypes for eHealthcare environments and also provide the details of their security characteristics. In addition, we discuss in detail the challenges and opportunities of these systems.

Cybersecurity in the health care domain is one of the most important and critical issues of this era. In fact, it was reported in 2014 that on the black market medical records are worth 10 times more than credit card details [1]. Datasets experience a particularly high risk when shifted to a different domain for the documentation of therapeutic or diagnostic procedures. U-Prove is a token based security concept whereby a user may disclose safely and securely a limited amount of information for authentication and verification purposes. In this paper, a U-Prove based security mechanism is proposed for mobile device authentication and authorization in the eHealthcare environment. The complete architecture of the proposed security mechanism and its detailed methodology with process flow is presented. In addition, a generic security analysis is performed to show the strength of the proposed security mechanism.

Hypertension is the main cause of maternal death. Preeclampsia can affect pregnant women before or during pregnancy. Identification of patients with higher risk for preeclampsia allows some precautions that are taken to prevent its severe disease and subsequent complications. In medicine, there are different situations that deal with a large range of information, which needs a thorough assessment to be able to help experts in the decision-making process. Smart decision support systems allow grouping all existing information and finding pertinent information from it. Bayesian networks offer models that allow the information capture and handle situations of uncertainty. This paper proposes the construction of a system to support intelligent decision applied to the diagnosis of preeclampsia using Bayesian networks to help experts in the pregnant's care. The processes of qualitative and quantitative modeling to the construction of a network are also presented. The main contribution of this work includes the presentation of a Bayesian network built to help decision makers in moments of uncertainty in care of pregnant women.

The future of ambient assisted living (AAL) especially eHealthcare almost depends on the smart objects that are part of the Internet of things (IoT). In our AAL scenario, these objects collect and transfer real-time information about the patients to the hospital server with the help of Wireless Mesh Network (WMN). Due to the multi-hop nature of mesh networks, it is possible for an adversary to reroute the network traffic via many denial of service (DoS) attacks, and hence affect the correct functionality of the mesh routing protocol. In this paper, based on a comparative study, we choose the most suitable secure mesh routing protocol for IoT-based AAL applications. Then, we analyze the resilience of this protocol against DoS attacks. Focusing on the hello flooding attack, the protocol is simulated and analyzed in terms of data packet delivery ratio, delay, and throughput. Simulation results show that the chosen protocol is totally resilient against DoS attack and can be one of the best candidates for secure routing in IoT-based AAL applications.

Testing and verification of healthcare information systems is a challenging and important issue since faults in these critical systems may lead to loss of lives, and in the best cases, loss of money and reputations. However, due to the complexity of these systems, and the increasing demand for new products and new technologies in this domain, there are several methods and technologies being used for testing these systems. In this paper, we review the state of the art on testing and verification of healthcare information systems, and then we identify several open issues and challenges in the area. We divide the exiting methods into three categories: simulation based methods, formal methods, and other techniques such as semi-formal methods. Then, we discuss challenging and open issues in the domain.

Cloud computing has appeared to be state of the art in the world of Information Technology especially in healthcare systems due to its innovative computing deployment model as a source of utility for users. However, many healthcare organizations are disinclined to adapt cloud computing due to security shortcoming associated with its infrastructure and many of them are still unaddressed. To provide a promising security to cloud computing infrastructure is a key issue, as users have a tendency to store their sensitive and classified medical data with cloud service providers which include the both trusted and un-trusted parties of cloud service providers. This paper addresses the distinguishing features of cloud such as, rapid elasticity, pooling of resources, on-demand services to users, multi-tenancy, third-party rule and its security requirements. Then, it presents the analysis of security concerns of cloud computing for healthcare systems in terms of availability, trust, confidentiality, compliance, integrity and audit. Furthermore, single-cloud is far more vulnerable to failure of service unavailability and malicious insiders and due to this reason it is less popular in healthcare, as medical healthcare systems are concerned about its security. From this notion of security concern an advanced model has emerged; “multi-cloud” also known to be “cloud-of-clouds”. This paper also provides an insight of security in single-cloud and multi-cloud and possible security recommendations for healthcare systems, as it has been observed that single-cloud has received more focus from researchers in terms of security vulnerabilities than a multi-cloud environment.

The wide dissemination of Internet around the world changed the way people communicate among them. The evolution of the information and communication technologies (ICT) or artificial intelligence (AI) allowed the creation of new types of services involving electronic devices with huge potential. The emergence of intelligent personal assistants (IPAs) is one of such potential example. Combining the IPA concept with the recent paradigm of Internet of Things (IoT), offers new possibilities and new services to end users, since they could learn more about other entities present in the surrounding environment. This paper proposes a novel mobile gateway solution for a ubiquitous mobile health scenario, in which information gathered from a body sensor network (BSN) is used by an IPA belonging to another person, typically mentioned to as caretaker. The mobile gateway receives real time information related to location, heart rate, and possible falls of the monitored person and acts as a communication channel between the BSN sensors and the IPA platform. Furthermore, the paper presents a performance evaluation study of the proposed mobile IoT-based gateway demonstrating and validating its feasibility.

Health telematics is a growing up issue that is becoming a major improvement on patient lives, especially in elderly, disabled, and chronically ill. In recent years, information and communication technologies improvements, along with mobile Internet, offering anywhere and anytime connectivity, play a key role on modern healthcare solutions. In this context, mobile health (m-Health) delivers healthcare services, overcoming geographical, temporal, and even organizational barriers. M-Health solutions address emerging problems on health services, including, the increasing number of chronic diseases related to lifestyle, high costs of existing national health services, the need to empower patients and families to self-care and handle their own healthcare, and the need to provide direct access to health services, regardless of time and place. Then, this paper presents a comprehensive review of the state of the art on m-Health services and applications. It surveys the most significant research work and presents a deep analysis of the top and novel m-Health services and applications proposed by industry. A discussion considering the European Union and United States approaches addressing the m-Health paradigm and directives already published is also considered. Open and challenging issues on emerging m-Health solutions are proposed for further works.

Currently, the eHealthcare information management is the most critical and hot research topic. Especially with the involvement of new and promising telecommunication technologies like Machine to Machine (M2M) Communication. In M2M communication the devices interact and exchange information with each other in an autonomous manner to accomplish the required tasks. Mostly machine communicate to another machine wirelessly. The wireless communication opens the medium for enormous vulnerabilities and make it very easy for hackers to access the confidential information and can perform malicious activities. In this paper, we propose a Machine to Machine (M2M) Low Delay and Secure (LDS) communication system for e-healthcare community based on random distributive key management scheme and modified Kerberos realm to ensure data security. The system is capable to perform the tasks in an autonomous and intelligent manner that minimizes the workload of medical staffs, and improves the quality of patient care as well as the system performance. We show how the different actors in the e-healthcare community can interact with each other in a secure manner. The system handles dynamic assignments of doctors to specific patients. The proposed architecture further provides security against false attack, false triggering and temper attack. Finally, the simulation type implementation is performed on Visual Basic .net 2013 that shows the feasibility of the proposed Low Delay and Secure (LDS) algorithm.

In this paper, we inspire from two analogies: the warfare kill zone and the airport check-in system, to design and deploy a new line in the defense-in-depth strategy, called the third line. This line is represented by a security framework, named the Intrusion Ambushing System and is designed to tackle the issue of SMS-based malware in the Android-based Smartphones. The framework exploits the security features offered by Android operating system to prevent the malicious SMS from going out of the phone and detect the corresponding SMS-based malware. We show that the proposed framework can ensure full security against SMS-based malware. In addition, an analytical study demonstrates that the framework offers optimal performance in terms of detection time and execution cost in comparison to intrusion detection systems based on static and dynamic analysis.

The field of wireless sensor network (WSN) is an important and challenging research area today. Advancements in sensor networks enable a wide range of environmental monitoring and object tracking applications. Secure routing in sensor networks is a difficult problem due to the resources limitations in WSN. Moreover, multihop routing in WSN is affected by new nodes constantly entering/leaving the system. Therefore, biologically inspired algorithms are reviewed and enhanced to tackle problems arise in WSN. Ant routing and human self security systems have shown an excellent performance for WSNs. Certain parameters like energy level, link quality, lose rate are considered while making decision. This decision will come up with the optimal route and also to take best action against the security attacks. In this paper, the design and initial work on BIOlogical-inspired self-organized Secure Autonomous Routing Protocol (BIOSARP) for WSNs is presented. The proposed bio-inspired algorithm will also meet the enhanced sensor network requirements, including energy consumption, success rate and time.